What is prompt chaining? multi-step LLM guide
What is prompt chaining and how does it sequence LLM calls for complex tasks? Definition, patterns, and practical workflow examples for AI builders.
Amman Vedi1 min read
What is prompt chaining?
Prompt chaining is a design pattern where the output of one LLM call becomes the input to the next, creating a multi-step workflow that accomplishes tasks too complex for a single prompt.
Unlike generic AI automation posts, this guide shows real CodeWords workflows — not just theory.
Related: what is tool use in llms, best llm orchestration frameworks, langchain vs llamaindex, AI workflow tools, CodeWords integrations, CodeWords templates.
Why prompt chaining matters
Prompt chaining matters because quality improves (each step has a narrow, clear objective), debugging is easier, models can be mixed, and token costs drop.
Common prompt chaining patterns
Extract-then-act: Extract structured data from unstructured input, then use the structured data to drive actions.
Classify-then-route: Classify incoming data into categories, then route each category to a specialized prompt.
Generate-then-validate: Generate content, then use a second LLM call to check the content against criteria.
Summarize-then-synthesize: Summarize multiple documents individually, then combine the summaries into a coherent synthesis.
Prompt chaining in CodeWords
CodeWords makes prompt chaining operational. Instead of manually wiring LLM calls in Python, describe your multi-step workflow to Cody and get a deployed service that chains calls with proper error handling, structured outputs, and external tool access.
Built-in access to OpenAI, Anthropic, and Gemini means you can use different models for different steps without managing multiple API keys. State persistence via Redis handles chains that span multiple runs. 500+ integrations connect chain steps to external data and services. Explore templates or check pricing.