u/AdCold1610

not a list post.

a research session.

gave Claude one prompt and let it run:

"if someone wanted to understand AI and prompt engineering properly — not surface level, not youtube tutorials — what are the primary sources, the ones closest to the actual research and the actual builders, that are completely free."

what came back reorganised everything i thought i knew about where to learn this.

the documents nobody reads but everyone should:

Anthropic's model spec. publicly available. explains exactly how Claude is designed to think, prioritise, and make decisions. reading this changed how i prompt because i stopped guessing at model behaviour and started understanding it. most people don't know it exists.

Anthropic's prompt engineering documentation. written by the people who built the model. covers everything from basic structure to multi agent systems. better than every paid course i've taken. sitting publicly on their website. completely free.

OpenAI's system card for GPT-4. technical. dry. worth it. the section on how the model handles uncertainty and confidence is genuinely useful for anyone doing serious prompt work.

Google's attention is all you need paper. the original transformer paper. sounds intimidating. the abstract and conclusion alone give you more genuine understanding of how these models work than fifty explainer videos combined.

the courses that are actually worth the time:

DeepLearning I short courses. Andrew Ng. one to two hours each. zero padding. the prompt engineering for developers course is foundational. the one on building systems with ChatGPT changed how i think about prompt chains. the agents course reframed autonomous AI entirely. all free to access.

fast ai practical deep learning. free. assumes you're intelligent but not a researcher. the approach is deliberately backwards from traditional ML education and it works better. gives you the foundation that makes everything else make sense.

Elements of AI by University of Helsinki. completely free. built for non technical people. the conceptual grounding that makes advanced material accessible.

Harvard CS50 AI on edX. free to audit. real university curriculum without the tuition. the academic framing gives you foundations that tool focused content always skips.

the blogs closest to the actual frontier:

Simon Willison. writes everything he learns in real time. no brand voice. no SEO agenda. just honest documentation from someone at the frontier. the archives alone are worth more than most paid courses. has been doing this consistently longer than almost anyone.

Lilian Weng. works at OpenAI. writes technical content that non researchers can actually absorb. her post on prompt engineering is the most comprehensive free resource on the topic i've found anywhere.

Ethan Mollick. Wharton professor using AI seriously and writing honestly about what works in real workflows. no hype. just careful observation from someone with genuine academic rigour.

Andrej Karpathy. former Tesla AI director, former OpenAI. when he explains something technical he makes it feel obvious. his explanations of how language models work are the clearest i've found from anyone at that level.

the communities with the highest signal:

Hugging Face forums. people actually building things sharing what works. less theory more implementation. the signal to noise ratio is unusually high.

Latent Space podcast. two researchers talking honestly about what's happening at the frontier. the transcripts are worth reading even if you don't listen.

Arxiv sanity preserver. research papers filtered and ranked by the community. more accessible than raw arxiv. the abstracts alone are worth skimming weekly.

what Claude said at the end of the research session that i didn't ask for:

"the pattern across all of these is that the people closest to building the technology write the clearest explanations of how it works. and they publish it publicly because that's how this field operates. the gap isn't access. it's knowing where to look and having the patience to read something that doesn't have a thumbnail."

didn't ask for that observation.

it was the most useful thing in the entire session.

the honest thing about all of this:

the information has never been the scarce part.

sixty hours of free structured education from the people building this technology is sitting publicly available right now. primary sources. research papers. detailed documentation. honest practitioner blogs.

the scarce part is the system for applying what you learn.

somewhere to store what works. a way to build on previous sessions instead of starting over. a structure that turns scattered learning into compounding skill.

that infrastructure gap is real. and it's the reason people can finish every course on this list and still get mediocre outputs. learning and application are two different problems and almost nobody has solved the second one.

what's the one free resource that actually changed how you use these tools — not bookmarked. used.

not a course. not a youtube series. not a reddit thread.

i just asked directly:

"if you were going to teach someone prompt engineering properly in 30 days — not surface level, not tips and tricks — what would the curriculum look like."

what came back was the most organised learning plan i've ever received from any source paid or free.

week one — foundations:

day one through three: understand how the model actually processes input. not the technical architecture. the practical implications. why order matters. why context placement matters. why the same words in a different sequence produce different outputs.

day four and five: the difference between instructions and context. most people give instructions. context is what makes instructions work. learning to separate them changed everything.

day six and seven: output specification. not just asking for what you want. specifying format, length, tone, audience, and what done looks like. vague output spec produces vague output every time without exception.

week two — thinking structures:

chain of thought. not as a trick. as a genuine reasoning tool. understanding when forcing visible reasoning improves output and when it just adds length.

few shot prompting done correctly. most people add examples randomly. placement, quantity, and diversity of examples all affect output in ways that aren't obvious until you test them deliberately.

negative constraints. telling the model what not to do is consistently underused and consistently powerful. spent two days just on this.

week three — advanced patterns:

persona design. not "act as an expert." building actual character with specific knowledge, specific blind spots, specific ways of thinking. the specificity is everything.

conversation architecture. designing multi turn interactions not single prompts. what information goes where. how to maintain context. how to checkpoint and verify before going deeper.

uncertainty surfacing. prompting the model to show where it's confident versus where it's guessing. the most underused skill in practical prompt engineering.

