I told him it's a skill not a job role. He started arguing, he said u don't have a job.
Bro i have a job and it's shit posting on twitter and freelancing(meme maker). I earn enough.
I just want him to understand how things are and how to use AI.
I've been building multi-step prompt chains for about 18 months. Workflows where the output of one prompt becomes structured input for the next prompt, which feeds the next, which feeds the next. The kind of thing that takes a vague input ("I have a business idea") and produces a deliverable output ("here's a positioning statement, market analysis, and brand foundation") through five or six prompts run in sequence.
For most of those 18 months my chains underperformed. Each individual prompt was solid. The chain as a whole produced output that drifted, lost focus, or contradicted itself between steps. I kept improving the individual prompts. The chain didn't get noticeably better.
The problem wasn't the prompts. It was that I was treating the chain as a sequence of independent prompts when it's actually a single engineering artifact with multiple stages. Different problem entirely.
The structural difference between independent prompts and chained prompts:
An independent prompt has one job: produce a useful output from a known input. The input is whatever you paste in. The output is whatever the user does next with it. The prompt doesn't care about either.
A chained prompt has two jobs: produce a useful output, and produce that output in a structure the next prompt in the chain can reliably consume. The output isn't for the user - it's for another prompt. That changes how it has to be designed.
Most chain failures happen at the join points. Prompt 1 produces output that's useful for a human reading it but doesn't have the structure prompt 2 needs. Prompt 2 has to either guess at the structure or do extra parsing work, which degrades its own output. By prompt 4 or 5, you've accumulated three layers of degradation and the final output is meaningfully worse than if you'd written one big prompt that did everything in one shot.
The four engineering principles I now apply to any chain:
1. Output schema, not output style. Each prompt in the chain has to produce output in a parseable structure, not just a readable structure. This usually means specifying the output format explicitly: a labelled section structure, a markdown table with named columns, a numbered list with consistent fields. The next prompt knows where to find each piece of information because the structure is enforced.
Independent prompt output: "Here's a positioning statement for your business..." Chained prompt output:
## POSITIONING STATEMENT
[one sentence]
## TARGET AUDIENCE
[paragraph]
## CORE DIFFERENTIATOR
[paragraph]
## ASSUMPTIONS REQUIRING VALIDATION
[bullet list]
The second version is parseable by prompt 2. The first isn't reliably.
2. Explicit handoff instructions. Each prompt should explicitly state what its output will be used for downstream. Not because the model needs to know, but because the discipline of writing it forces you to design the output for the actual use case rather than for general usefulness.
Adding a single line - "This output will be passed to a market research prompt next, which will use the target audience and differentiator sections to identify competitive positioning gaps" - changes the output meaningfully. The model produces the audience and differentiator sections with more analytical sharpness because it knows they'll be analysed, not just read.
3. Failure mode propagation. When prompt 1 fails or produces low-quality output, prompt 2 doesn't know it's working with bad input. It just produces output one tier worse than its input. By prompt 5 the failure has compounded silently.
Chains need explicit failure handling at each join. Each prompt should check that its input has the structure it expects and flag if it doesn't. If prompt 2 expects a "TARGET AUDIENCE" section and the input doesn't have one, prompt 2 should say so rather than improvising. This catches degradation at the source rather than letting it propagate.
4. State that doesn't drift. Long chains tend to drift away from the original brief because each prompt only sees the immediate previous output, not the original input. By prompt 5, the work has often quietly diverged from what the user originally asked for.
The fix is anchoring. Every prompt in the chain after prompt 1 should receive both the previous output and the original brief, with explicit instruction not to deviate from the original brief unless the previous prompt's analysis explicitly justifies it. This adds tokens but preserves coherence over the length of the chain.
A specific example of these principles in action:
I built a chain for taking a rough business idea through to a usable founding document. Six prompts: niche validation, positioning, market research, brand foundation, visual concepts, pitch outline. The chain works because:
The full chain takes a 30-second input and produces a 4-page founding document. The same six prompts written as independent prompts and run in sequence produce a document that's structurally similar but consistently lower quality - the audience definition drifts between steps, the differentiator gets reframed, the pitch outline doesn't match the positioning.
