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Prompt Chaining: Building Multi-Step LLM Workflows

Prompt chaining connects multiple LLM calls to accomplish complex tasks. Instead of solving everything in one prompt, chains break the task into manageable steps. Each step builds on the previous output.

Chain Types

Sequential chains pass output from one step to the next. Example: extract text → summarize → translate. Each step depends on the previous one. Sequential chains are simple but accumulate latency.

Parallel chains execute independent steps concurrently. Example: research a topic from multiple sources simultaneously. Parallel chains reduce total execution time for independent sub-tasks.

Conditional chains use the output of one step to decide what to do next. Example: classify the user intent, then route to the appropriate handler. Conditional chains enable flexible, adaptive workflows.

State Management

Chain state accumulates across steps. Store intermediate results in a structured format (JSON). Pass relevant context to each step. Clean up unnecessary state to reduce token consumption.

Error Handling

Each chain step can fail. Implement retry logic for transient failures. Use fallback prompts for repeated failures. Validate outputs between steps. Log chain execution for debugging.

Performance

Minimize chain length. Each step adds latency and cost. Combine related tasks into fewer, larger prompts. Cache identical prompt results. Monitor token usage per chain execution.

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