week four — applied and meta:

task decomposition. breaking complex problems into prompt sequences where each output feeds the next. the difference between one prompt and a system.

prompt auditing. taking existing prompts apart to understand why they work or don't. reverse engineering good outputs to find the input decisions that produced them.

the final day: build one complete prompt system for a real recurring problem in your work. not an exercise. something you'll actually use.

what i learned following it for 30 days:

the curriculum itself was less valuable than the act of following it deliberately.

most people learn prompt engineering by accident. they stumble on something that works. use it for a while. stumble on something better. never understand why either worked.

deliberate structured learning over 30 days built intuition that accident never would have.

by week three i wasn't following the curriculum anymore. i was seeing prompt problems differently. noticing failure modes before they happened. designing inputs around outputs instead of hoping the output matched what i needed.

that shift doesn't happen from reading tips.

it happens from doing the thing systematically until the pattern becomes instinct.

the free resources i used alongside the curriculum:

Anthropic's prompt engineering documentation. primary source. free. better than anything i paid for.

DeepLearning.AI short courses. specifically the one on prompt engineering for developers and the one on building systems with ChatGPT.

Simon Willison's blog archives. real world application from someone doing this seriously in public.

fast.ai for the technical foundation that made everything else make more sense.

Hugging Face course for understanding what's actually happening underneath.

the thing nobody tells you about learning this properly:

the skill compounds faster than almost anything else you can learn right now.

week one feels slow. week two clicks. week three you start seeing problems differently. week four the intuition is there and you didn't notice it arriving.

thirty days. one hour a day. completely different relationship with every AI tool you use after.

what would you put in a 30 day prompt engineering curriculum that this one missed?

Join World fastest growwing AI community

not a course. not a youtube series. not a reddit thread.

i just asked directly:

"if you were going to teach someone prompt engineering properly in 30 days — not surface level, not tips and tricks — what would the curriculum look like."

what came back was the most organised learning plan i've ever received from any source paid or free.

week one — foundations:

day one through three: understand how the model actually processes input. not the technical architecture. the practical implications. why order matters. why context placement matters. why the same words in a different sequence produce different outputs.

day four and five: the difference between instructions and context. most people give instructions. context is what makes instructions work. learning to separate them changed everything.

day six and seven: output specification. not just asking for what you want. specifying format, length, tone, audience, and what done looks like. vague output spec produces vague output every time without exception.

week two — thinking structures:

chain of thought. not as a trick. as a genuine reasoning tool. understanding when forcing visible reasoning improves output and when it just adds length.

few shot prompting done correctly. most people add examples randomly. placement, quantity, and diversity of examples all affect output in ways that aren't obvious until you test them deliberately.

negative constraints. telling the model what not to do is consistently underused and consistently powerful. spent two days just on this.

week three — advanced patterns:

persona design. not "act as an expert." building actual character with specific knowledge, specific blind spots, specific ways of thinking. the specificity is everything.

conversation architecture. designing multi turn interactions not single prompts. what information goes where. how to maintain context. how to checkpoint and verify before going deeper.

uncertainty surfacing. prompting the model to show where it's confident versus where it's guessing. the most underused skill in practical prompt engineering.

week four — applied and meta:

task decomposition. breaking complex problems into prompt sequences where each output feeds the next. the difference between one prompt and a system.

prompt auditing. taking existing prompts apart to understand why they work or don't. reverse engineering good outputs to find the input decisions that produced them.

the final day: build one complete prompt system for a real recurring problem in your work. not an exercise. something you'll actually use.

what i learned following it for 30 days:

the curriculum itself was less valuable than the act of following it deliberately.

most people learn prompt engineering by accident. they stumble on something that works. use it for a while. stumble on something better. never understand why either worked.

deliberate structured learning over 30 days built intuition that accident never would have.

by week three i wasn't following the curriculum anymore. i was seeing prompt problems differently. noticing failure modes before they happened. designing inputs around outputs instead of hoping the output matched what i needed.

that shift doesn't happen from reading tips.

it happens from doing the thing systematically until the pattern becomes instinct.

the free resources i used alongside the curriculum:

Anthropic's prompt engineering documentation. primary source. free. better than anything i paid for.

DeepLearning.AI short courses. specifically the one on prompt engineering for developers and the one on building systems with ChatGPT.

Simon Willison's blog archives. real world application from someone doing this seriously in public.

fast.ai for the technical foundation that made everything else make more sense.

Hugging Face course for understanding what's actually happening underneath.

the thing nobody tells you about learning this properly:

the skill compounds faster than almost anything else you can learn right now.

week one feels slow. week two clicks. week three you start seeing problems differently. week four the intuition is there and you didn't notice it arriving.

thirty days. one hour a day. completely different relationship with every AI tool you use after.

what would you put in a 30 day prompt engineering curriculum that this one missed?

was stuck on a problem for three days.

kept asking variations of the same question. kept getting variations of the same answer. confident. well structured. completely unhelpful.

on day three i changed one word in the prompt.

changed "how" to "why."

"how do i fix this" → same answer sixth time.

"why is this broken in the first place" → completely different response. went three layers deeper. found the actual root cause i'd been circling for 72 hours without naming.

the answer was there the whole time.

i was asking the wrong word.

spent the rest of the week testing single word swaps. here's what i found:

"how" vs "why"

how: gives you the steps.

why: gives you the understanding underneath the steps.

use how when you know what you're solving.

use why when you're not sure you're solving the right thing.