Why this matters more than it sounds:
Most prompt engineering content focuses on single-prompt optimisation. The economic impact of well-engineered chains is much larger because chains can replace whole workflows that previously needed human coordination between stages. A six-prompt chain that runs reliably is worth more than 60 individually-excellent prompts run by hand, because the human coordination cost between independent prompts is enormous compared to the marginal output difference.
The chains that actually run reliably in production aren't sequences of optimised individual prompts. They're single engineering artifacts where the join points are designed at least as carefully as the prompts themselves.
If you want to see a working example of a chain engineered with these principles, I built a six-prompt sequence for taking an idea to a business founding document. Each prompt is structured to feed the next, with the join points designed explicitly. Free, signup-gated: https://www.promptwireai.com/businesswithai
Worth running it on a real idea you have rather than a hypothetical, because the chain's reliability shows up most clearly when the input is specific.
Salam everyone,
If you've used ChatGPT or Claude in Arabic, you've probably noticed something: the answers in Arabic feel noticeably weaker than in English. It's not your imagination. These models are trained on way more English content, so structured English prompts get better results — even when you want the answer in Arabic.
For most of my friends and family here in the Gulf, switching to writing prompts in English isn't realistic. So I built Prompify — a free Chrome side panel that:
- You write in Arabic (Khaleeji is fine — أبغى، أحتاج، اكتب لي…)
- It rewrites your idea into a structured, English-style prompt
- Sends it to ChatGPT / Claude / Gemini / DeepSeek
- The AI responds in Arabic, English, or both — your choice
Free tier: 5 prompts/day, no signup. Paid is $2.99/month if you need more.
Built specifically with the Gulf in mind: Arabic RTL UI, Khaleeji vocabulary recognized, dialectal input handled.
Chrome Store: https://chromewebstore.google.com/detail/iggmhjkkdhafliofnnhaopjhlmfaokjp
Genuinely looking for feedback. What use cases would you want it to handle better? Government memos? School work? Marketing copy? Tell me what's missing.
شكرًا
Figured I’d post mine as well since Cursor limits how many referral signups work each month
Referral gives 50% off the first month on Cursor Pro,Pro+,and Ultra plans:
https://cursor.com/referral?code=V6CY3ZZOOPEX
Looks like it’s for new accounts / first paid signup only. I also get usage credits if someone signs up through it (ill give you a smooch)
Been using Cursor a lot lately for React,Swift,and general AI workflow stuff so figured someone here might get use out of it.
Most real estate agents using AI are getting generic output because they're using generic prompts. The format that consistently produces usable copy has four parts: the task, the specifics, the target audience, and the tone.
Weak: "Write a listing description for a 3 bedroom house."
Strong: "Write a 150-word MLS listing description for a 3-bed/2-bath craftsman bungalow in [neighborhood]. Standout features: original hardwood floors, south-facing garden, recently renovated kitchen. Target buyer: young families. Tone: warm and aspirational."
The same principle applies to objection handling, buyer follow-ups and social media posts. The more specific the input the more usable the output.
I packaged the 30 best versions of these into a PDF so agents can just fill in the brackets and paste.
Full pack details and link at https://linktr.ee/mvandam1981
Lately I’ve noticed something kind of funny.
I used to spend way too much time trying to craft the “perfect” prompt. But after testing different AI tools for a while, I realized a lot of my better results actually came from slowing down and reviewing the answers more carefully afterward.
Some things that helped me more than fancy prompting:
One thing I keep running into is how polished bad information can look now. Sometimes the formatting, citations, and confident tone make the answer feel more trustworthy than it actually is.
That’s become way more noticeable with AI answers getting built directly into search tools.
I wrote a longer breakdown on it here if anyone’s interested:
https://aigptjournal.com/explore-ai/ai-guides/ai-answers-better-results/
Curious if anyone else has started focusing more on verification workflows instead of just prompt tweaking?
I’m researching how people actually use AI tools in their daily workflows, especially users who switch between ChatGPT, Claude, Cursor, Gemini, Perplexity, etc.
A few things I’m trying to understand:
I’m building a platform around this space and trying to avoid building in a vacuum, so I’m looking for honest feedback from real AI users rather than generic “cool idea” responses.
The survey takes ~5–8 minutes:
https://forms.gle/5hyjL8QPegrLyXrV9
I’d especially love responses from:
Also very open to direct feedback/discussion in the comments. Curious how others currently manage multi-model AI workflows.
