Retrieval-Augmented Generation (RAG) for Medical Data

8/5/2026

This post introduces and details 13 distinct strategies for chunking data in Retrieval-Augmented Generation (RAG) systems. It explains the importance of chunking for RAG performance and accuracy, categorizes chunking into umbrella strategies (fixed size, sentences, overlapping, semantic), and provides detailed explanations, pros, cons, best use cases, and implementation examples for each of the 13 specific strategies: fixed-length, sentence-based, semantic-based, paragraph-based, sliding-window, recursive, hierarchical, context-enriched, topic-based, modality-specific, agentic, subdocument, and hybrid chunking. It also discusses metrics for evaluating chunking performance (precision, recall, context preservation) and common pitfalls.

7/8/2026

This post introduces the concept and implementation of Retrieval-Augmented Generation (RAG) specifically for medical data. It details the technical steps involved in building a medical RAG system, including data ingestion, embedding, indexing in a vector database (mentioning Meilisearch's capabilities for hybrid search), retrieval, and generation. It also discusses the importance of evaluating RAG outputs for accuracy and relevance in the medical domain and introduces agentic RAG for more complex tasks.

6/23/2026

This post introduces and explains the concept of context distillation in AI, detailing its importance for LLM efficiency, how it works, the problems it solves, its limitations, and common techniques. It also differentiates context distillation from model distillation and LLM fine-tuning, and outlines how Meilisearch can be used to support LLM context workflows by acting as a precision layer for retrieving relevant information from knowledge bases.

6/9/2026

This post introduces query rewriting as a technique to improve retrieval accuracy within RAG pipelines by transforming user queries to better match dataset language, addressing issues like vocabulary mismatch and ambiguous intents. It details techniques such as query expansion, decomposition, paraphrasing, multi-query generation, and step-back prompting, and provides implementation steps involving query normalization, LLM-based rewriting, and retrieval.

5/14/2026

This post introduces structured RAG, explaining its benefits, challenges, and implementation. It details how structured RAG works with organized data, the retrieval process using SQL queries and metadata, and data injection into prompts using formats like JSON. The post highlights benefits such as reduced hallucinations, real-time updates, and better control over output, while also discussing challenges like mapping semantic queries to structured ones and maintaining data freshness. It outlines implementation steps including ingestion, tagging, vectorization, hybrid search, and LLM connection, and specifically mentions Meilisearch's capability to index structured records and provide filtered semantic search. The post also provides examples of structured RAG use cases like BI assistants and customer service help, and differentiates it from knowledge graphs and data lakes.

5/5/2026

This post introduces RAG reranking as a post-retrieval step to reorder search results by true relevance, improving the quality of context provided to LLMs and reducing hallucinations. It details how reranking works, the different types of rerankers (cross-encoder, lightweight, LLM-based, hybrid) and their trade-offs in terms of accuracy, latency, and cost. It also discusses common challenges like latency, cost, scaling, and evaluation.

4/28/2026

This post extends the understanding of Retrieval-Augmented Generation (RAG) by focusing on its application in customer support. It details the RAG pipeline stages in this context, explains how RAG improves accuracy and reduces hallucinations, and outlines specific use cases within customer support. It also provides criteria for companies to assess their readiness for RAG implementation and discusses factors for choosing AI systems for this domain.

4/21/2026

This post details the technical implementation of an AI-powered workplace search using Meilisearch. It outlines the steps involved: installing and launching Meilisearch, connecting it with the OpenAI API, defining document structures (including role-based access control), configuring the Meilisearch index with specific fields and vector embeddings, generating embeddings for document titles and content using OpenAI's text-embedding-3-small model, and indexing these documents with their embeddings into Meilisearch. It also explains how user queries are converted into vectors for semantic search and how Meilisearch's capabilities can be leveraged to build such search engines.

4/15/2026

Introduces a Cloud UI for Meilisearch Chat that automates system prompt generation, guardrail setup, and provides an inspector tab for debugging RAG execution. This aims to make Meilisearch Chat production-ready with less manual effort.

4/7/2026

This post introduces and defines RAG-as-a-Service (RAGaaS) as a managed infrastructure solution for Retrieval-Augmented Generation (RAG). It details the end-to-end RAGaaS workflow, including ingestion, indexing, retrieval, prompt and generation, and citations/access control. The post outlines the benefits of RAGaaS such as shorter time to market, less infrastructure overhead, better quality answers, faster iteration, and built-in scalability. It also lists common use cases, differentiates RAGaaS from DIY RAG, and highlights essential features to consider when evaluating RAGaaS platforms, such as hybrid search, filtering, reranking, access control, and source citations. Finally, it discusses challenges associated with RAGaaS, including chunking limitations and retrieval quality.