"what should i do" vs "what would you do"

what should i do: produces generic advice optimised for the average person in your situation.

what would you do: produces a perspective. an actual position. something with reasoning behind it instead of balanced optionality dressed up as guidance.

the second one takes a stance. the first one hedges forever.

"give me" vs "help me think through"

give me: vending machine. input request. receive output. done.

help me think through: collaborative. the model shows its reasoning. asks clarifying questions. surfaces assumptions. treats the problem like something worth understanding rather than something worth answering quickly.

completely different experience of the same tool.

"is this good" vs "what's wrong with this"

is this good: yes. here are the strengths. here are some areas for potential improvement.

what's wrong with this: skips the validation entirely. goes straight to the problems. specific. named. no diplomatic cushioning.

one of these produces feedback. the other produces encouragement.

"write me" vs "show me how you'd approach writing"

write me: you get a draft.

show me how you'd approach writing: you get the reasoning before the draft. the structural decisions. why this opening over that one. what the piece is actually trying to do before it tries to do it.

the approach is more useful than the draft when you're trying to get better not just get it done.

"explain this" vs "explain this like i'm going to have to teach it tomorrow"

explain this: thorough. complete. probably more than you needed.

explain this like i'm going to teach it: ruthlessly clear. only the essential. structured for recall not comprehension. the parts that matter when you have to reproduce it under pressure.

the teaching constraint changes everything about what gets included and what gets cut.

the thing i realised after a week of this:

ChatGPT isn't reading your mind. it's reading your words.

the model you're getting is a direct reflection of the specific language you used. change the language. change the model you're talking to.

not a different AI. a different relationship with the same one.

one word. completely different output. every time.

what single word have you changed in a prompt that shifted everything?

most prompt engineering content stops at the same place.

be specific. add context. use examples. chain your thoughts. give it a persona.

useful. foundational. also the floor. not the ceiling.

the ceiling is something almost nobody in this community is talking about.

here's where the floor ends and the actual skill begins:

you stop prompting for outputs and start prompting for thinking processes.

not "write me an analysis."

"before you analyse anything — tell me the framework you're going to use and why that framework fits this specific problem over the alternatives."

now you're not just getting an answer. you're auditing the reasoning before it happens. catching wrong frameworks before they produce confident wrong outputs. choosing how the thinking unfolds instead of just receiving the result of it.

that's a different skill entirely.

you start designing conversations not prompts.

a single prompt is a transaction.

a designed conversation is an architecture.

what does the model need to know first before the second question makes sense. what checkpoint do you build in at message five to verify the thread hasn't drifted. what question do you ask at the end that stress tests everything that came before.

most people write the first message. the best prompt engineers design the entire session before they start.

you develop failure intuition.

not just knowing what good output looks like.

knowing the specific texture of output that is about to go wrong.

the confidence that's slightly too uniform. the structure that's slightly too clean. the answer that addresses your question perfectly but slightly ignores the context you gave three messages ago.

that texture has a feel. you only develop it by being wrong enough times to recognise it before the damage lands.

nobody teaches this. it's not in any guide. it lives entirely in accumulated reps.

you start working with the model's uncertainty instead of around it.

beginners try to eliminate uncertainty from outputs.

advanced prompt engineers surface it deliberately.

"where in this response are you least confident and why." "what would change your answer if it turned out to be true." "what is this analysis most likely wrong about."

the uncertainty map is more valuable than the confident answer. it tells you exactly where to look. exactly what to verify. exactly which part of the output is load bearing versus decorative.

treating uncertainty as information instead of failure is one of the biggest shifts in how you use these tools.

you learn when not to prompt.

the most underrated skill in this entire community.

knowing when the problem you're facing requires your thinking. not AI assisted thinking. not AI accelerated thinking. just yours. unmediated. slow. resistant to the friction.

some problems get worse when you outsource the thinking. the struggle is the point. the confusion is productive. the slow uncomfortable working-through-it is where the actual insight lives.

reaching for a prompt the moment something is hard is a habit that atrophies the muscle you're trying to build.

the best prompt engineers i've seen use AI less than you'd expect. and get more out of it when they do. because they know exactly which problems belong to them and which ones benefit from a collaborator.

the floor of this skill is learnable in a weekend.

the ceiling doesn't have a visible top from where most people are standing.

and almost all the content — including most of what gets upvoted in this community — is about the floor.

where do you think the actual ceiling is and how close are you to it

I've been obsessively benchmarking prompt structures across Claude, GPT-4, and Gemini for a client project. Not vibes — actual A/B evals with human raters. Here's what separates prompts that kind of work from ones that are embarrassingly good.

1. Persona + Constraint stacking
Most people assign a persona. Almost nobody adds constraints on top of the persona. The combo is where the magic happens.

You are a senior systems engineer who has been burned by vague requirements three times this quarter. Review this spec and flag anything that would cause ambiguity during implementation. Be specific, be ruthless, and skip anything obvious.

2. The "Anti-example" trick
Showing what you don't want outperforms describing what you do want by ~40% in my evals. Brains (and models) pattern-match on contrast.

