What's changed: Initial: 2 sections for Domain 5 (testing, validation, and troubleshooting)
5.2Troubleshooting GenAI Applications
Learn to isolate GenAI-specific issues: context window overflow, FM API integration problems, prompt issues, retrieval issues (embedding quality/drift), and prompt maintenance with schema validation, CloudWatch Logs/X-Ray.
GenAI issues originate in input (context), retrieval, prompt, or API. Isolate the responsible layer from the symptom, then reproduce, fix, and prevent regressions systematically.
5.2.1Isolating cause from symptom
- Context overflow: truncated answers/missing info = context window overflow. Fix with dynamic chunking, prompt design, and truncation-error analysis.
- API integration issues: identify GenAI-specific integration errors via error logging, request validation, and response analysis.
- Retrieval issues: low relevance = diagnose embedding quality/drift/vectorization/chunking and optimize vector-search performance.
- Prompt maintenance: detect format inconsistencies via template testing, version comparison, and schema validation; ensure prompt observability with CloudWatch Logs/X-Ray.
Common: truncated/missing info = context window overflow (dynamic chunking/truncation analysis), off-target retrieval = diagnose embedding quality/drift/chunking, broken output format = schema validation, prompt observability = CloudWatch Logs/X-Ray.
Troubleshoot via “symptom → isolate layer → fix → prevent regression.” Truncated answers/missing info typically indicate context window overflow—fix with dynamic chunking, prompt redesign, and truncation-error analysis. Off-target answers point to the retrieval layer—diagnose embedding quality, embedding drift, vectorization, chunking, and preprocessing in turn (consistent with the prior chapter ordering: chunk → embeddings → hybrid/reranker → query preprocessing). For API integration issues, use CloudWatch Logs error logging, request validation, and response analysis, and trace service boundaries with X-Ray. Detect broken output format with JSON Schema schema validation, and resolve prompt confusion via template testing, version comparison, and systematic refinement. Prevent recurrence by baking regression tests and quality gates (prior section) into CI and continuously monitoring prompt observability (CloudWatch Logs/X-Ray).
| Symptom | Suspect layer | Fix |
|---|---|---|
| Truncated answer | Context overflow | Dynamic chunking/truncation analysis |
| Off-target answer | Retrieval (embeddings) | Diagnose embedding quality/drift |
| Broken output format | Prompt/output | JSON Schema validation |
| Unknown bottleneck | Integration/distributed | CloudWatch Logs/X-Ray |
Trap: “truncated answers always mean the model is not capable enough” is wrong—often it is context window overflow or response-size limits, fixed by dynamic chunking/prompt design. Also “off-target retrieval is fixed by using a bigger model” is wrong—first diagnose embedding quality, chunking, and search method.
5.2.2Section summary
- Truncated = context overflow / off-target = embedding/retrieval diagnosis / broken format = schema validation
- Observability = CloudWatch Logs / X-Ray / prevention = regression tests + quality gates
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Quick check
(just a quick review)Q1. In a RAG over long documents, answers are truncated or miss needed information. Most likely cause and fix?
Q2. The FM output sometimes does not match the JSON format expected downstream, causing parse failures. Best way to detect format inconsistencies?
Q3. RAG answer relevance has degraded over time. When isolating the cause, which retrieval-layer factors should you diagnose first?
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