r/ArtificialInteligence

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:

• Each prompt outputs in a labelled section structure the next prompt parses by section name
• Each prompt's instructions explicitly state what downstream prompts will do with its output
• Each prompt validates the structural integrity of its input before processing
• The original brief is re-passed with each step, with explicit anchoring to prevent drift

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.

If your only tool is a hammer, you tend to see every problem as a nail. Does anyone agree? I've found that Claude code is good for speed but when I have a complex issue Codex really does be more thoughtful.

SOURCES & REFERENCES

Shumailov et al. — "AI Models Collapse When Trained on Recursively Generated Data." Nature, July 2024. https://www.nature.com/articles/s41586-024-07566-y
Villalobos et al. (Epoch AI) — "Will We Run Out of Data? Limits of LLM Scaling Based on Human-Generated Data." International Conference on Machine Learning, 2024. https://arxiv.org/abs/2211.04325
OpenAI — o3 and o4-mini System Card (April 2025). PersonQA hallucination benchmark.
Gartner — Forecast on synthetic training data, projecting 60% of training corpora by 2024.
Duke University Library — Generative AI Student Survey (January 2025).
DeepMind — AlphaZero (chess/Go from self-play); AlphaGeometry (Olympiad-level geometry from synthetic data).
Ed Zitron — "The Truth About the AI Bubble & The Software Decline." Tech Report interview. https://www.wheresyoured.at/
Gary Marcus — "How an AI feedback loop threatens to break ChatGPT." Tech Report. https://garymarcus.substack.com/

Been watching this Salesforce situation develop for a while. Benioff confirmed on the All-In podcast that the company will spend around $300 million on Anthropic tokens this year, mostly for internal coding work.

What's interesting isn't just the number - it's the whole picture:

• Hired zero software engineers since January 2025
• AI now handles 30 to 50% of overall company workload
• Cut support staff from 9,000 to 5,000 using agents
• Agentforce just hit $800M ARR, up 169% year on year

The money that used to go into payroll expansions is now going into token spend. That's a structural shift, not a cost-cutting round.

Source: https://www.techloy.com/marc-benioff-says-salesforce-will-spend-300-million-on-anthropic-tokens-this-year/

Full breakdown here if useful: https://youtu.be/WmZyStkMM1M

Is Salesforce the template everyone else follows, or is this specific to companies that already have AI-native products to sell?

I Mainly built this as I got tired of conflicting headlines about AI profitability, and the huge amounts of money that was being spent on AI.
Site: https://isaiprofitable.com/

Most people who followed $CYDY remember March 30, 2021. The FDA publicly stated that CytoDyn's claims about leronlimab were "misleading and not supported by the data", no benefit was shown in COVID-19 treatment trials. The stock dropped 25%+ that day.

What happened afterward was a class action lawsuit covering investors who held $CYDY between March 27, 2020 and March 30, 2022.

A $500,000 settlement has been reached and terms are now submitted to the court for approval.

Who qualifies?

Anyone who held $CYDY during the class period and suffered losses from the alleged misrepresentations about leronlimab's effectiveness for HIV and COVID-19.

Can I still apply?

Yes, you can submit your application now and it will be processed once claims filing officially opens after court approval.

If you were damaged by this don't forget to check your eligibility. GL!