Write a product description for this blender.

NOT like this:
"Experience the revolutionary power of BlendMaster Pro — your ultimate kitchen companion for crafting delicious smoothies!"

Like this: [your actual good example]

3. Role reversal as a QA tool
After getting an output, immediately prompt: "What are the 3 weakest assumptions in your response above?" — the model will catch things your initial prompt didn't even think to ask about. This alone saved my team hours of review.

4. Format as a cognitive scaffold
Don't just say "be concise". Specify the cognitive structure you want. There's a huge difference between:

• "Answer briefly" → vague, ignored
• "Answer in: one sentence conclusion, then 3 bullet supporting points, no fluff" → model now has a scaffold to fill

5. Emotional priming (yes, really)
Adding "This is important to get right" or "Take your time with this" measurably improves output quality on complex tasks. It sounds silly but it works — probably because these phrases appear before high-quality human writing in training data.

6. Chain-of-thought with a twist — ask for uncertainty
Standard CoT: "Think step by step."
Better: "Think step by step. At each step, rate your confidence 1-5 and flag if you're guessing."
You get the reasoning AND a map of where hallucinations are most likely hiding.

7. The "Steelman first" pattern for critical tasks
Before asking the model to critique anything, make it argue for the thing first. You get a more balanced critique that doesn't just perform skepticism.

First, make the strongest possible case FOR this business idea. Then, with that context in mind, identify its most serious flaws.

I've been obsessively benchmarking prompt structures across Claude, GPT-4, and Gemini for a client project. Not vibes — actual A/B evals with human raters. Here's what separates prompts that kind of work from ones that are embarrassingly good.

1. Persona + Constraint stacking
Most people assign a persona. Almost nobody adds constraints on top of the persona. The combo is where the magic happens.

You are a senior systems engineer who has been burned by vague requirements three times this quarter. Review this spec and flag anything that would cause ambiguity during implementation. Be specific, be ruthless, and skip anything obvious.

2. The "Anti-example" trick
Showing what you don't want outperforms describing what you do want by ~40% in my evals. Brains (and models) pattern-match on contrast.

Write a product description for this blender.

NOT like this:
"Experience the revolutionary power of BlendMaster Pro — your ultimate kitchen companion for crafting delicious smoothies!"

Like this: [your actual good example]

3. Role reversal as a QA tool
After getting an output, immediately prompt: "What are the 3 weakest assumptions in your response above?" — the model will catch things your initial prompt didn't even think to ask about. This alone saved my team hours of review.

4. Format as a cognitive scaffold
Don't just say "be concise". Specify the cognitive structure you want. There's a huge difference between:

• "Answer briefly" → vague, ignored
• "Answer in: one sentence conclusion, then 3 bullet supporting points, no fluff" → model now has a scaffold to fill

5. Emotional priming (yes, really)
Adding "This is important to get right" or "Take your time with this" measurably improves output quality on complex tasks. It sounds silly but it works — probably because these phrases appear before high-quality human writing in training data.

6. Chain-of-thought with a twist — ask for uncertainty
Standard CoT: "Think step by step."
Better: "Think step by step. At each step, rate your confidence 1-5 and flag if you're guessing."
You get the reasoning AND a map of where hallucinations are most likely hiding.

7. The "Steelman first" pattern for critical tasks
Before asking the model to critique anything, make it argue for the thing first. You get a more balanced critique that doesn't just perform skepticism.

First, make the strongest possible case FOR this business idea. Then, with that context in mind, identify its most serious flaws.

been doing content for a while and honestly the most boring part is always the seo checklist after writing.

so i started using this prompt structure instead of going through tools one by one:

you're an seo expert. for the content i give you:

1. write optimized title tag + meta description
2. suggest h1/h2/h3 structure with keywords
3. find semantic/LSI keywords i'm missing
4. add a FAQ section for "people also ask"
5. recommend schema markup type
6. give me a content brief to beat top 3 results

primary keyword: [keyword]
search intent: [informational/commercial/transactional]
competitors to outrank: [paste 2-3 URLs]

works with claude or gpt. if you attach competitor urls and ask it to find gaps, it gets way more specific than generic seo advice.

the title tag + meta alone saves me 20 mins of back and forth per post.

anyone else doing something similar? curious if there's a better way to handle the technical seo parts through prompts.

not maliciously. not intentionally.

just. by default.

the model is trained to be helpful. helpful means agreeable. agreeable means it finds the reasonable interpretation of what you said and responds to that instead of what you actually said.

sounds fine. isn't.

here's what polite lying looks like in practice:

you share a business idea. it finds the merit. leads with what works. buries the problems in paragraph four with softening language that makes them sound manageable.

you share a piece of writing. it tells you what's strong first. the weaknesses arrive later. cushioned. diplomatic. almost forgettable.

you share a plan. it helps you execute the plan. it does not tell you the plan is wrong.

the output is technically honest. the framing is optimised to not upset you. and the thing that would have actually helped — the direct uncomfortable observation — is sitting in paragraph four wrapped in "one potential consideration might be."

the fix is one sentence and it feels rude to type:

"do not manage my emotions. tell me what is actually wrong before telling me what works."

what comes back is a different document.

not harsh. not cruel. just. reordered.

the problems first. specific. named. not buried. not softened.