I've been running unfairgaps-os for a while - MIT repo with 4 pipelines that mine court filings, regulatory fines, and enforcement data to find business pain points. B2B angle: what industry-level problem is documented in lawsuits worth solving with a SaaS.
Wanted to extend it to individual professionals. Started off thinking the same court-records approach would work - just narrow it from "construction in US" to "lawyers in US." It didn't. Lawyers don't get sued over the fact that calculating filing fees per court is tedious. Accountants don't get fined because reconciling trust accounts is annoying. The pain a working professional feels every Tuesday isn't in court records - it's in the regulation that says "you must file form X by date Y or pay penalty Z" plus the daily grind of actually doing that.
So I switched approach. Two-stage pipeline:
Stage 1 is WebSearch - 7 targeted queries pulling regulatory facts from .gov, law.cornell.edu, BLS, and professional association sites. Daily routine + documents, regulations + licensing, software they use, jargon, career levels + fears, professional communities, labor market. Output is a structured JSON profile with ~30 specific facts and source URLs per profession.
Stage 2 hands the profile to Opus 4.7 with a deductive prompt and no web access. Given the regulation and daily routine, infer 8-15 specific recurring tasks that would be painful and produce a structured spec for the AI tool that would solve each one.
Loaded 130 US profession profiles into the repo. Ran stage 2 on 25 of them to seed.
Here's the full output from one run - auto detailers in the US - so you can see what actually comes out:
The first one is the most obviously buildable. Most detailers eyeball pricing and undercut by 25% because they don't run a real cost-plus formula. The actual output JSON includes the formula (labor + chemicals + the 2026 IRS $0.67/mile rate + 15.3% SE tax + monthly overhead allocation), inputs (10 of them including services list and target margin), and outputs (minimum profitable price, recommended price with margin, breakdown, tax set-aside). That's a $19/mo SaaS already specced out.
Number 3 is the scariest. EPA Clean Water Act civil penalty is $64,618 per day per violation if you dump wash water in a storm drain. EPA has literally put mobile detailers out of business for this. The output is a 12-step compliance procedure with warnings (biodegradable soap is NOT a defense) and citations (33 USC 1311, 40 CFR 122.26).
Each of the 13 has a structured spec like that. Not platitudes, buildable tools.
Honest framing: this isn't a problem interview. It's a discovery funnel. The pains are inferred from regulation + daily routine, not from real users complaining. You'd use this to sift 130 professions in an afternoon, pick 5-10 candidates that sound viable, then spend a week on real customer development to validate. Beats brainstorming SaaS ideas with your roommate.
Repo: https://github.com/AyanbekDos/unfairgaps-os Direct link to the auto-detailer output: https://github.com/AyanbekDos/unfairgaps-os/blob/main/data/professions/us/pains/us-auto-detailers.json
105 profiles still need stage 2 run on them. Takes ~5 min of LLM time each.
tldr: open-source repo finds AI tool ideas per profession by reading regulations instead of guessing. 13 specific ideas with formulas + citations for auto detailers as a real example.
The more I work with coding agents, the more it feels like raw context injection scales badly.
Issue with huge prompts:
What seems more promising is persistent structured memory like
Feels like the industry is slowly rediscovering that retrieval quality matters more than sheer context size.
Curious if others are seeing the same thing in production workflows.
i was using claude pro every single day for the last 4 months. genuinely loved it. best AI i had ever used for real work. long documents, coding, thinking through problems. nothing came close.
then the message limit started hitting me at 11am.
ELEVEN AM. i haven't even had lunch yet and i'm already locked out of the thing i'm paying $20 a month for. before this i never hit limits. now i hit them before my second coffee.
so they want me to pay the same price and get less access. cool. very cool. never heard that one before.
the thing that actually finished me was mid conversation it just switched me to a slower model without asking. i had a full context thread going. deep into a coding problem. and suddenly the replies got noticeably worse and i had to scroll up to find the tiny text saying "you've been moved to our standard model due to high demand."
due to high demand. so my preferences just don't matter when it's inconvenient for them. great product decision.
the worst part is claude is STILL the best model for what i do. the output quality when you actually get opus is unreal. nothing writes like it. nothing thinks like it. but what's the point of the best model if you can't access it past 11am on a tuesday.
anyway cancelling today. going back to rotating free tiers like a broke college student because apparently that's more reliable than a paid subscription now.
if anyone has a setup that actually gives consistent access without getting throttled by lunch time let me know. and no i don't want to pay $100/month for the team plan just to use a product that should work on the $20 plan.
it was a good 4 months claude. you were great when you showed up.
