LLMs, GenAI, RAG, agents, evaluation · insight-1552
Put a programmatic gate between chained model steps so a bad intermediate result stops early
- Evidence type
- documented fact
- Difficulty
- easy
- Roles
- Applied AI, ML engineer
- Industries
- aviation
Summary: situation, action, reason, boundary
Situation
A flow of fixed steps, such as extract a booking change, look up policy, draft a reply, has no agent but sometimes sends nonsense downstream.
Action
Keep the chain as ordinary code and add a cheap check after each model step, such as schema validity, required fields present or value within range, and route failures to a retry, a fallback or a human.
Reason
Prompt chaining with programmatic checks on intermediate steps is a documented workflow pattern, and it stops errors where they occur rather than after later steps have built on them.
Boundary
Known-sequence tasks where each step has a checkable output.
Symptom or risk
A malformed or empty extraction passes silently through later steps, which then produce a confident but wrong final answer.
Exceptions
Steps whose quality cannot be checked cheaply need sampling or review instead of a gate.
Trade-offs
Each gate adds code and some false rejections.
Pitfalls
Gates that only check format let wrong but well-formed values through.
Lessons and concepts
Claims and their sources
Anthropic describes prompt chaining as a workflow in which programmatic checks, drawn as gates, can verify intermediate steps before the next one runs.