Prompt Chaining
Breaking complex tasks into a sequence of simpler prompts, where each prompt's output feeds into the next.
Also known as: Chained Prompts, Sequential Prompting, Multi-step Prompting, Prompt Pipelines
Category: Techniques
Tags: ai, prompting, llm-techniques, workflows, automation, ai-agents
Explanation
Prompt chaining is a technique for handling complex tasks by decomposing them into a series of linked prompts, where the output of one becomes the input for the next. This mirrors how humans break down complex problems into manageable steps.
Common chaining patterns:
**Sequential Chain**:
1. Prompt 1: Research/gather information
2. Prompt 2: Analyze findings
3. Prompt 3: Generate recommendations
4. Prompt 4: Format final output
**Conditional Chain**:
- Route to different prompts based on intermediate outputs
- Example: Classify intent first, then use specialized prompts per intent
**Parallel Chain**:
- Run multiple prompts simultaneously on different aspects
- Combine results in a final synthesis prompt
Benefits:
- **Improved accuracy**: Each step is simpler and more focused
- **Better debugging**: Identify which step causes issues
- **Flexibility**: Swap or improve individual steps independently
- **Cost control**: Use cheaper models for simpler steps
- **Transparency**: Intermediate outputs show reasoning
Implementation tips:
- Design clear interfaces between prompts
- Handle errors gracefully at each step
- Consider caching intermediate results
- Validate outputs before passing to next step
Prompt chaining is fundamental to building complex AI applications, workflows, and agents that need to accomplish multi-step tasks reliably.
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