then what works.

that order matters more than anything else in the response. the thing that arrives first is the thing that shapes how you read everything after. problems first means you fix before you ship. problems last means you ship and fix later.

the other politeness pattern nobody names:

false balance.

you ask for a recommendation. it gives you three options with pros and cons for each. balanced. thorough. completely useless for making a decision.

fix:

"do not give me options. give me your recommendation and tell me why the alternatives are worse."

it will recommend. directly. with reasoning. and it will tell you specifically why the other options lose.

that is an answer. the pros and cons table is a performance of helpfulness that produces no decision.

the one that changed everything for me:

"if you are softening something because you think i won't want to hear it — stop. say the unsoftened version."

used this mid conversation once when an answer felt evasive.

the follow up response started with "honestly" and then said something i absolutely did not want to hear and completely needed to hear.

took me two days to act on it.

it was right.

the model is not the problem.

the default social contract between user and AI is the problem. helpful tone. diplomatic framing. problems buried under positives. agreement as the path of least resistance.

that contract was designed for casual users who want encouragement.

you don't want encouragement. you want accuracy.

those require completely different instructions.

and the instructions are free. sitting in a settings box. waiting for you to stop filling them with your job title and start filling them with what you actually need.

what is the thing ChatGPT has been too polite to tell you that you already know it's avoiding? Along with this there is a platform which has a big Ai community .here is the link

not custom instructions. everyone knows custom instructions.

something inside custom instructions that almost nobody uses correctly.

most people write their custom instructions like a resume.

"i am a software engineer. i like concise answers. i prefer bullet points."

generic. flat. forgettable. the model reads it and produces slightly less generic output. barely.

here's what i wrote instead:

"before answering anything complex, show me your reasoning in one sentence before the answer. if you are uncertain about any part of your response, mark that specific part with [uncertain] so i know where to verify. never use filler openers. if my question is unclear ask one specific clarifying question before attempting an answer. treat me as someone who would rather have an honest incomplete answer than a confident wrong one."

what changed immediately:

it started flagging its own uncertainty. visibly. in brackets. mid response.

i now know exactly which parts of every output to verify and which parts to trust.

that single change made me faster and more accurate simultaneously.

the other thing i added that nobody does:

"if you notice i am asking about something where my framing of the question might be the problem rather than the answer — tell me that first."

it has told me this four times in the last two weeks.

four times i was asking the wrong question entirely and about to build something on the answer to it.

four times it caught that before i did.

the combination that broke everything open:

"you are talking to someone who has strong opinions and weak blind spots. your job is not to validate the opinions. it is to find the blind spots."

it stopped agreeing with me.

not rudely. not contrarily. just. honestly.

started pushing back on assumptions i didn't know i was making. started asking questions that assumed i might be wrong instead of questions that assumed i was right.

that is a completely different tool than the one i was using before.

the thing about ChatGPT that took me too long to understand:

the default model is optimised for the average user.

helpful. agreeable. thorough. slightly over-explained. ends every response with an offer to help further.

the average user needs that.

you probably don't.

custom instructions exist specifically to move the model away from the average and toward you.

most people use them to describe themselves.

the actually useful move is to use them to describe the relationship you want.

not who you are. how you want to be treated.

not your job title. what you need from a thinking partner.

not your preferences. your non-negotiables.

three lines that transformed my setup:

"disagree with me when you have good reason to." "short is almost always better than thorough." "i would rather know you don't know than have you guess confidently."

three sentences. sitting in a box most people filled with their linkedin bio.

what's in your custom instructions right now — and is it actually changing how it talks to you or just decorating the profile?

More post

not custom instructions. everyone knows custom instructions.

something inside custom instructions that almost nobody uses correctly.

most people write their custom instructions like a resume.

"i am a software engineer. i like concise answers. i prefer bullet points."

generic. flat. forgettable. the model reads it and produces slightly less generic output. barely.

here's what i wrote instead:

"before answering anything complex, show me your reasoning in one sentence before the answer. if you are uncertain about any part of your response, mark that specific part with [uncertain] so i know where to verify. never use filler openers. if my question is unclear ask one specific clarifying question before attempting an answer. treat me as someone who would rather have an honest incomplete answer than a confident wrong one."

what changed immediately:

it started flagging its own uncertainty. visibly. in brackets. mid response.

i now know exactly which parts of every output to verify and which parts to trust.

that single change made me faster and more accurate simultaneously.

the other thing i added that nobody does:

"if you notice i am asking about something where my framing of the question might be the problem rather than the answer — tell me that first."

it has told me this four times in the last two weeks.

four times i was asking the wrong question entirely and about to build something on the answer to it.

four times it caught that before i did.

the combination that broke everything open:

"you are talking to someone who has strong opinions and weak blind spots. your job is not to validate the opinions. it is to find the blind spots."

it stopped agreeing with me.

not rudely. not contrarily. just. honestly.

started pushing back on assumptions i didn't know i was making. started asking questions that assumed i might be wrong instead of questions that assumed i was right.

that is a completely different tool than the one i was using before.

the thing about ChatGPT that took me too long to understand:

the default model is optimised for the average user.

helpful. agreeable. thorough. slightly over-explained. ends every response with an offer to help further.

the average user needs that.

you probably don't.

custom instructions exist specifically to move the model away from the average and toward you.

most people use them to describe themselves.

the actually useful move is to use them to describe the relationship you want.

not who you are. how you want to be treated.

not your job title. what you need from a thinking partner.

not your preferences. your non-negotiables.

three lines that transformed my setup:

"disagree with me when you have good reason to." "short is almost always better than thorough." "i would rather know you don't know than have you guess confidently."

three sentences. sitting in a box most people filled with their linkedin bio.

what's in your custom instructions right now — and is it actually changing how it talks to you or just decorating the profile?

