Topic
RAG Pipeline Observability
14 posts on rag pipeline observability — part of developer tools on the n4n AI blog.
Why RAG hallucinations often trace back to retrieval
Retrieval failures cause most RAG hallucinations. This analysis breaks down the root causes in chunking, embedding, and ranking—and how to observe them.
What is RAG observability, and why it's different
What is RAG observability? It's tracing retrieval, embedding, and generation in LLM pipelines. This guide explains how it works, why it matters, and debunks myths.
Tracing embedding, retrieval, and generation as one span
Practical guide to tracing embedding retrieval generation as one span in RAG pipelines, with real OpenTelemetry code and pitfalls.
RAGAS metrics explained: faithfulness, relevance, and recall
RAGAS metrics explained: faithfulness, answer relevancy, and context recall quantify RAG pipeline quality. Learn how each metric works, with code and pitfalls.
Monitoring vector database latency in RAG pipelines
A practical guide to vector database latency monitoring in RAG pipelines: instrumentation, percentile tracking, correlation, SLOs, and common pitfalls.
Monitoring hybrid search RAG pipelines: keyword plus vector
Practical guide to monitoring hybrid search RAG pipelines: instrument keyword and vector retrieval, fusion, and generation to catch drift and cost.
Measuring retrieval precision and recall in production RAG
A practical how-to for engineering teams measuring retrieval precision and recall RAG in production, with code to instrument, compute, and validate.
Logging retrieved chunks for RAG debugging without bloat
Learn how to implement logging retrieved chunks RAG pipelines without bloating your logs: structured schemas, sampling, sidecar storage, and verification.
Detecting stale embeddings before they hurt RAG results
Practical steps to detect stale embeddings in RAG pipelines before they degrade retrieval quality, with code for drift checks and monitoring.
Debugging poor retrieval quality in a RAG pipeline
Practical steps for debugging retrieval quality RAG: measure recall, inspect embeddings, tune chunking, add reranking, and verify fixes with real queries.
Debugging chunking strategy through retriever traces
A practical guide to debugging chunking strategy RAG pipelines by capturing retriever traces, spotting boundary splits, and tuning overlap.
Context relevance scoring for RAG pipeline monitoring
Context relevance scoring RAG measures whether retrieved documents actually answer the query. This guide explains the metric, implementation, and pitfalls.
Comparing retrieval quality across embedding models
A head-to-head comparison of five leading embedding models for RAG, covering retrieval quality, cost, latency, and ergonomics to guide your selection.
Building a RAG evaluation dashboard with retrieval metrics
Hands-on tutorial to build a RAG evaluation dashboard that tracks retrieval precision, recall, and latency using Python, Flask, and SQLite step by step.
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