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Hands-on AI Playbook

Prompt Chaining

Prompt chaining breaks a complex task into a sequence of smaller steps. Each step (LLM call) builds on the output of the previous one, with intermediate gates that validate quality before proceeding. If an output fails validation, the process can exit early to prevent error propagation.

Think of it as an assembly line where each station contributes a specific part to the final product.

Why It Matters

Section titled “Why It Matters”
  • Increased accuracy — Each LLM call focuses on a specific goal, reducing errors and improving overall quality
  • Modularity — You can inspect and debug intermediate steps, making the workflow easier to adapt and refine
  • Enhanced reliability — Programmatic gates catch errors early, increasing confidence in the final output
  • Efficiency trade-off — This workflow trades some latency (multiple steps) for more robust, higher-quality outputs

Key Components

Section titled “Key Components”
ComponentPurposeExample
LLM Call 1Handles the initial step and produces the first outputGenerate a document outline based on user input
GateValidates the output of the previous call; continues or exitsCheck if the outline contains all necessary sections
LLM Call 2Processes the validated output from the previous stepExpand the approved outline into a detailed draft
LLM Call 3Finalizes the task by refining or transforming the outputTranslate the draft or format it for publication
ExitEnds the workflow early if validation failsStop if the outline doesn’t meet quality standards
OutputDelivers the final product after all steps complete successfullyA polished, translated document ready for publication

When to Use It

Section titled “When to Use It”
  • Multi-step processes — Tasks that naturally decompose into sequential steps (generate, validate, refine)
  • Tasks requiring validation — When intermediate outputs need quality checks before proceeding
  • Complex workflows — When different steps benefit from different prompts, models, or configurations

Example: Generating Marketing Materials

Section titled “Example: Generating Marketing Materials”

A company needs product launch copy translated into multiple languages with consistent tone:

  1. LLM Call 1 — Generate initial marketing copy from product details and target audience
  2. Gate — Validate against tone guide and campaign goals
  3. LLM Call 2 — Translate the validated copy into multiple languages
  4. Gate — Ensure translations retain tone and key messages
  5. LLM Call 3 — Format translated copy for each platform (email, social media, website)
  6. Output — Finalized, multilingual marketing materials ready for deployment

How to Implement

Section titled “How to Implement”
  1. Decompose the task — Identify the logical subtasks that make up the overall goal
  2. Define validation criteria — Establish clear checkpoints (gates) to evaluate outputs after each step
  3. Connect steps programmatically — Design the workflow so outputs from one step feed into the next
  4. Test and refine — Ensure each step performs as intended and adjust based on intermediate results

Based on Building Effective Agents by Anthropic.

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