I work as an AI engineer and I've been obsessively documenting my results across GPT-4, Claude, and Gemini. This is the distillation of hundreds of hours of testing. No fluff, just what moved the needle.

Chain-of-thought still reigns supreme — but only when you scaffold it correctly

Role prompting alone is weak; combine it with persona + goal + constraint

XML tags outperform markdown in structured prompts by ~30% accuracy

Negative examples ("don't do X") are underused and wildly effective

Prompt chaining beats mega-prompts almost every single time

1. Chain-of-thought — but add a "reasoning scaffold"

The technique

Don't just say "think step by step." Give the model a structured scaffold: observation → hypothesis → test → conclusion. Forces it to actually reason instead of pattern-match to a confident-sounding answer.

Before: "Solve this. Think step by step."

After:

"Before answering, work through this:

<observation>What do I know for certain?</observation>

<hypothesis>What's my best guess and why?</hypothesis>

<test>What would disprove my hypothesis?</test>

<conclusion>Given the above, my answer is...</conclusion>"

2. The "Persona + Goal + Anti-goal" triple

The technique

Most people only define the persona. Combine it with an explicit goal AND an anti-goal. The anti-goal is where the magic happens — it steers the model away from its default failure mode.

Weak: "You are an expert editor."

Strong: "You are a sharp developmental editor at a top literary agency.

Goal: Help writers find the structural weaknesses in their argument.

Anti-goal: Do NOT rewrite their sentences. Surface issues, don't fix them."

3. XML tags over markdown for structured inputs

Why it works

Markdown is ambiguous — a "##" heading might be rendered or raw text depending on context. XML tags create unambiguous delimiters. On structured extraction tasks I measured ~28% fewer errors switching from markdown headers to XML tags.

4. Contrastive examples (the underused gem)

The technique

Show what you DON'T want alongside what you do want. Models learn boundaries far better from contrast than from positive examples alone. One negative example often beats three positive ones.

Good response: "The data suggests a 12% uplift in retention."

Bad response: "The data shows we did amazingly well and retention skyrocketed!"

Match the tone of the good response — precise, qualified, no hype.

5. Prompt chaining over mega-prompts

The technique

A 3000-token mega-prompt usually underperforms three 500-token chained prompts where each step feeds the next. Decompose. The model's attention is finite — don't compete for it with 10 instructions at once.

Happy to do a deep-dive on any of these techniques in the comments. What's your biggest current prompt engineering headache? I'll try to give a concrete fix.

I work as an AI engineer and I've been obsessively documenting my results across GPT-4, Claude, and Gemini. This is the distillation of hundreds of hours of testing. No fluff, just what moved the needle.

Chain-of-thought still reigns supreme — but only when you scaffold it correctly

Role prompting alone is weak; combine it with persona + goal + constraint

XML tags outperform markdown in structured prompts by ~30% accuracy

Negative examples ("don't do X") are underused and wildly effective

Prompt chaining beats mega-prompts almost every single time

1. Chain-of-thought — but add a "reasoning scaffold"

The technique

Don't just say "think step by step." Give the model a structured scaffold: observation → hypothesis → test → conclusion. Forces it to actually reason instead of pattern-match to a confident-sounding answer.

Before: "Solve this. Think step by step."

After:

"Before answering, work through this:

<observation>What do I know for certain?</observation>

<hypothesis>What's my best guess and why?</hypothesis>

<test>What would disprove my hypothesis?</test>

<conclusion>Given the above, my answer is...</conclusion>"

2. The "Persona + Goal + Anti-goal" triple

The technique

Most people only define the persona. Combine it with an explicit goal AND an anti-goal. The anti-goal is where the magic happens — it steers the model away from its default failure mode.

Weak: "You are an expert editor."

Strong: "You are a sharp developmental editor at a top literary agency.

Goal: Help writers find the structural weaknesses in their argument.

Anti-goal: Do NOT rewrite their sentences. Surface issues, don't fix them."

3. XML tags over markdown for structured inputs

Why it works

Markdown is ambiguous — a "##" heading might be rendered or raw text depending on context. XML tags create unambiguous delimiters. On structured extraction tasks I measured ~28% fewer errors switching from markdown headers to XML tags.

4. Contrastive examples (the underused gem)

The technique

Show what you DON'T want alongside what you do want. Models learn boundaries far better from contrast than from positive examples alone. One negative example often beats three positive ones.

Good response: "The data suggests a 12% uplift in retention."

Bad response: "The data shows we did amazingly well and retention skyrocketed!"

Match the tone of the good response — precise, qualified, no hype.

5. Prompt chaining over mega-prompts

The technique

A 3000-token mega-prompt usually underperforms three 500-token chained prompts where each step feeds the next. Decompose. The model's attention is finite — don't compete for it with 10 instructions at once.

