Troubleshooting RAG: Typical Issues and Easy Remedies

RAG (Retrieval-Augmented Generation) helps AI give better answers by letting it look up documents first. But RAG can still go wrong. Here are five common problems, why they happen, and quick simple fixes.

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1) Poor recall

What happens:
The system can’t find the right documents, so answers miss important facts.

Why it happens:

• The document index is missing files.

• The embedding model (the part that turns text into numbers) is weak or not right for your data.

• The search settings are too strict and cut out good matches.

Quick fixes:

• Add the missing documents to your index.

• Try a better or domain-specific embedding model.

• Return more search results (raise top-k) or lower the similarity cutoff while testing.

• Use metadata (tags/filters) so the search looks in the right place.

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2) Bad chunking

What happens:
Text is split badly. Either chunks are too big and noisy, or too small and incomplete. The AI gets confused.

Why it happens:

• Chunks too large: they mix many topics.

• Chunks too small: sentences get cut in half.

• Splitting ignores natural boundaries like paragraphs or headings.

Quick fixes:

• Aim for chunks around 200–500 words (adjust for your model).

• Split at natural places (paragraphs, headings).

• Use some overlap (20–30%) so important sentences don’t get cut.

• Test with sample queries and check the returned chunks.

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3) Query drift

What happens:
The AI starts answering a different question or wanders off topic while writing the reply.

Why it happens:

• The prompt is unclear or too long.

• The retrieved chunks include unrelated context.

Quick fixes:

• Ask a clear, short question in the prompt.

• Add a one-line summary of the user’s goal to the prompt.

• Filter retrieved chunks by keywords so only relevant text is used.

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4) Outdated indexes

What happens:
The index contains old info, so the AI gives stale or wrong answers.

Why it happens:

• Documents changed but the index wasn’t updated.

• There’s no regular re-indexing schedule.

Quick fixes:

• Re-index regularly (daily, weekly, whatever fits your use case).

• Add version numbers or timestamps to documents so you can tell what’s new.

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5) Hallucinations from weak context

What happens:
The AI makes things up because it doesn’t have enough good context.

Why it happens:

• Retrieved chunks don’t fully cover the answer.

• Low-quality or irrelevant documents are included.

Quick fixes:

• Keep chunk overlap reasonable so context stays intact.

• Raise the retrieval score threshold to prefer stronger matches.

• Put a few key sentences (short context) into the prompt so the model has the facts it needs.

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Short summary

RAG can fail when the search, chunking, or prompts are off, or when the index is old. The fixes are simple: add and update documents, chunk smartly, make prompts clear, and tune retrieval. Do these and your RAG answers will be much more accurate.