Large language models stumble when you ask them to do too much at once. Dump a fifty page PDF into a chat window and ask

Large language models stumble when you ask them to do too much at once. Dump a fifty-page PDF into a chat window and ask for a structured analysis, a risk assessment, and an executive summary in the same breath. The result is usually thin, confused, or flat wrong. A better approach is mechanical. Split the job into discrete stages. Feed the output of the first stage directly into the second, and so on down the line. Anthropic calls this pattern prompt chaining. Google refers to it as a sequential pipeline. Both names describe the same thing: an assembly line where each station handles one specific transformation.

What This Looks Like in Practice

Instead of one giant prompt, you build a series of small, focused steps. Picture a compliance team that processes vendor security assessments. Step one extracts raw text from a scanned PDF. Step two identifies every mention of encryption standards and access controls. Step three maps those findings against an internal checklist. Step four drafts a short memo for the security lead. One agent turns the PDF into text. The next agent pulls specific data from that text. The final agent writes a summary based on that data. None of these steps are glamorous, and none of them multitask. Each part does one job well.

This is why the assembly line metaphor holds up. In a factory, one worker does not assemble the entire car. Specialization keeps quality high and failure modes narrow. The same logic applies to language models. A prompt that only asks for JSON extraction is less likely to hallucinate than a prompt that also asks for opinion and formatting in the same request.

Build Gates, Not Guesses

The weakest point in any chain is the handoff. A model might return a polite refusal, a chunk of markdown instead of JSON, or a truncated response. If that garbage flows into step two, the whole chain collapses. The fix is a gate.

A gate is not a model call. It is simple code. You write a short script that runs between steps. It might check the length of the output to ensure it is not empty. It might run a JSON schema validation to confirm the keys match what step three expects. A regex check can verify that an email address or date field is actually present before the next prompt is even constructed. This stops errors before you waste money on bad outputs. A gate costs microseconds of compute. A failed downstream LLM call costs tokens, latency, and your sanity.

Think of it as a quality checkpoint on the factory floor. You do not need AI to count widgets. You need a ruler.

When to Chain, and When to Stop

Prompt chaining is a poor fit for every problem. Use it when the work has fixed, repeatable steps. Monthly financial reports, standardized contract review, and log analysis pipelines are good examples. If you can write the procedure as a checklist, you can probably chain it. You should also reach for chaining when you need high accuracy for complex work. Breaking a problem into stages forces the model to handle one logical layer at a time. Finally, chains are easier to debug than monolithic prompts. When the summary is wrong, you inspect the extraction. When the extraction is wrong, you inspect the source text. You have intermediate artifacts to examine.

Avoid prompt chaining when you do not know the steps in advance. Exploratory research, open-ended brainstorming, or investigative tasks do not follow a straight line. Skip it too when speed is your only priority. Chains are serial; step two cannot start until step one finishes. If your steps do not depend on each other, run them in parallel instead. There is no reason to chain three independent translations of the same document.

The Rigidity Trap

The trade-off for all this structure is rigidity. A fixed chain cannot adapt to new situations. If a vendor sends a six-field form and your schema validation gate expects five, the line stops. If a user uploads a Word document instead of a PDF, the first step breaks and the rest of the chain has nothing to chew on.

Worse, errors propagate. A mistake that happens early flows through the whole chain. If the PDF extractor drops a negative symbol from a financial figure, every downstream step treats that wrong number as