{"id":15093,"date":"2026-03-27T05:16:51","date_gmt":"2026-03-27T09:16:51","guid":{"rendered":"https:\/\/www.data-mania.com\/blog\/?p=15093"},"modified":"2026-03-27T05:16:51","modified_gmt":"2026-03-27T09:16:51","slug":"improve-rag-performance","status":"publish","type":"post","link":"https:\/\/www.data-mania.com\/blog\/improve-rag-performance\/","title":{"rendered":"Improve RAG Performance With These 3 Simple Best Practices"},"content":{"rendered":"

You know when you need support for a product and they send you to a stupid chatbot that wastes your time and only provides you outdated or irrelevant responses? So annoying, right?! Well, the sad part is that some human built that untenable \u201csolution\u201d\u2026\u00a0 Today, I come as a bearer of good news. You don\u2019t have to be \u201cthat builder\u201d. There are several straightforward best practices you can put into place to improve RAG performance, so that the AI applications you build are actually helpful to other people in real life. And, I\u2019m going to share those best practices with you in this short blog post!<\/span><\/p>\n

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If you\u2019ve been following along with Convergence emails this year, then you already know what RAG is and how it\u2019s helpful. But just in case, RAG stands for Retrieval-Augmented Generation which is a methodology that combines the retrieval of custom user-provided information from a large database with a generative large language model to produce informed and contextually relevant outputs.<\/span><\/p>\n

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If you\u2019re a GPT-4 user then you\u2019re aware of custom GPTs\u2026 The ability to upload pdfs and images into your GPT to act as a custom information source is the most accessible user-friendly instance of RAG I\u2019ve ever seen. So, now that you\u2019re clear on what RAG is, let\u2019s talk about best practices you can put into place to improve RAG performance, and where you can go to <\/span>learn more about building and evaluating RAG applications<\/span><\/a>.<\/span><\/p>\n

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Three Best Practices to Improve RAG Performance, Almost Overnight \ud83d\ude09<\/span><\/h2>\n

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Garbage in, garbage out.\u00a0<\/span><\/p>\n<\/blockquote>\n

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You need a solid strategy for selecting reliable information sources for RAG because – well, the quality of your information sources directly impacts the accuracy and reliability of the content that you generate. It\u2019s the principle of garbage in, garbage out. High quality input data leads to outputs that are informed by credible and authoritative information sources. Additionally, a well-defined strategy for selecting information sources helps mitigate the risk of propagating misinformation or biased content.\u00a0 Furthermore, selecting high-quality sources enhances the model’s ability to generate nuanced and contextually relevant responses, which consequently results in significant improvements to user experience and satisfaction.<\/span><\/p>\n

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So, without further ado\u2026 my three favorite best practices for selecting reliable information sources to improve RAG performance are detailed below.\u00a0<\/span><\/p>\n

Chunking and Indexing with Advanced Retrieval<\/span><\/h3>\n

This best practice involves the preprocessing of data through \u201cchunking\u201d. Chunking is the process of <\/span>breaking down text into manageable segments for storage in embedding vectors<\/i><\/b>. This method employs a variety of indexing methods, examples of which include constructing multiple indexes for different user questions and routing user queries via an LLM to the appropriate index.\u00a0<\/span><\/p>\n

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Advanced retrieval methods, including the use of cosine similarity, BM25, custom retrievers, or knowledge graphs, improve the results of the retrieval process. Reranking the results from the retriever and employing query transformations can further refine the accuracy and relevance of the information sourced\u200b\u200b.<\/span><\/p>\n

Employing Domain-Specific Pre-Training and Fine-Tuning<\/span><\/h3>\n

This best practice focuses on tailoring the AI’s training to specific domains by <\/span>extending the original training data, fine-tuning the model, and integrating it with external sources of domain-specific knowledge<\/i><\/b>.\u00a0<\/span><\/p>\n

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Domain-specific pre-training involves building models that are pre-trained on a large data corpus that represents a wide range of use cases within a specific domain. Fine-tuning these models on a narrower dataset that\u2019s tailored for more specific tasks within the domain tends to improve RAG performance while also reducing the limitations associated with parametric knowledge (eg; context inaccuracy and the potential for generating misleading information)\u200b\u200b.<\/span><\/p>\n

Improve RAG Performance by Integrating with Non-Parametric Knowledge<\/span><\/h3>\n

This best practice addresses the limitations of LLMs by grounding their parametric knowledge with external, non-parametric knowledge from an <\/span>information retrieval system<\/i><\/b>. By passing this knowledge as additional context within the prompt to the LLM, it can significantly limit hallucinations and enhance the accuracy and relevancy of responses. This approach allows for the easy update of the knowledge base without changing the LLM parameters and enables responses that cite sources for human verification\u200b\u200b.<\/span><\/p>\n

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While, taken collectively, these best practices promise to improve the accuracy, reliability, and context relevance of responses generated by RAG systems, there is more you can do to improve RAG performance. For that, I encourage you to attend this free on-demand <\/span>training session<\/span><\/a>, which is a beginner\u2019s guide to building & evaluating RAG applications.<\/span><\/p>\n

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A Beginner\u2019s Guide to Building & Evaluating RAG Applications<\/span><\/h2>\n

Join us for an exclusive on-demand training that\u2019s designed to demystify RAG and its place in the world of LLMs and precise information retrieval.\u00a0<\/span><\/p>\n

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You\u2019ll see the mechanics of how RAG is revolutionizing text generation, while also learning how to leverage RAG in your own projects right away.\u00a0<\/span><\/p>\n

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Whether you’re new to the genAI field or just looking to improve your skills, <\/span>this session<\/span><\/a> will provide incredible insights into building and evaluating effective RAG applications.<\/span><\/p>\n

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You\u2019ll come away with:<\/b><\/p>\n