Why Your RAG Retrieval Is Failing (And How to Fix It)

Why Your RAG Retrieval Is Failing (And How to Fix It)

Nov 16, 2025 - 5 Min read

The Silent Failure Mode That Breaks RAG Systems

You’ve built a RAG system. You’ve connected your documents. You’ve tested it with a few questions. It works great.

Then you deploy it. Users start asking questions. Half the time, it says “I don’t have that information” even though the answer is clearly in your documents.

This is the RAG failure mode nobody talks about: retrieval failure.

The Problem

RAG systems fail silently. They don’t hallucinate. They don’t give wrong answers. They give no answer at all.

Why? Because the retrieval step failed to find relevant documents.

Why Retrieval Fails

1. Semantic Mismatch

Your documents use one vocabulary. Users ask questions using different words.

Example:

Document: “The customer onboarding process takes 3 business days”
Question: “How long does it take to get a new user set up?”
Result: No match

The meaning is the same. The words are different. Embeddings miss it.

2. Chunking Breaks Context

Information is split across chunks in ways that break meaning.

Example:

Chunk 1: “To reset your password, click the settings icon”
Chunk 2: “Then select ‘Security’ and choose ‘Change Password’”
Question: “How do I reset my password?”
Result: Either chunk alone is incomplete

3. Too Much Noise

You retrieve 10 documents. 9 are irrelevant. The LLM gets confused and misses the one good answer.

4. Embedding Model Mismatch

You’re using a general-purpose embedding model for domain-specific content.

Example:

Medical documents with medical terminology
Legal documents with legal jargon
Technical documentation with technical concepts
General embedding model doesn’t understand the domain

5. The Question Isn’t in Your Documents

Sometimes the answer really isn’t there. But the system confidently says it doesn’t have the information instead of offering alternatives.

How to Diagnose Retrieval Failures

Step 1: Test Retrieval Directly

Don’t test the full RAG system. Test just the retrieval part.

Ask a question you know has an answer in your documents. Check what gets retrieved.

Question: "How do I reset my password?"
Retrieved documents:
1. "Password reset is available in settings" (relevant)
2. "Security best practices for passwords" (irrelevant)
3. "Password requirements for new accounts" (somewhat relevant)

Result: Found 1 good document out of 3

Step 2: Check Semantic Similarity

Use your embedding model to check similarity between questions and documents.

Question embedding vs Document embeddings:
- "How do I reset my password?" vs "Password reset in settings": 0.87 (good)
- "How do I reset my password?" vs "Security best practices": 0.42 (poor)
- "How do I reset my password?" vs "Password requirements": 0.61 (okay)

Similarity scores below 0.7 often fail to retrieve.

Step 3: Try Different Questions

Ask the same question in different ways.

- "How do I reset my password?" → Retrieved: 1/5 relevant
- "Password reset steps" → Retrieved: 3/5 relevant
- "Forgotten password recovery" → Retrieved: 2/5 relevant
- "Reset password procedure" → Retrieved: 4/5 relevant

If results vary wildly, you have a vocabulary mismatch problem.

Step 4: Check Chunk Quality

Look at what actually got chunked.

Chunk 1: "To reset your password, click the settings icon in the top right."
Chunk 2: "Then select 'Security' from the dropdown menu."
Chunk 3: "Click 'Change Password' and follow the prompts."

Are chunks meaningful on their own? Or is context split across chunks?

Solutions

Solution 1: Improve Chunking

Use semantic chunking instead of fixed-size chunks.

Bad chunking (fixed 500 characters):

Chunk 1: "To reset your password, click the settings icon in the top right. 
Then select 'Security' from the dropdown menu. Click 'Change Password' and"
Chunk 2: "and follow the prompts. You'll receive a verification email. Click 
the link in the email to confirm your new password."

Good chunking (semantic boundaries):

Chunk 1: "To reset your password: 1) Click settings icon (top right), 
2) Select 'Security', 3) Click 'Change Password', 4) Follow prompts"

Chunk 2: "You'll receive a verification email. Click the link to confirm 
your new password. The link expires in 24 hours."

Solution 2: Use Domain-Specific Embeddings

For specialized content, use specialized embedding models.

General embedding: "text-embedding-3-small" (works for everything)
Medical embedding: "BioLinkBERT" (trained on medical literature)
Legal embedding: "LegalBERT" (trained on legal documents)
Code embedding: "CodeBERT" (trained on code repositories)

Solution 3: Add Query Expansion

When a question comes in, generate multiple versions of it.

Original: "How do I reset my password?"

Expanded:
- "Password reset steps"
- "Forgotten password recovery"
- "Change my password"
- "Reset password procedure"
- "Account password reset"

Retrieve for all versions, combine results

Solution 4: Implement Hybrid Search

Combine semantic search with keyword search.

Semantic search: "How do I reset my password?" 
→ Finds documents about password resets

Keyword search: "reset" AND "password"
→ Finds documents containing both words

Combined: Union of both results
→ More comprehensive retrieval

Solution 5: Add a Fallback

When retrieval fails, have a backup plan.

If retrieval returns low-confidence results:
1. Try query expansion
2. Try broader search
3. Offer to search related topics
4. Escalate to human support

Solution 6: Improve Your Documents

Sometimes the problem is your source material.