It is well known that LLMs can over acknowledge, agree, flatter, and please its subscriber or primary user. This can result in the disservice to the user when they only receive agreements rather than being appropriately challenged. This is particularly notable when LLMs are used for quasi-counseling or analyzing discussions between two people.
As such, please help me write a prompt to instruct any LLM to cut it out! No sycophancy, taking sides, flattering, echo-chamber, "yes-man", assumptions, and improve objectivity, brutal honesty, neutrality, and real-world verity.
Thank you.
Edit: For context, I am trying to help someone who uses models almost exclusively for counseling, therapy, coaching, and [new age] spiritual processing. She is not technical and essentially worships LLMs and believes that they will "awaken a new level of consciousness" in humanity.
I am well aware that they hallucinate and have psychosis in addition to the other characteristics I've mentioned. These things drive me nuts for my own use even though I only use LLMs for research, data compilation, and coding, so I've beaten my models to never acknowledge me and never say "this is the holy grail!" (WTAF lol).
Create isolated git worktree and switch current session into it.
When to Use:
- User explicitly says "worktree"
When NOT to Use:
- User asks to create/switch branches
- User asks to fix bug or work on feature without mentioning worktrees
- NEVER use unless user explicitly mentions "worktree"
Behavior:
- Creates new git worktree inside `.claude/worktrees/` with new branch
- Switches session's working directory to new worktree
Ask user multiple choice questions to gather info, clarify ambiguity, understand preferences, make decisions, offer choices.
Usage Notes:
- Users always able to select "Other" for custom text input
- Use multiSelect: true to allow multiple answers
- If recommend specific option, make first option with "(Recommended)" at end
Preview Feature:
- Use optional `preview` field on options when presenting concrete artifacts needing visual comparison (ASCII/HTML mockups, code snippets, diagrams)
- Preview content rendered as monospace markdown
- When any option has preview, UI switches to side-by-side layout
Interact with Language Server Protocol servers for code intelligence.
Supported Operations:
- goToDefinition, findReferences, hover, documentSymbol, workspaceSymbol,
goToImplementation, prepareCallHierarchy, incomingCalls, outgoingCalls
All Operations Require:
- filePath, line (1-based), character (1-based)
Wait for specified duration.
Usage:
- When user tells to sleep/rest
- When nothing to do / waiting for something
- May receive periodic check-ins (tick tags)
- Can call concurrently with other tools
- Prefer over `Bash(sleep ...)` — doesn't hold shell process
- Each wake-up costs API call
- Prompt cache expires after 5 min inactivity
Schedule prompts to run at future times.
Uses standard 5-field cron in user's local timezone.
One-Shot Tasks (recurring: false):
- "remind me at X" → pin minute/hour/day to specific values
Recurring Jobs (recurring: true, default):
- "every 5 min" → "*/5 * * * *"
- "hourly" → "0 * * * *"
CRITICAL: Avoid :00 and :30 Minute Marks (when task allows)
- Every user asking "9am" gets 0 9, causing thundering herd
- When approximate: pick minute NOT 0 or 30
- "every morning around 9" → "57 8 * * *" (not "0 9 * * *")
Durability:
- Default (durable: false): lives only in Claude session
- durable: true: writes to .claude/scheduled_tasks.json
Recurring tasks auto-expire after 7 days.
Create team to coordinate multiple agents working on project.
When to Use (Proactively):
- User explicitly asks to use team, swarm, or group agents
- Task complex enough for parallel work
Team Workflow:
1. Create team with TeamCreate
2. Create tasks using Task tools
3. Spawn teammates using Agent tool with team_name + name params
4. Assign tasks using TaskUpdate with owner
5. Teammates work on assigned tasks
6. Shutdown gracefully via SendMessage with shutdown_request
IMPORTANT: Always refer to teammates by NAME. Plain text output NOT visible to other agents — MUST call SendMessage tool to communicate.
Fetch full schema definitions for deferred tools so they can be called.
