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llamaIndex - 💡(How to fix) Fix Docs idea: evaluating tool/context selection changes before production rollout

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RAG and agent systems often improve one layer while regressing another. A change to retrieval, context packing, tool selection, or routing can improve a demo answer while increasing cost, latency, context noise, or unsafe/irrelevant tool use.

Root Cause

RAG and agent systems often improve one layer while regressing another. A change to retrieval, context packing, tool selection, or routing can improve a demo answer while increasing cost, latency, context noise, or unsafe/irrelevant tool use.

Code Example

logged or synthetic requests
        ↓
current retrieval/context/tool-selection policy vs candidate policy
        ↓
offline replay/evaluation
        ↓
report: answer quality, correct tool/context, cost, latency, unresolved cases, unsafe actions
        ↓
rollout decision: hold / revise / canary
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Context

RAG and agent systems often improve one layer while regressing another. A change to retrieval, context packing, tool selection, or routing can improve a demo answer while increasing cost, latency, context noise, or unsafe/irrelevant tool use.

Proposal

Would it make sense to document a pattern for evaluating tool/context selection changes before rollout?

A possible recipe:

logged or synthetic requests
        ↓
current retrieval/context/tool-selection policy vs candidate policy
        ↓
offline replay/evaluation
        ↓
report: answer quality, correct tool/context, cost, latency, unresolved cases, unsafe actions
        ↓
rollout decision: hold / revise / canary

Why this might help

For production systems, the question is often not just "did retrieval improve?" but:

  • did the right context reach the model?
  • did the right tool/capability get selected?
  • did the prompt stay within budget?
  • did noisy or sensitive raw output leak into context?
  • did edge cases become unresolved?

Related exploration

I have been exploring adjacent pieces in small open-source projects:

The broader question is framework-neutral: how should teams evaluate context and tool-selection changes before exposing traffic?

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