Retrieval-Augmented Generation (RAG) systems are becoming essential tools for building intelligent AI applications that go beyond static prompts. They allow you to query a live or private knowledge base using vector embeddings and large language models. But building a basic RAG system is only the beginning. If you're retrieving the top 10 chunks from a vector database and feeding them directly into your LLM, you're missing out on significant performance and accuracy points.

Retrieval is just the first step! Improving your RAG system isn’t just about storing better embeddings or scaling your database. The real power comes from how well you select and prepare information before handing it to the LLM.

In this article, we’ll explore effective strategies to enhance your RAG system using vector databases and LLMs—improving retrieval relevance, answer quality, and overall user experience.

Improve Chunking for Better Context

One of the most overlooked areas in RAG development is how documents are chunked. Many systems split text into equal-sized token blocks, which can cut important ideas in half and reduce retrieval relevance.

Instead, use smarter chunking strategies. Semantic chunking involves breaking content at natural points, like section breaks, headings, or complete thoughts, rather than arbitrary lengths. Recursive chunking can help maintain structure by breaking long documents first by heading, then paragraph, then sentence. Adding overlap between chunks (e.g., 10–20%) helps maintain context across boundaries.

If you're processing content like PDFs or web articles, consider layout-aware chunking that respects visual structure like columns, headers, and bullet lists. This preserves meaning and improves downstream relevance when users search.

Expand Initial Retrieval Without Losing Relevance

Vector similarity search is powerful, but by default it only retrieves a small number (top k) of matches. This can be limiting, especially when the relevant information is spread across many parts of your dataset. 

Instead, increase the match count to 50, 100, or even 200. This broadens recall and ensures more potentially relevant chunks are considered. To avoid overwhelming the LLM with irrelevant data, apply a similarity threshold (e.g., only include chunks with cosine similarity over 0.6). This simple filter can improve relevance while keeping your context window efficient.

Metadata filtering is another key optimization. If your embeddings have metadata like author, publication date, or document type, use those filters before running a vector search. This helps narrow down the most contextually appropriate content before the LLM ever sees it.

In more advanced setups, hybrid search, combining vector similarity with keyword-based search like BM25, can catch matches that embedding-based search might miss.

Use Re-Ranking to Prioritize Relevance

While vector embeddings provide broad semantic matching, they’re based on bi-encoders that process the query and document separately. This means they sometimes rank results that “sound” similar but don’t truly answer the question.

Re-ranking addresses this by using a cross-encoder model that evaluates both the query and each chunk together. It scores them based on actual contextual relevance. Tools like Cohere Rerank or open-source models like BGE-Reranker from Hugging Face are specifically designed for this.

By re-ranking your top 100 results and selecting the top 5–10 most relevant ones, you can significantly boost the precision of your LLM’s responses, leading to clearer, more accurate answers.

Handle User Queries More Intelligently

• Rewrite follow-up questions
  Users often ask follow-up questions that lack context ("explain more...", "why?" etc). Instead of passing these vague questions directly to the vector search, detect and rewrite them as standalone queries. This can be done using an LLM to analyze chat history and generate a fully self-contained version of the question. Use an LLM to detect vague follow-up questions and rewrite them into standalone queries based on chat history. This ensures better relevance during vector search.
• HyDE (Hypothetical Document Embeddings)
  Another advanced technique is HyDE (Hypothetical Document Embeddings), where you ask the LLM to generate a possible answer to the question, embed that synthetic answer, and use it to retrieve better-matching chunks. This works especially well for abstract or open-ended queries where direct matches are rare.
• Multi-Query Retrieval
  You can also try multi-query retrieval, where you generate several paraphrased versions of the user’s question and search using all of them. This improves coverage and reduces the chance of missing relevant information.

Compress Context with Summarization

If your system retrieves a great number of relevant chunks, the total token count may exceed the LLM’s context window. Instead of trimming, consider summarizing the chunks first using a lightweight summarization model. Then pass the compressed, information-dense summary to the LLM for answer generation.

This approach preserves key facts while reducing noise and cost.

Final Thoughts

Each of these improvements works independently, but together they can dramatically increase the relevance and reliability of your system’s answers, especially for educational, research, or enterprise search applications. 

Start by chunking smarter, retrieving wider, filtering tighter, and ranking more precisely. Use metadata and user history when available. Consider adding summarization or query rewriting to improve context clarity.

I hope you learned something new in this article.