May 20
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that’s a RAG system failure. Here’s how I’d debug it 👇 I’d break it into 5 parts: ingestion, embeddings, retrieval, generation, monitoring 1️⃣ Ingestion is where most people mess up Bad input = bad embeddings • Check chunking (too big = mixed topics, too small = no context) • Remove noise (headers, footers, repeated text) • Fix formatting issues (tables, OCR errors) • Validate metadata (wrong doc/page tagging breaks everything) Key insight: your embeddings are only as good as your chunks 2️⃣ Embeddings are rarely the real issue but you still need to verify them • Are you using the right model for your domain? • Are vectors normalized correctly? • Are you mixing embedding models? (huge mistake) • Test similarity manually (sanity check neighbors) If “dog” is close to “car” your problem is upstream or model choice 3️⃣ Retrieval layer is usually the real culprit This is where systems break • Check ANN index configuration (HNSW, IVF params) • Verify distance metric (cosine vs dot vs L2 mismatch) • Inspect top-k results without reranking • Test with metadata filters ON vs OFF Key insight: bad retrieval looks like bad embeddings but it’s often just bad indexing 4️⃣ Reranking + generation can hide problems You might be masking retrieval issues • Remove reranker → inspect raw results • Check if LLM is hallucinating around bad context • Reduce chunk count (too many = noise) • Ensure top results are actually relevant More context doesn’t fix bad retrieval it amplifies it 5️⃣ Monitoring is how you actually fix it long-term Otherwise you’re guessing • Track retrieval accuracy (recall@k) • Log failed queries + inspect manually • Compare query → expected doc vs actual doc • Set up evaluation datasets If you can’t measure it you can’t fix it BOTTOM LINE: When embeddings look wrong it’s almost never just embeddings It’s your entire RAG system pipeline Most people tweak models real AI engineers debug systems #aiengineer #softwareengineer #aijobs #tech #jobmarket #ai