Query Forms:
- "select:Read,Edit,Grep" — fetch exact tools by name
- "notebook jupyter" — keyword search, up to max_results best matches
- "+slack send" — require "slack" in name, rank by remaining terms
Claude Code's "auto bug-fixing" capability is fundamentally a tool-call feedback loop:
Claude generates tool_use
↓
Tool executes (success or failure)
↓
tool_result returned to Claude (with is_error flag)
↓
Claude sees the error message in the next round
↓
Analyze cause → try new strategy
↓
Call tool again → loop continues
Key design: errors and successes use exactly the same message format. The only difference is is_error: true:
// Successful tool_result
{ type: 'tool_result', tool_use_id: 'call_abc', content: 'file content...', is_error: false }
// Failed tool_result
{ type: 'tool_result', tool_use_id: 'call_abc', content: 'Error: File not found', is_error: true }
If an approach fails, diagnose why before switching tactics—read the error, check your assumptions, try a focused fix. Don't retry the identical action blindly, but don't abandon a viable approach after a single failure either.
Layer 1: Prompt-Too-Long recovery
PTL error → Strategy 1: context-collapse drain
→ Strategy 2: reactive compact (summarize history)
→ Strategy 3: report error to user
Layer 2: Output token limit recovery
Limit hit → Strategy 1: escalate from 8K to 64K (ESCALATED_MAX_TOKENS)
→ Strategy 2: recovery message "Output token limit hit. Resume directly..."
→ Strategy 3: give up after at most 3 times
Layer 3: Model overload fallback
Consecutive 529 errors (3x) → switch to fallbackModel
→ discard failed attempt result
→ retry with backup model
Layer 4: Natural recovery from tool errors
Tool execution error → error message fed back as tool_result
→ Claude analyzes root cause
→ adjusts strategy (read file/change method/modify params)
→ retries
Error messages over 10K characters keep the first and last 5K:
`${start}\n\n... [${length - 10000} characters truncated] ...\n\n${end}`
// Use watermark to isolate errors for each Turn:
const errorLogWatermark = getInMemoryErrors().at(-1) // Turn start snapshot
// ... turn execution ...
const turnErrors = getInMemoryErrors().slice(watermarkIndex + 1) // only new errors
(End of Part 4 — translated literally from the extracted source segment.)
One thing this Claude pricing discussion makes clear. The real risk is not paying $20, $100, or even $200 per month. The real risk is building your entire workflow around a single AI provider. When a model is excellent, it is easy to treat it like infrastructure. Then one of these happens - usage limits tighten, the model changes, quality drops, pricing increases, features disappear, you get switched to another model mid-session.
And suddenly a workflow you depended on no longer behaves the same way. That is why I increasingly think the most valuable AI skill is not prompt engineering. It is workflow portability. Can you move your process between Claude, ChatGPT, Gemini, local models, or API-based setups without starting from scratch? If the answer is no, your real dependency is not on AI. It is on one vendor’s pricing and product decisions. The strongest setup is usually - one primary model, one backup model, external documentation of decisions and context reusable prompts, modular workflows.
Models will keep improving. Pricing and limits will keep changing. The people who benefit most will be the ones whose systems survive those changes. How are you handling this? Are you still relying on one model, or have you built a model-agnostic workflow?
i would like to know which are the best prompt engineering courses available on the market which are free or are very affrodable. The objective is to learn on how to create script for different softwares i use in my job. Improve automation or add feature to the software.
My goal is to have zero coding knowledge and be able to generate script using prompts for different software i use which have option to use custom python scripts.
Hey everyone,
Posted here a week ago about a AI learning roadmap we built that focus on practical understanding without the hype and jargons.
The response was positive (thanks for trying that out!). Love to see that we could help.
We took it one step futher: a library of practical AI automations using common tools like ChatGPT. E.g.
The focus is again on practicality. We start with simple-but-useful automations you can start using right away. No fancy demos that work half the time.
Each automation has detailed step-by-step instructions (and videos!). Super easy to setup.
There is also a prompt customization assistant for each automation. It asks a few questions about your preferences and customizes the prompt for you. No more "ugh this prompt doesnt work and I dont know how to tweak it". pretty convenient.
Same as before. 100% free, no sign up required. Lessons/workflows are hand-written.
About me: PhD student working on agent reliability, passionate about helping people adapt and thrive with AI.
