What you will learn
- Split work where intermediate output can be inspected or validated.
- Give every step an input and output contract.
- Add branching and recovery instead of blindly passing errors forward.
01
Know when to create a chain
A chain is useful when a task contains distinct stages such as extraction, classification, planning, drafting, and review. Separating stages makes errors easier to locate and allows different models or rules to handle different work.
Do not split simple tasks into unnecessary steps. Each boundary should improve observability, reuse, validation, or control.
02
Define contracts between steps
Specify required fields, allowed values, evidence references, confidence, and error states. Prefer structured data between machine-controlled steps and readable prose at the human interface.
03
Validate before continuing
Check schema, missing fields, source identifiers, business rules, and risk conditions after each step. Retry only failures that are likely to be transient or repairable.
Route uncertainty to a human or a dedicated research step rather than allowing weak assumptions to contaminate the rest of the chain.
04
Use specialized stages
Different stages can use different prompts, context, models, and tools. Extraction may prioritize consistency; synthesis may require larger context; final editing may prioritize tone and concision.
05
Evaluate end to end and by stage
Measure final task success and intermediate accuracy. A strong final average can hide a brittle early stage whose errors are corrected only by chance.
Trace inputs and outputs with appropriate privacy controls so failed cases can be reproduced and added to the regression set.
FAQ
Common questions
Is prompt chaining the same as an AI agent?
No. A chain follows a mostly predefined sequence. An agent selects actions or tools dynamically. A system can combine both patterns.
Should every step use an LLM?
No. Use normal code, database queries, or rules when they are more reliable. Reserve model calls for tasks that benefit from language interpretation or generation.
Turn the method into a reusable instruction.
Explore expert prompts