Retrieval-Augmented Generation (RAG) for Medical Data

21 posts · Nov 2025 – Sep 2026

What this trail covers

Meilisearch Chat has been enhanced with a Cloud UI for easier production readiness. This includes auto-generated system prompts and guardrails based on index data, an inspector tab for debugging RAG pipeline execution (tool calls, LLM messages, search queries, and retrieved documents), and an integrate tab with code snippets for application integration. The goal is to simplify the setup and debugging of RAG workflows for use cases like support portals, internal knowledge bases, and documentation. This post introduces RAPTOR RAG, a hierarchical retrieval method that organizes document chunks into a tree structure for improved accuracy and performance in RAG systems, particularly for long documents.

All Meilisearch trails

Sep 2026Raptor RAG explained: A full guide to hierarchical retrievalThis post introduces and explains the RAPTOR (Recursive Abstractive Processing for Tree-Organized Retrieval) method for RAG systems. It details the five-step process of chunking, generating embeddings, clustering similar chunks, creating cluster summaries, and building a recursive tree. It also provides Python code examples for each step and discusses limitations such as cost, error chaining, and the need for frequent updates with dynamic data. The post contrasts RAPTOR with other RAG methods like GraphRAG and CRAG.
Sep 2026Agentic search: build AI agent search tools with MeilisearchThis post introduces agentic search as an evolution of RAG, enabling LLMs to iteratively query and filter data using search tools. It details the architecture of agentic search, contrasting it with deterministic RAG workflows by highlighting the dynamic control flow and variable latency of agentic systems. The post introduces the `@meilisearch/ai-sdk` for TypeScript, which provides search tools compatible with the Vercel AI SDK, allowing developers to build AI agents that can query Meilisearch indexes. It provides examples of building basic agentic search and more complex multi-hop agentic execution for domain-specific use cases like Magic: The Gathering rules, demonstrating how purpose-built tools and iterative querying improve context gathering and response accuracy.
Aug 202613 RAG chunking strategies for better retrievalThis post introduces and details 13 distinct strategies for chunking documents in RAG systems to improve retrieval accuracy. It explains the importance of chunking for performance, accuracy, and cost reduction, and outlines evaluation metrics like precision, recall, and context preservation. Specific strategies discussed include fixed-length, sentence-based, semantic-based, paragraph-based, sliding-window, recursive, hierarchical, context-enriched, topic-based, modality-specific, agentic, subdocument, and hybrid chunking, with pros, cons, and best use cases for each.
Jul 2026RAG for medical data: improving healthcare AI accuracyThis post details the application of RAG for medical data, focusing on improving accuracy by connecting LLMs to trusted medical datasets, reducing hallucinations, and supporting clinical decision-making through specific use cases like clinical decision support, medical research search, and patient question-answering. It outlines the process of building medical RAG systems by preparing and indexing medical datasets, and evaluating outputs, and introduces agentic RAG in healthcare for complex tasks.
Jun 2026What is context distillation in AI & how does it improve LLM efficiency?This post introduces and explains context distillation as a technique to improve LLM efficiency by identifying, filtering, and compressing input data before it's passed to the model. It details why context distillation is important (cost, efficiency, performance), how it works through a step-by-step process, and the problems it solves (limited context windows, prompt complexity, inconsistent outputs). Limitations such as setup time, potential for over-optimization, and loss of flexibility are discussed. Common techniques like prompt-based supervision, synthetic dataset generation, on-policy distillation, and iterative refinement are presented with real-world examples. The post clarifies the differences between context distillation, model distillation, and LLM fine-tuning. It highlights how Meilisearch can support LLM context workflows by acting as a precision layer for retrieving relevant chunks from a knowledge base, converting documents to vector embeddings, and reducing hallucinations.
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Jun 2026Query rewriting for RAG: how to improve retrieval accuracyThis post introduces query rewriting as a technique to improve retrieval accuracy in RAG pipelines. It defines query rewriting, explains its importance in addressing user intent mismatches and vocabulary differences, and details its workflow within a RAG system. Common techniques like query expansion, decomposition, paraphrasing, multi-query generation, and step-back prompting are described. The post also outlines implementation steps, including query normalization, LLM-based query generation, document retrieval, and final response generation. Limitations such as over-expansion, semantic drift, increased latency, and evaluation complexity are also discussed.
May 2026RAG for structured data: benefits, challenges, examples, & moreThis post details how structured data can be integrated into RAG systems, including benefits like reduced hallucinations and real-time updates, challenges such as mapping semantic queries to structured ones, and implementation strategies. It highlights Meilisearch's capability to index structured records and provide filtered semantic search, enabling hybrid search with filters and semantic matching. Examples of structured RAG applications include BI assistants, customer service help, fintech analytics, and internal copilots. The post also differentiates structured RAG from knowledge graphs and data lakes.