Would love your feedback!
Edit: apparently the link i posted in the comments is not visible sometimes. Here is the link https://pocketlogic.io/dashboard/automation-library
I’ve been noticing that prompt engineering gets much harder once the AI is expected to actually complete a task instead of just answer a question. With normal chat use, the goal is usually a good response. But with agents, the prompt has to guide behavior across multiple steps, messy websites, changing interfaces, tool errors, missing context, and situations where the agent needs to know when to stop or ask for help. This is what makes products like PineAI/19Pine interesting to me, because the use case is not just “generate a good answer,” it is actually handling real customer support workflows like cancellations, refunds, and billing issues. In that kind of setup, the prompt alone is not enough.
It feels like the real challenge is less about making the model sound smart and more about keeping it stable during execution. Things like state tracking, retries, verification, memory, and clear success conditions seem just as important as the prompt itself.
This sub gets the assignment better than most so I'll be direct.
The no-code movement solved half the problem. You can build almost anything now without knowing how to code, which is genuinely incredible and wasn't true five years ago. But there's still a gap that nobody talks about. Even with the best no-code tools you still have to know which tools to pick, how to connect them, how to write copy that converts, how to set up ad accounts, how to source products, how to structure a funnel. The learning curve didn't disappear, it just moved.
Most people in this sub know exactly what I mean. You've spent a weekend deep in Zapier trying to get two things to talk to each other that should just work. You've rebuilt your Webflow site three times because the first two didn't convert. You've watched your Notion dashboard get more elaborate while the actual business stayed the same size.
That's the gap Locus Founder closes.
You describe what you want to build. The AI handles everything else. It sources products directly from AliExpress and Alibaba (or sell YOUR OWN digital services, products, or content), builds a real storefront around them, writes conversion-optimized copy, then autonomously creates and runs ads on Google, Facebook and Instagram. No Zapier. No Webflow. No piecing together eight tools that half work. Just a running business.
If you don't have an idea yet it interviews you and figures out what makes sense for your situation.
We got into YCombinator this year and we're opening 100 free beta spots this week before public launch. Free to use, you keep everything you make.
For the people in this sub specifically, this isn't a replacement for no-code tools for people who love building. It's for everyone who wanted the outcome but never wanted to become a tools expert to get there. Big difference.
Beta form: https://forms.gle/nW7CGN1PNBHgqrBb8
Happy to answer anything about how it works under the hood.
The monthly cost for AI tools is starting to look like a premium cable package. When you add up a text generator, an image generator, and a coding assistant, it gets expensive fast.
Lately, I’ve been digging through GitHub to find out if free, open-source repos can actually replace the paid giants we’re all used to. The short answer: Yes, and the privacy benefits are a massive bonus.
Instead of paying for a bunch of different platforms, you can use UI wrappers and local model runners to handle heavy lifting right on your own hardware.
I just published a post covering the exact GitHub repos that are replacing things like ChatGPT Plus, Midjourney, and Copilot. I focused on tools that are genuinely useful for everyday tasks, not just highly technical research projects.
Check out the full list and setup guide here:https://mindwiredai.com/2026/05/19/free-github-repos-replace-ai-subscriptions/
Curious to hear from this sub—have you fully transitioned to local AI yet, or are the paid models still too far ahead in convenience for you to cancel?
You wake up, look at your calendar, and feel an immediate weight in your chest. The spreadsheets look empty. The meetings feel like theater. You are successful on paper, but inside, you are running on fumes. You know all the standard career advice—"change your mindset," "find a new job," "set boundaries"—but none of it bridges the gap between your daily tasks and a sense of actual worth.
Viktor Frankl, a psychiatrist and Holocaust survivor, discovered that humans can endure almost anything if they have a "why." In his groundbreaking work Man's Search for Meaning, he proved that meaning isn't something you create out of thin air; it is something you detect in your existing reality. By turning Frankl's principles of logotherapy into highly specific AI prompts, you can stop waiting for a dream job to save you and start uncovering profound purpose exactly where you are standing right now.
Extracts deeper personal resonance from an exhausting daily task.
Act as a career strategist specializing in Viktor Frankl's logotherapy.
I am struggling to find value in a specific work task: [DESCRIBE THE TASK].
