Prompt Chaining

Prompt chaining is exactly what it sounds like: instead of asking one mega-prompt, you split a task into a sequence of smaller prompts, where each output becomes input for the next.

It’s how you go from “AI gave me a mushy answer” to “AI produced something I can ship.” And it works whether you’re an AI engineer building a workflow, or a sales/ops leader trying to turn messy notes into clean deliverables.

When to Use It

Prompt chaining shines when tasks are:

• Multi-step (analysis → decision → output)
• High-stakes (emails to clients, legal summaries, exec briefings)
• Structured (JSON, tables, checklists, tickets)
• Ambiguous (you need clarifying questions before producing the final deliverable)

If you find yourself writing “do A, B, C, D, and also…” in one prompt, chaining is your escape hatch.

The Core Pattern

A clean chain often looks like this:

1. Clarify what’s missing
2. Extract facts from the input
3. Transform the facts into a plan
4. Generate the final artifact
5. Check the output for quality

Not every chain needs all five, but this structure is a great default.

Example 1: Engineer Chain (Debug + Patch)

Goal: fix a bug without blindly trusting the model.

Step 1 — Diagnose (analysis only)

Context: You are a senior backend engineer.
Instruction: Identify the most likely root cause and list 3 hypotheses ranked by likelihood.
Input Data: Here is the stack trace and the function. (paste)
Output Indicator: Return a table: Hypothesis | Evidence | How to Validate.

Step 2 — Propose a patch

Context: Same codebase. We prefer minimal changes.
Instruction: Implement the fix for the top-ranked hypothesis.
Input Data: Use the exact function from above.
Output Indicator: Return a unified diff patch and a short explanation (<=120 words).

Step 3 — Add guardrails

Context: We want to prevent regressions.
Instruction: Write 3 unit tests that would fail before the fix and pass after.
Input Data: Function + patch.
Output Indicator: Provide tests in pytest format with clear test names.

This chain keeps the model honest: you’re forcing it to explain, act, and prove.

Example 2: Non-Technical Chain (Sales Call → Follow-up Email)

Goal: turn a messy transcript into a crisp, personalized follow-up.

Step 1 — Extract what matters

Context: You are a sales ops assistant.
Instruction: Extract key facts from this call transcript.
Input Data: (paste transcript)
Output Indicator: Return bullets under: Pain Points, Current Workflow, Decision Criteria, Stakeholders, Next Steps.

Step 2 — Draft the email

Context: Write a follow-up email to the prospect.
Instruction: Draft a concise email referencing the extracted pain points and next steps.
Input Data: Use the extracted bullets exactly.
Output Indicator: Email format with: subject line + 120–160 word body + 3 bullet recap + 1 clear CTA.

Step 3 — Tone check

Context: We want confident, not pushy.
Instruction: Rewrite the email to be warmer and more consultative.
Input Data: The email draft.
Output Indicator: Provide the revised email and list 3 changes you made.

This chain is reliable because each step is simple—and you can sanity-check outputs as you go.

Takeaway

Prompt chaining is the easiest way to make AI outputs more accurate, more controllable, and more reusable. Treat complex work like a pipeline: extract → transform → generate → verify. You’ll spend less time “fighting the model” and more time shipping results you trust.