Prompt Chaining

Prompt Chaining is the technique of linking multiple prompts in a sequential workflow so the output of one prompt becomes the input or context for the next, enabling large language models to tackle complex, multi-step tasks. A chain might start with “Generate five marketing ideas,” feed those ideas into a second prompt that “Select the strongest idea,” and finish with a third prompt that “Write a 200-word pitch.” Chains often blend roles—planner, researcher, editor—each running with different temperature, system instructions, or retrieved documents. Tools such as LangChain or Azure OpenAI function calling orchestrate branches, retries, and guardrails, while evaluation harnesses log token cost, latency, and success rate at every step. Benefits include modularity, improved accuracy, and easier debugging, but challenges arise with context-window limits and error propagation, mitigated by summarizing intermediate results or adding validation prompts. Prompt Chaining turns monolithic requests into structured pipelines that mirror human workflows.