Debug Production: Generative AI: full course guide
The failures that only appear once a generative AI system carries real traffic: latency and cost blowouts, silent quality regressions, retrieval and context defects, and how to trace, log and run an incident on a system that is not deterministic.
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What is inside this guide
01Latency, Cost & Throughput
- Diagnosing Tail Latency in Token Streaming
- Runaway Token Spend & Cost Attribution
- Rate Limits, Throttling & Retry Storms
- Cold Starts, Model Loading & Warm Pools
- Concurrency Limits and Capacity Planning
02Silent Quality Regressions
- Model Version Drift Nobody Announced
- Prompt Edits That Break Downstream Parsers
- Hallucination Triage: Retrieval or Generation
- Non-Determinism and Irreproducible Bug Reports
- Evaluation Sets That Rot
03Retrieval & Context Failures
- Stale and Out-of-Sync Vector Indexes
- Chunking Defects That Split the Answer
- Context Overflow and Silent Truncation
- Embedding Mismatch Between Index and Query
- Right Document, Wrong Position
04Observability & Incident Response
- Tracing a Request Through the Whole Pipeline
- What to Log Without Storing User Content
- Guardrail Failures and Safe Degradation
- Playbooks for Model and Provider Outages
- Postmortems for Probabilistic Systems
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