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Context Window Problems — Troubleshooting Guide

Diagnose AI application failures caused by oversized, incomplete, or poorly prioritized model context.

1 min readLevel: IntermediateUpdated Current
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Troubleshooting

Start with the symptom. End with a verified fix.

What’s wrong?

Requests fail, truncate useful evidence, become expensive, or produce worse answers as context grows.

Possible causes

  1. Too much retrieved content
  2. oversized prompts
  3. poor chunk selection
  4. repeated conversation history
  5. token budget mismatch

Diagnosis

  1. Measure prompt and completion tokens
  2. inspect retrieved context
  3. identify repeated content
  4. compare answer quality at different context sizes

2 · Fix

Reduce unnecessary context and prioritize the evidence required for the task.

Verify

Compare quality, latency, and token usage against a representative workload.

3 · Prevent

Set context budgets, monitor token usage, and test retrieval/context regressions.

Context Window Problems

More context is not automatically better. Treat context as a constrained engineering budget.

Diagnose

Measure prompt size, completion size, retrieved chunks, repeated history, and the model's available budget. Then compare quality as context is reduced.

Fix → Verify → Prevent

Prefer relevant, ranked evidence over indiscriminate retrieval. Verify quality and cost together, then enforce context budgets in tests and observability.

Engineering companion

Learn → troubleshoot → design → prepare → test

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