3/31/2026

This post introduces Self-RAG, an evolution of RAG that incorporates self-reflection and iterative retrieval to improve factual accuracy and reduce hallucinations. It details how Self-RAG uses special tokens for retrieval and critique, its benefits in question answering and fact verification, and its limitations regarding cost and complexity. Meilisearch is positioned as a high-performance retrieval engine within Self-RAG systems, emphasizing its low-latency retrieval and hybrid search capabilities for building robust RAG pipelines.

3/24/2026

This post provides a comprehensive guide to building and optimizing Retrieval-Augmented Generation (RAG) systems. It explains the core concepts of RAG, its role in grounding generative AI and reducing hallucinations, and details the practical steps involved in building a RAG pipeline, including data ingestion, chunking, indexing, retrieval, and generation. The post also offers actionable strategies for improving retrieval relevance through hybrid search, metadata filters, reranking, query rewriting, and optimized chunking. It outlines methods for evaluating RAG quality using both offline and online metrics, identifies common RAG implementation failures, and discusses crucial security considerations such as retrieval-time access control, multi-tenant isolation, PII handling, secure API usage, and audit logs. Finally, it addresses the importance of keeping RAG results up-to-date through controlled data indexing, refreshing, and re-embedding.

1/15/2026

This post provides a technical comparison between knowledge graphs and vector databases for RAG systems. It details how each approach models and retrieves information, their advantages (e.g., relational reasoning for knowledge graphs, scalability for vector databases), and limitations (e.g., development complexity for knowledge graphs, lack of transparency for vector databases). It also discusses hybrid approaches, highlighting Meilisearch's unified `/chat` endpoint and its hybrid search capabilities as a way to combine the benefits of both.

1/6/2026

This post expands on the business applications of Retrieval-Augmented Generation (RAG), detailing its benefits such as improved decision-making, better customer support, increased productivity, higher accuracy, and a competitive edge. It provides specific examples of RAG applications across various business functions like customer support, contract analysis, enterprise search, business intelligence, financial reporting, audit assistance, and sales enablement. The post also highlights industries that benefit most from RAG (finance, law, healthcare, technology, retail) and introduces the concept of agentic RAG for enhanced business intelligence and automation, discussing its capabilities beyond simple retrieval and response generation.

12/16/2025

This post details the implementation of hybrid search for RAG pipelines using Meilisearch. It covers combining BM25 keyword search with vector search (using Sentence Transformers and FAISS) and merging results with Reciprocal Rank Fusion (RRF). The post provides a Python code walkthrough for setting up Meilisearch, creating sample documents, implementing BM25 and semantic retrieval classes, and combining them for a recipe assistant chatbot. It also discusses challenges like ranking and scoring, and latency.

12/9/2025

This post details the differences between naive and advanced Retrieval-Augmented Generation (RAG) systems. It explains the workflows of both, including indexing (chunking, embedding, storing in vector databases like Meilisearch), retrieval (query embedding, similarity search), and generation. Advanced RAG is described as incorporating pre-retrieval optimization (query rewriting), multi-stage retrieval, and post-retrieval reranking/filtering. The post outlines the benefits and limitations of each approach, with naive RAG favoring simplicity and speed for prototypes and basic chatbots, while advanced RAG prioritizes accuracy and reliability for enterprise-grade applications in domains like finance and healthcare. It positions Meilisearch as a tool that can simplify the transition to advanced RAG.

11/26/2025

This post details three practical RAG use cases: 'Retrieval' for commercial search (e.g., e-commerce, B2B marketplaces), 'Answers' for internal Q&A (combining retrieval with generation for concise, sourced answers), and 'Growth' for scaling RAG infrastructure. It emphasizes Meilisearch's unified hybrid search approach for simplifying scaling, data management, and relevance tuning in RAG applications, contrasting it with complex multi-service architectures. Specific examples from Bookshop.org, Minipouce, Qogita, HitPay, Louis Vuitton, Symfony, OCTO Technology, CarbonGraph, Hugging Face, TutKit, and Bildhistoria are provided to illustrate these patterns.

11/25/2025

This post provides a detailed comparison between Retrieval-Augmented Generation (RAG) and long-context Large Language Models (LLMs). It outlines the core mechanisms of each approach, their advantages (e.g., RAG for dynamic data and cost-efficiency, long-context LLMs for static data and deep reasoning), and their limitations (e.g., RAG's retrieval accuracy and maintenance costs, long-context LLMs' resource usage and scaling challenges). The post also discusses benchmark findings, cost impacts, and real-world use cases for both, concluding that advancements in long-context LLMs are beginning to challenge traditional RAG pipelines by offering alternatives for certain tasks.

11/18/2025

This post details the process of RAG indexing, including data collection, chunking, metadata addition, embedding, and storage in a vector index, to improve AI grounding and relevance. It explores various indexing strategies like hierarchical, hybrid, time-based, multi-representation, domain-specific, and graph-enhanced indexing, and discusses common challenges and best practices for RAG indexing pipelines, highlighting Meilisearch's support for vector and hybrid search.