How to Build RAG: Hybrid Search, Re-ranking, HyDE

Nov 28, 2025

Learn how to build production-grade RAG systems using hybrid search, BM25, re-ranking, and HyDE for accurate retrieval and better results.

In the fast-evolving world of AI and machine learning, retrieval-augmented generation (RAG) systems have become indispensable for applications that demand precise, contextually relevant answers. However, challenges like inaccurate or misleading results ("hallucinations") plague many RAG implementations. As professionals working with intellectual property (IP), where precision and context are paramount, understanding these challenges and the solutions available is critical.

This article breaks down the insights and techniques from a highly detailed video tutorial, offering a roadmap to creating a production-grade RAG system that retrieves accurate and useful information. By the end, you'll learn how to combine advanced search algorithms, re-ranking, and hypothetical document embeddings (HyDE) to transform your RAG workflows.

Understanding the Problem with Basic RAG Implementations

Standard RAG systems often rely on semantic search to retrieve relevant documents or context based on user queries. While semantic search excels at capturing intent, it struggles with exact matches, especially when users need highly specific information like figures, codes, or precise legal terms. This phenomenon, often referred to as "keyword blindness", can lead to frustratingly incorrect or misleading results.

For example, querying a dataset for details about "a $4.5 billion charge in the first fiscal quarter of 2026" might retrieve irrelevant chunks, such as customer complaints, instead of financial results. This inaccuracy undermines trust and efficiency, particularly in workflows where precision is non-negotiable.

To overcome these challenges, the video introduces three transformative techniques: hybrid search, re-ranking, and hypothetical document embeddings (HyDE). Each technique plays a critical role in improving recall, precision, and the overall reliability of RAG systems.

Key Approaches to Building a Reliable RAG System

1. Combining Semantic Search with Keyword Matching (Hybrid Search)

Semantic search focuses on understanding the intent behind queries but can miss exact matches critical in many use cases. By integrating BM25, a keyword-based algorithm, hybrid search bridges this gap. BM25, a tried-and-tested approach used in search engines, scores documents based on term frequency and inverse document frequency, ensuring that exact matches are prioritized.

How It Works:

Semantic Search: Captures contextual and intent-based matches.
BM25 (Keyword Search): Prioritizes exact matches, ensuring critical terms like numbers, names, or phrases are not overlooked.
Hybrid Search: Combines both methods, assigning weights (e.g., 50% semantic, 50% keyword) to tailor results to specific needs.

Example: When hybrid search is applied, a query for the $4.5 billion charge retrieves the correct document (Nvidia financial results) as the top-ranked result, while irrelevant chunks are pushed further down the list.

2. Improving Precision with Re-Ranking

Hybrid search enhances recall, casting a wide net to retrieve relevant results. However, to ensure that the most precise documents dominate the top positions, re-ranking is applied. Re-ranking involves using a more sophisticated model to filter and prioritize retrieved documents based on their contextual relevance.

Key Features of Re-Ranking:

Models like Answer AI’s CoBERT are lightweight and production-ready while maintaining the ability to understand nuanced context.
By integrating re-ranking, irrelevant results are filtered out, leaving only the top-quality matches for user queries.

Use Case for Precision: For an IP professional seeking data on a specific patent valuation, re-ranking ensures that only the most relevant financial or legal documents are returned, saving valuable time.

3. Enhancing Queries with Hypothetical Document Embeddings (HyDE)

Even the best retrieval systems falter when faced with vague or incomplete user queries. This is where HyDE (Hypothetical Document Embeddings) comes in. The technique uses a smaller language model to generate hypothetical, context-rich answers to queries, which are then used as augmented inputs to improve retrieval performance.

How HyDE Works:

A lightweight model generates a hypothetical answer based on the user’s query.
This hypothetical answer is appended to the original query, adding rich context and specificity.
The enhanced query is passed through the retrieval pipeline, resulting in far more accurate and relevant results.

Practical Application for IP: When searching a large database for prior art, HyDE can generate a hypothetical summary of the search intent, improving the relevance of retrieved documents.

4. Integrating Citations for Transparency

In professional workflows like intellectual property management, the ability to cite sources is critical. By adding a prompt to your RAG pipeline, you can ensure that the system generates not only answers but also references the specific documents or chunks used. This builds trust and offers users a clear path for verification.

Putting It All Together: Building a Complete RAG Pipeline

To create a robust RAG pipeline, the video outlines a step-by-step process that integrates all three techniques into a seamless workflow:

Query Parsing: Start with the user’s input query.
HyDE Augmentation: Generate a hypothetical answer to enrich the query.
Hybrid Search: Retrieve documents using a combination of semantic search and keyword matching (BM25).
Re-Ranking: Apply a lightweight contextual model to refine the list of retrieved results.
Citations: Format and provide direct references for each retrieved document.

This pipeline ensures high recall (finding all relevant documents), precision (prioritizing the right ones), and transparency (citing sources).

Key Takeaways

Semantic Search: Ideal for capturing intent but prone to missing exact matches critical for precision-focused applications.
BM25 (Keyword Search): A proven algorithm for ensuring exact matches, critical for IP professionals handling detailed queries.
Hybrid Search: Combines semantic and keyword search for a balanced retrieval method.
Re-Ranking: Filters out irrelevant results, ensuring only top-quality matches are presented.
HyDE: Enhances vague queries by generating hypothetical answers, improving retrieval performance on large datasets.
Citations: Adds transparency by providing source references for retrieved documents.
Production-Ready Solutions: The combination of these techniques transforms basic RAG systems into robust, professional-grade tools suitable for IP workflows.

Conclusion

The techniques explored in this guide - hybrid search, re-ranking, and HyDE - are game-changers for building reliable RAG systems. For professionals in intellectual property, where time efficiency and precision are paramount, these methods provide a clear path to transforming how complex queries are handled. By implementing these strategies, you can ensure your RAG system delivers results that are not only accurate but also actionable and verifiable, elevating the quality and reliability of your work.

Whether you're managing patent portfolios, conducting prior art searches, or analyzing legal documents, these insights will empower you to build next-generation retrieval systems that meet the rigorous demands of modern IP workflows.

Source: "Build Production-Ready Retrieval RAG Pipeline in LangChain | Hybrid Search (BM25), Re-ranking & HyDE" - Venelin Valkov, YouTube, Aug 23, 2025 - https://www.youtube.com/watch?v=YNcoFoRwoc8