Making RAG Smarter: Improving Accuracy

In my previous blog on Retrieval-Augmented Generation (RAG), I broke down what RAG is, why it matters, and how it supercharges LLMs with external knowledge.
Then, in my follow-up post, I shared the common failure points in RAG systems and how to fix them quickly.

I recently started digging deeper into RAG (Retrieval-Augmented Generation) and realized that while the basic RAG architecture is powerful, it’s also far from perfect. So, in this article, let me explain:

• How basic RAG works

• Why RAG struggles sometimes

• Different optimization techniques to improve accuracy

• When not to overengineer things

How Basic RAG Works

At its core, a RAG system does something simple:

1. Take user input → a query or question.

2. Convert it into vector embeddings → numerical representations of meaning.

3. Search the vector database → e.g., Qdrant, Pinecone, or FAISS.

4. Retrieve relevant chunks of information.

5. Send the retrieved chunks + user query to an LLM.

6. LLM generates an answer using both its knowledge + provided context.

Sounds neat, right? But here’s the problem…

The Garbage In, Garbage Out (GIGO) Problem

RAG is only as good as the input you give it.
If the user’s query is vague, incomplete, or inconsistent, the retrieved context may not match well, leading to poor answers.

For example:

• Your vector DB has chunks about “machine learning model deployment”

• The user asks: “How to put my AI online?”

• The retriever might miss relevant chunks because the wording doesn’t match, even though the intent is related.

So, we need smarter techniques to bridge this gap and make RAG more accurate.

Ways to Make RAG Smarter

1. Query Rewriting (Simplest Fix)

Idea:
Before hitting the vector DB, rewrite the user’s query to make it more clear, structured, and context-friendly.

Flow:

How it helps:

• Better embeddings → better chunk retrieval

• More consistent matches with your knowledge base

When to use it:

• Works great for small optimizations

• Minimal performance impact

2. Multi-Query Retrieval (More Accurate, Slightly Slower)

Idea:
Instead of one improved query, generate multiple related queries to cover all possible angles of the user’s intent.

Flow:

Why it works:

• Covers semantic variations the original query might miss

• Retrieves more complete and accurate context

• Significantly improves overall precision

Trade-off:

• Increases retrieval time slightly

• Best for complex or ambiguous queries

3. HyDe Approach (Hypothetical Document Embeddings)

This one’s clever. Instead of directly searching the vector DB with the user’s query, we:

1. Generate a “hypothetical answer” using an LLM.

2. Convert this generated answer into vector embeddings.

3. Use those embeddings to search the vector DB.

4. Retrieve highly relevant chunks.

5. Finally, send the best chunks + user query to the LLM for final output.

Flow:

Why it works:

• The LLM “imagines” the right answer first

• This makes the retrieval process much more accurate

• Especially useful when user queries are vague or incomplete

Bonus: Combine Multi-Query + HyDe = Ultra Accuracy

For critical tasks where accuracy matters more than speed, you can combine techniques 2 and 3:

• Use HyDe to generate a better search base

• Then perform multi-query retrieval

• Finally, pick the highest-frequency chunks for the final answer

This gives you near-perfect retrieval accuracy, but it’s slower — so use it wisely.

Final Thoughts

The key takeaway here is:

RAG isn’t broken — it just needs help understanding what you really mean.

• Use query rewriting for quick wins

• Use multi-query retrieval when precision matters

• Use HyDe for vague queries or weak context

• Combine techniques only when necessary

And most importantly:

Don’t overengineer your RAG pipeline to kill a cockroach
Keep it simple unless your use case truly demands ultra accuracy.