When your embeddings group unrelated documents togetherthat’s not an embedding problem…

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

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Видео When your embeddings group unrelated documents togetherthat’s not an embedding problem… канала Bashi Fuirkashi