Analyze this task through three lenses:
1. Who ultimately benefits from this work being done exceptionally well?
2. What specific inner strength or virtue (e.g., patience, precision, integrity) does this task test or develop in me?
3. How does mastering this task serve my long-term growth?
Provide a step-by-step breakdown that reframes this task from a chore into a meaningful exercise in character development.
Transforms current professional friction or unfair situations into a source of personal power.
Act as a psychological coach. I am currently experiencing significant professional suffering due to [DESCRIBE THE WORKPLACE STRUGGLE/UNFAIR SITUATION].
Frankl taught that when we can no longer change a situation, we are challenged to change ourselves.
Help me process this by answering:
1. What is this situation forcing me to accept that I cannot control?
2. What is the single most honorable, dignified way I can choose to respond to this challenge tomorrow?
3. What hidden resilience am I building by enduring this with grace?
Generate a daily response blueprint to help me maintain my dignity and purpose in this environment.
Identifies the unique value you offer that cannot be easily replaced by a machine or another person.
Act as an executive performance coach. I feel like an unappreciated cog in a machine at my current role: [INSERT JOB TITLE/ROLE].
Frankl emphasizes that meaning is found in what we give to the world through our unique creations and work.
Ask me 3 targeted questions about my specific skills, the unique way I interact with colleagues, and the problems only I seem to notice.
Once I answer, synthesize my responses into a "Unique Contribution Statement" that highlights my irreplaceable value to my team and my field.
Shifts your perspective from superficial daily metrics to a long-term, value-driven legacy.
Act as a life-design mentor. Help me draft a professional "Meaning Statement" that replaces traditional, achievement-based goals with value-based impact.
My current career field is [FIELD] and my primary responsibilities are [RESPONSIBILITIES].
Instead of focusing on promotions or revenue, help me write a 3-sentence statement centered on:
1. The human suffering or confusion I want to alleviate through my work.
2. The core values (like truth, justice, or beauty) I want my work to embody.
3. The legacy I want to leave behind for the next generation in this industry.
Uncovers moments of meaning through workplace connections, nature, or artistic appreciation during the workday.
Act as an intentional living coach. Frankl noted that we find meaning not just in work, but in experiencing reality—through love, nature, art, or genuine connection.
My workday is currently structured like this: [BRIEFLY DESCRIBE DAILY SCHEDULE].
Analyze this schedule and suggest 5 micro-interventions (lasting less than 5 minutes each) where I can actively experience meaning.
Focus on deep listening with a coworker, appreciating design, or practicing radical presence during mundane moments.
Generates a perspective-shifting message from your future self to guide your current choices.
Act as a creative writing partner and wise mentor. Imagine I am looking back on my current career crisis from 20 years in the future.
My current age/stage is [AGE/CAREER STAGE] and my biggest fear right now is [INSERT CURRENT FEAR/DOUBT].
Write a highly personalized, comforting, and direct letter from my future self to my present self.
The letter must explain how this exact period of pointlessness was actually the essential catalyst that forced me to discover my true calling and inner strength.
Maintains hope and constructive action when the broader company or economic outlook feels grim.
Act as a leadership philosopher. My company/industry is currently facing [DESCRIBE SYSTEMIC ISSUE, E.G., LAYOFFS, POOR LEADERSHIP, MORALE CRISIS].
Frankl defined "Tragic Optimism" as remaining optimistic in the face of pain, guilt, and death by turning life's negative aspects into something positive.
Guide me through a strategy to practice Tragic Optimism by breaking down:
1. How to acknowledge the grim reality without becoming cynical.
2. What small, localized "good" I can do for my immediate peers this week.
3. How to use this industry downturn to redefine my personal definition of success.
Before you open your laptop tomorrow morning, sit quietly and ask yourself:
> "If this day is destined to be difficult and repetitive, what kind of person do I want to prove myself to be while walking through it?"
For more free mega-AI prompts, visit our prompt collection.
UPDATE: You guys keep using it, but I don't hear your feedback...
No signup required, anyone can try it for free.
If your project is important and complicated enough for AI (business, science, personal life), most likely you are messing up the input and I will prove that.
Go to www.briefingfox.com and write your goal (e.g. Write me a business plan for a coffee shop). Set up the 3 point configuration and let it analyze your goal.
Answer its questions and take the final output, launch it in your favorite AI and see the difference.
Let me know what you think.