Diagnose AI application failures caused by oversized, incomplete, or poorly prioritized model context.
Troubleshooting
Requests fail, truncate useful evidence, become expensive, or produce worse answers as context grows.
Reduce unnecessary context and prioritize the evidence required for the task.
Compare quality, latency, and token usage against a representative workload.
Set context budgets, monitor token usage, and test retrieval/context regressions.
More context is not automatically better. Treat context as a constrained engineering budget.
Measure prompt size, completion size, retrieved chunks, repeated history, and the model's available budget. Then compare quality as context is reduced.
Prefer relevant, ranked evidence over indiscriminate retrieval. Verify quality and cost together, then enforce context budgets in tests and observability.
Learning checkpoint
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Knowledge path
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