Prompt Engineering

Prompt Chaining: Outputs as Inputs

Prompt chaining runs prompts in sequence where each output becomes the next prompt's input — the technique that turns a chat into a pipeline.

Open in Multi-Step Prompt Builder

Overview

Prompt chaining is the core mechanic behind every multi-step workflow: Step 2 doesn't just follow Step 1, it consumes Step 1's output as input. That changes what's possible — a scoped research question feeds a candidate map, the map feeds an evaluation, the evaluation feeds a recommendation, and by the end the model is reasoning on five layers of validated context instead of improvising from a cold start. This resource loads a research goal whose generated workflow demonstrates clean chaining: every prompt explicitly references the previous step's output, and every expected-output line defines the handoff.

How to use this resource

Build the loaded chain

The CRM research goal generates a 5-step chain. Notice every prompt's 'from the previous step' reference.
Trace one handoff

Step 1 outputs criteria; Step 3 evaluates against them. That dependency is why order is non-negotiable.
Run it in one conversation

Same chat, step by step — the conversation history is the chain's memory.
Validate at each link

A weak Step 1 makes every later step confidently wrong. The expected-output line is your acceptance test.

Why This Works

Defined handoffs (expected outputs) make a chain debuggable — you know exactly which link broke
Conversation history carries the chain's state without any tooling
Layered context produces reasoning depth a cold-start prompt can't reach

Best for

Tasks where later judgments depend on earlier definitions (criteria → evaluation → verdict)
Anyone building repeatable AI pipelines in plain chat
Workflows that several people will run the same way

Not for

Independent tasks with no data flowing between them — a checklist beats a chain
Comparing two finished prompts — that's the Prompt Comparator

Use cases

Understanding the output-as-input mechanic before building chains
Converting a flat prompt list into a real chain with defined handoffs
Debugging a chain that drifts — checking the handoffs is the fix

FAQ

Why does my prompt chain drift or give confidently wrong answers by the last step?

Drift usually traces to a weak early link, not the final prompt. In this CRM chain, Step 3 evaluates candidates against Step 1's criteria, so vague criteria or a fuzzy out-of-scope list poison every later step. Test each 'expected output' against what that step promised before continuing, and fix the earliest broken link rather than the visible symptom.

How does one step actually pass its output to the next in this chain?

The handoff lives in the 'expected output' line, which names exactly what Step N produces so Step N+1 can consume it. Step 1's expected output is a scoped research question with criteria; Step 2 opens with 'Using the research question and criteria from the previous steps' and returns an unjudged candidate table. The Multi-Step Prompt Builder writes those references; you run each step and check the result.

Do I need separate chat sessions for each step of a prompt chain?

Keep the whole sequence in one conversation wherever you run it — ChatGPT, Claude, or Gemini. The chain has no external storage; the conversation history is what carries Step 1's criteria forward to Step 3's evaluation. Start each step in a fresh session and the later prompts referencing 'the previous steps' have nothing to read back.

Customize This Resource

Opens this goal in Multi-Step Prompt Builder. Build the workflow to get every step's prompt — then adjust type, complexity, and step count.

Open in Multi-Step Prompt Builder

Prompt Template

Copy it as-is, or use Open in Multi-Step Prompt Builder to load it pre-filled and customize it with your own context.

MULTI-STEP PROMPT WORKFLOW (excerpt)
Goal: Choose the best CRM platform for a 20-person sales team

STEP 1 — DEFINE THE RESEARCH QUESTION
Prompt:
Turn this research goal into a precise research question: "Choose the best CRM platform for a 20-person sales team".
Define: the decision this research will inform, 3–5 evaluation criteria, and what is explicitly out of scope.
Output: one research question, the criteria list, and the out-of-scope list.
Expected output: A scoped research question with evaluation criteria.

STEP 2 — MAP THE LANDSCAPE
Prompt:
Using the research question and criteria from the previous steps, list every relevant option, vendor, approach, or school of thought.
For each: one-line description and why it qualifies for evaluation. Do not evaluate yet.
Output: a candidate table with inclusion reasons.
Expected output: A complete candidate list, unjudged.

The chain: Step 2 consumes Step 1's criteria. Step 3 evaluates Step 2's candidates against Step 1's criteria. Each handoff is defined by the expected-output line.

How to run it: paste each step into the same conversation, validating the output before continuing.

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