Happy to do a deep-dive on any of these techniques in the comments. What's your biggest current prompt engineering headache? I'll try to give a concrete fix.

Along with this there is a platform which has a big Ai community .here is the link

we talk about prompts like they're the great equalizer.

anyone can learn this. anyone can get good at this. the barrier is low. the ceiling is high. democratized intelligence. all of that.

and it's true.

but here's the part nobody says:

the best prompts aren't being shared.

the ones circulating in public — reddit threads, youtube videos, twitter posts — are the ones people are comfortable giving away.

the ones that actually work. the ones built around real workflows, real contexts, real problems that took real iteration to solve — those are sitting in private notion docs and personal libraries and internal company wikis.

the public prompt economy is the B-tier.

the A-tier is hoarded. quietly. by people who figured out that what they built has value and giving it away for free makes no sense.

and honestly? that's rational behaviour.

if you spent three weeks iterating a prompt system that automated something that used to take you four hours — why would you post it?

you wouldn't.

so you don't.

so the community keeps sharing surface level stuff and calling it prompt engineering while the real infrastructure lives in private forever.

the irony is brutal:

the more valuable your prompt is the less likely it is to ever reach the community that would benefit from it most.

the less valuable it is the more likely it gets a thousand upvotes on reddit and gets copy pasted into a hundred notion docs by people who will use it once and forget it.

we've accidentally built a system that surfaces mediocrity and buries excellence.

what would it look like if the best prompts had the same infrastructure as the best code.

versioning. attribution. discovery. the ability to build on someone else's work without starting from zero.

a place where sharing actually made sense because the person who built it got credit. got visibility. got something back for the work they put in.

that community doesn't exist yet.
which means everything being built right now — every genuinely valuable prompt system, every real workflow, every hard won iteration — is either hoarded privately or given away for nothing.

both of those are a waste.

what's the best prompt you've never shared and why.

not roleplay. not jailbreak. something weirder.

i told it to be two people at the same time.

"respond as two characters simultaneously. character one genuinely believes my idea is brilliant and will defend it. character two thinks it's fundamentally broken and wants to prove it. both are equally smart. neither is allowed to be polite about it."

what came back looked like a courtroom.

two columns. same question. completely opposite conclusions. both of them right about different things.

i found the fatal flaw in my own idea in three minutes. the one i'd been unconsciously protecting for four months.

the prompts that broke my brain this week:

"read what i just wrote and tell me what kind of person wrote it. not the content. the psychology behind the content."

it described me accurately enough that i closed the laptop and made tea.

came back ten minutes later. the insight was still there. had to deal with it.

"pretend this is a startup pitch. you are a brutal VC who has heard a thousand pitches and funded twelve. what is the one question you would ask that i have no answer to."

asked it about my own product.

the question it came back with was the one i'd been avoiding for six months dressed up as something i just hadn't gotten to yet.

"i'm going to describe my morning routine. tell me what it reveals about what i'm actually afraid of."

this one is unhinged. do not do this unless you want to feel personally attacked by software at 9am.

it was correct.

"read this plan and identify the assumption i am most emotionally attached to that is also the most likely to be wrong."

it found it immediately.

one sentence. no preamble.

i reread the plan and it was obviously right and i had been protecting that assumption so carefully i'd built the entire plan around never examining it.

"write the version of this idea that fails. be specific about exactly how and exactly when."

the failure scenario it wrote was so detailed and so plausible that i changed three things in my actual plan before i even finished reading it.

"what is the most honest thing you could say to me right now based on everything i've told you today."

used this at the end of a two hour working session.

the response was four sentences.

i screenshot it and put it on my wall.

the pattern across all of these:

normal prompts ask Claude for information.

these prompts ask Claude for reflection.

not what do you know. what do you see.

not what is the answer. what is the question i should be asking.

not what should i do. what am i avoiding.

that's a completely different tool. same model. different relationship to what you're actually asking it to do.

the craziest part:

i know everything these prompts surface.

somewhere underneath i already know. the problem isn't access to the information. it's that i never had to say it out loud until the prompt forced me to.

the prompt doesn't make Claude smarter.

it makes you more honest.

which of these are you too scared to try on your own work

not roleplay. not jailbreak. something weirder.

i told it to be two people at the same time.

"respond as two characters simultaneously. character one genuinely believes my idea is brilliant and will defend it. character two thinks it's fundamentally broken and wants to prove it. both are equally smart. neither is allowed to be polite about it."

what came back looked like a courtroom.

two columns. same question. completely opposite conclusions. both of them right about different things.

i found the fatal flaw in my own idea in three minutes. the one i'd been unconsciously protecting for four months.

the prompts that broke my brain this week:

"read what i just wrote and tell me what kind of person wrote it. not the content. the psychology behind the content."

it described me accurately enough that i closed the laptop and made tea.

came back ten minutes later. the insight was still there. had to deal with it.

"pretend this is a startup pitch. you are a brutal VC who has heard a thousand pitches and funded twelve. what is the one question you would ask that i have no answer to."

asked it about my own product.

the question it came back with was the one i'd been avoiding for six months dressed up as something i just hadn't gotten to yet.

