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. Instead of asking a model to research a topic, analyze findings, write a report, and format it for email — all in one shot — you break the job into discrete steps. Each step gets a focused prompt, receives the previous step's output, and passes its result forward.

Unlike generic AI automation posts, this guide shows real CodeWords workflows — not just theory. Prompt chaining is a core pattern in production AI automation.

Related: what is tool use in llms, what is model context protocol, best llm orchestration frameworks, langchain vs llamaindex, AI workflow tools, CodeWords integrations, CodeWords templates.

Why prompt chaining matters

A single LLM call has limits. Context windows cap how much data the model processes. Complex instructions get partially followed. Quality degrades when you overload a prompt with multiple objectives. Anthropic's guide to building effective agents identifies prompt chaining as one of the foundational patterns for reliable AI systems.

Prompt chaining matters because:

• Quality improves: Each step has a narrow, clear objective. The model performs better on focused tasks
• Debugging is easier: When the final output is wrong, you can inspect each step's input and output to find where the chain broke
• Models can be mixed: Use a cheap, fast model for classification and a powerful model for generation. Each step gets the right tool for the job
• Token costs drop: Smaller, focused prompts use fewer tokens per call than one massive prompt that tries to do everything

OpenAI's prompt engineering guide recommends breaking complex tasks into simpler subtasks — prompt chaining is the implementation of that recommendation.

How prompt chaining works

A basic prompt chain has three elements:

Steps: Each step is a single LLM call with a specific instruction. "Classify this support ticket by urgency." "Extract the customer name and issue description." "Draft a response appropriate for the urgency level."

Data passing: The output of step N becomes input to step N+1. This can be the raw text output, a structured extraction (JSON), or a specific field from a structured response. Reliable chains use structured outputs (Pydantic models, JSON schemas) to reduce parsing errors between steps.

Control flow: Simple chains are linear. Advanced chains include branching (different prompts based on classification results), loops (re-run a step if quality checks fail), and parallelism (run multiple steps simultaneously and merge results).

Common prompt chaining patterns

Extract-then-act: Extract structured data from unstructured input, then use the structured data to drive actions. Extract entities from an email, then use those entities to query a CRM and draft a reply.

Classify-then-route: Classify incoming data into categories, then route each category to a specialized prompt. A support ticket classifier routes billing issues to one prompt chain and technical issues to another.

Generate-then-validate: Generate content, then use a second LLM call to check the content against criteria. A report generator produces a draft, then a validator checks for factual consistency and tone.

Summarize-then-synthesize: Summarize multiple documents individually, then combine the summaries into a coherent synthesis. This pattern handles inputs that exceed a single model's context window.

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.

A deep research workflow in CodeWords chains search queries, content extraction via web scraping, summarization, synthesis, and formatting — each step as a focused LLM call. 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.