How to Select the 5 Most Relevant Documents for AI Search Using Advanced Retrieval Techniques

Selecting the five most relevant documents for AI search is a crucial step in building an effective RAG (Retrieval-Augmented Generation) system. Without accurate document retrieval, even the most advanced language model will struggle to provide correct answers. This article dives into why this step matters, explores traditional methods, and introduces advanced techniques to improve both recall and precision. The core of a RAG pipeline begins with a user query. That query is converted into a vector embedding, which is then compared against precomputed embeddings of your document corpus—usually split into chunks with some overlap. The system retrieves the top K most similar chunks based on semantic similarity, typically between 10 and 20. These chunks are then fed into an LLM alongside the original query to generate a response. The quality of the final answer hinges entirely on whether the retrieved documents contain the right information. Traditional approaches rely on either embedding similarity or keyword search. Embedding similarity works well in most cases by measuring how semantically close a query is to each document chunk. However, it can miss relevant documents if the meaning is slightly off or if the model hasn’t seen similar phrasing before. Keyword search methods like BM25 or TF-IDF are effective for exact term matches but fail when synonyms or paraphrased queries are used. To improve retrieval, several advanced techniques can be applied. One powerful method is contextual retrieval, introduced by Anthropic in 2024. This approach enhances document chunks by using an LLM to rewrite them with relevant context from the full document. For example, a lease agreement chunk can be enriched with metadata like dates, addresses, or parties involved from earlier parts of the document. This improves the chunk’s ability to stand alone and be understood in context. Another key enhancement is combining semantic search (vector similarity) with keyword search (BM25). By retrieving top results from both methods and merging them, you increase the chance of capturing relevant documents that might be missed by either method alone. Fetching more chunks can also help improve recall, though it comes at a cost. More chunks mean higher computational load, increased risk of context bloat, and longer processing times. A better alternative is reranking—using a secondary model to re-score the retrieved chunks and reorder them by relevance. Models like Qwen Reranker are effective at boosting both recall and precision by pushing relevant documents higher in the ranking while filtering out noise. For precision, you can use LLM-based verification. Create a function that prompts an LLM to judge whether each chunk is relevant to the query. Only keep the chunks deemed relevant. While highly effective, this approach increases latency and cost due to multiple API calls, so it should be used strategically—perhaps only on a subset of candidates or in high-stakes applications. Improving document retrieval brings clear benefits. You’ll see higher success rates in answering user queries, fewer hallucinations, reduced context bloat, and better overall trust in the system. A well-optimized retrieval step ensures the LLM works with accurate, relevant information—making the entire RAG pipeline more reliable and effective. In summary, the document retrieval step is the foundation of any successful RAG system. Prioritize it. Use a mix of semantic and keyword search, apply contextual enrichment, leverage reranking, and consider LLM-based filtering when needed. By focusing on retrieving the most relevant documents—especially the top five—you lay the groundwork for a powerful, trustworthy AI search experience.