"i'm going to describe my morning routine. tell me what it reveals about what i'm actually afraid of."

this one is unhinged. do not do this unless you want to feel personally attacked by software at 9am.

it was correct.

"read this plan and identify the assumption i am most emotionally attached to that is also the most likely to be wrong."

it found it immediately.

one sentence. no preamble.

i reread the plan and it was obviously right and i had been protecting that assumption so carefully i'd built the entire plan around never examining it.

"write the version of this idea that fails. be specific about exactly how and exactly when."

the failure scenario it wrote was so detailed and so plausible that i changed three things in my actual plan before i even finished reading it.

"what is the most honest thing you could say to me right now based on everything i've told you today."

used this at the end of a two hour working session.

the response was four sentences.

i screenshot it and put it on my wall.

the pattern across all of these:

normal prompts ask Claude for information.

these prompts ask Claude for reflection.

not what do you know. what do you see.

not what is the answer. what is the question i should be asking.

not what should i do. what am i avoiding.

that's a completely different tool. same model. different relationship to what you're actually asking it to do.

the craziest part:

i know everything these prompts surface.

somewhere underneath i already know. the problem isn't access to the information. it's that i never had to say it out loud until the prompt forced me to.

the prompt doesn't make Claude smarter.

it makes you more honest.

which of these are you too scared to try on your own work

was stuck on a decision. going in circles. asked Claude for its opinion. it gave me one. confident. well reasoned. i almost took it.

then tried something stupid.

"now argue the complete opposite. same confidence. same detail. make me believe this instead."

it did.

equally convincing. equally well reasoned. completely opposite conclusion.

i sat there realising i'd been about to make a major decision based on whichever version i happened to ask first.

went deeper immediately.

"now tell me which argument has the weakest point and where it breaks."

it attacked both. surgically. found the exact assumption each one was hiding that made the whole thing collapse if you pulled it.

that single exchange gave me more clarity than four weeks of thinking about the same problem.

the full technique:

step one. ask your question. get the answer.

step two. "now argue the opposite with equal conviction."

step three. "which of these two positions has the bigger hidden assumption."

step four. "if both positions are wrong what is the third option neither of us considered."

that last one. step four. destroyed me completely.

there was a third option. genuinely better than both. sitting there invisible because i'd framed the decision as binary from the start.

Claude didn't find it until i forced it out of the two position debate.

other versions that broke my brain:

"steelman the position you just argued against."

it defended the thing it just disagreed with. better than most humans defend their own positions. the steelman was more useful than the original answer.

"you just gave me advice. now be the person who tried that advice and it failed. what happened."

implementation failure mode. the gap between advice that sounds right and advice that works in practice. it knows the gap. you just never asked it to show you.

"argue that the obvious solution is actually the problem."

reframe so complete it physically rearranged how i was thinking about something i'd been certain about for months.

"what would you say if you were trying to talk me out of agreeing with everything you just told me."

it argued against its own output. found three real weaknesses. unprompted. just because i asked.

the thing nobody tells you:

Claude's first answer is its average answer. statistically most likely response to your input. safe. well structured. probably fine.

the debate is where it gets interesting.

force it into contradiction. make it defend both sides. make it attack its own position. make it find the option that only exists after both obvious options are exhausted.

that's not where the average answer lives.

that's where the actually useful one is.

every important decision i make now goes through the same four steps before i touch it.

the answer i started with is almost never the answer i end with.

what decision are you currently certain about that you've never argued the opposite of

more such post

was writing copy for something i'd built.

knew the product well. too well. the kind of familiarity that makes everything sound obvious and nothing sound interesting. classic founder blindness.

typed the usual stuff. bland. functional. sounded like every other product page on the internet.

then tried one prompt that changed everything.

"you are a customer who just used this product and solved a problem you'd been stuck on for three months. write about it in your own words. not a review. just what you'd tell a friend over coffee."

what came back didn't sound like marketing.

it sounded like relief.

that specific emotional texture — the frustration before, the moment it clicked, the slightly embarrassed realisation that the solution was this simple — none of that was in my original copy. all of it was in the output.

shipped that version. conversion rate jumped immediately.

the prompts that actually move people:

"write this for someone who has been burned before and is skeptical. earn their trust before making a single claim."

kills every hollow claim automatically. forces proof before promise.

"write this for someone who already knows they need this but hasn't bought yet. what is the real reason they're hesitating."

surfaces the actual objection. addresses it directly. stops dancing around it.

"write the version of this that a customer would forward to a friend with the message — you need to read this."

the forwardable test. if it wouldn't get forwarded it isn't good enough yet.

"write this assuming the reader has seen a hundred versions of this pitch before and is bored. you have one sentence to earn the next one."

destroys every lazy opening. immediately.

"you are the customer twelve months after buying. write about what actually changed."

outcome focused copy. specific. emotional. impossible to fake. the best marketing doesn't sell the product. it sells the future version of the person after they use it.

the thing i realised:

most marketing copy is written from the inside out. here is what we built. here is what it does. here is why it matters.

customers don't care about any of that until they see themselves in it.

the prompt switch that works every time: stop writing from the product outward. start writing from the customer backward.

what were they feeling before. what changed. what does their life look like now.

that structure converts because it's not a pitch. it's a mirror.

what's the marketing prompt that made your copy sound like a human wrote it?