@tank/llm-observability

End-to-end LLM observability tracing maps user requests through retrieval, generation, and post-processing using typed metadata and complete spans. This makes failures explainable by linking every component's execution context for reliable debugging.

RAG answer regressions after prompt updates often stem from untracked retrieval or generation changes. Comparing evaluation datasets, metrics, and baselines against rollback-ready prompt history isolates whether retrieval or generation caused the drift.

Monitoring LLM token usage and latency costs requires operational dashboards that track spend, routing trade-offs, and execution times. Capturing these metrics as production alerts ensures routing decisions and operational limits stay measurable.

Choosing between Langfuse, LangSmith, Phoenix, Helicone, and Braintrust depends on workflow fit for prompt versioning, regression testing, and feedback capture. Platform selection guidance evaluates these features against your specific release gate requirements.

Regression testing on LLM prompts requires comparing datasets, metrics, and baselines to catch quality drift before rollout. Implementing release gates with rollback-aware prompt governance ensures only evaluated and approved changes reach production.

Effective LLM trace analysis requires end-to-end spans and typed metadata across retrieval, generation, and tool calls. Structuring this metadata correctly ensures failures are explainable and comparable across different prompt versions and evaluation runs.