May 2026RAG reranking explained: better context, better answersThis post introduces RAG reranking as a post-retrieval step to reorder search results by true relevance before they reach the language model. It explains how reranking improves the quality of context provided to LLMs, reduces hallucinations, and addresses context dilution and poor top-k retrieval. The post details the two stages of reranking (initial retrieval and reranker evaluation), the role of cross-encoder models in scoring and reordering documents, and the different types of rerankers (cross-encoder, lightweight, LLM-based, hybrid) with their respective advantages and disadvantages. It also discusses common challenges such as latency, cost, scaling, and infrastructure complexity associated with implementing RAG reranking.
Apr 2026How RAG for customer support improves accuracy at scaleThis post details how Retrieval-Augmented Generation (RAG) can be applied to customer support to improve accuracy and scalability. It explains the RAG pipeline stages (ingestion, indexing, retrieval, augmentation, generation) with a focus on how support data is indexed into vector embeddings for semantic search and how retrieval selects relevant context. It also discusses generation, LLM quality, integration flexibility, cost, and security considerations for choosing an AI system for customer support. The post provides criteria for determining company readiness for RAG, including data maturity, ticket volume, automation goals, and technical capacity. It differentiates RAG from fine-tuning and introduces agentic RAG as a more advanced approach.
Apr 2026AI-powered workplace search: how it works, benefits, and moreThis post details the implementation of an AI-powered workplace search using Meilisearch. It covers installing and launching Meilisearch, connecting it with the OpenAI API, defining documents with role-based access control, configuring the Meilisearch index to handle embeddings (specifically 1536-dimensional vectors from text-embedding-3-small), and indexing documents with generated embeddings. The process involves converting user queries into vectors and leveraging Meilisearch's keyword-based search combined with semantic vector search.
Apr 2026Conversational search, out of the box: introducing Meilisearch ChatThis post introduces enhancements to Meilisearch Chat within the Cloud UI, focusing on production readiness. Key contributions include auto-generated system prompts and guardrails derived from index data, an inspector tab for detailed RAG pipeline debugging (visualizing tool calls, LLM messages, search queries, and retrieved documents), and an integrate tab with ready-to-use code snippets for application integration. The goal is to significantly reduce the effort required to set up and debug RAG workflows.
Apr 2026RAG-as-a-Service: what it is, use cases, providers & moreThis post introduces the concept of RAG-as-a-Service (RAGaaS) as a managed infrastructure solution for building Retrieval-Augmented Generation (RAG) applications. It details the end-to-end flow of RAGaaS, including ingestion, indexing, retrieval, and prompt/generation stages, highlighting how it abstracts away the complexities of vector databases and embedding generation. The post outlines the benefits of RAGaaS, such as faster time to market, reduced infrastructure overhead, improved answer quality, faster iteration, and built-in scalability. It also discusses common use cases, the difference between DIY RAG and RAGaaS, and essential features to consider when evaluating RAGaaS platforms, such as hybrid search, filtering, reranking, access control, and source citations. Finally, it touches upon the challenges associated with RAGaaS, including chunking limitations and retrieval quality.
Mar 2026Self-RAG explained: how self-reflective retrieval boosts AI outputsThis post introduces and explains Self-RAG, an evolution of RAG that incorporates self-reflection for improved AI output quality. It details how Self-RAG uses special tokens (retrieval, reflection, critique) to evaluate its own outputs, re-trigger retrieval when necessary, and address issues like hallucination and weak grounding. The post contrasts Self-RAG with standard RAG, agentic RAG, and modular RAG, highlighting its benefits (accuracy, efficiency, controllability) and limitations (cost, complexity, latency). It provides implementation guidance, emphasizing the need for a fast retrieval layer (like Meilisearch) and a retrieve-critique-regenerate loop. The future of Self-RAG is seen in more adaptive retrieval and self-evaluating AI systems.
Mar 2026Meilisearch roadmap roundup: where we're headingThis post outlines significant roadmap advancements for Meilisearch Chat, focusing on scaling RAG capabilities. Key technical contributions include the introduction of parallel multi-search for LLM tool calls, simplifying agent configuration into a single call, dynamic facet discovery, external tool calling, conversational memory, and improved source attribution with real-time citation events. The roadmap also details a chat playground in the Cloud dashboard for testing and debugging, AI-driven auto-generation of system prompts, index descriptions, document templates, search parameters, guardrails, and few-shot examples, and a Chat UI starter kit for rapid application integration.
Mar 2026How to build and optimize RAG in AI for reliable answersThis post provides a comprehensive guide to building and optimizing Retrieval-Augmented Generation (RAG) systems for reliable AI answers. It details the RAG workflow, including data ingestion, chunking, indexing, retrieval, and generation. Key optimization techniques discussed include hybrid search, metadata filtering, reranking, query rewriting, and tuning chunking strategies. The post also outlines methods for evaluating RAG quality through offline and online metrics, covering retrieval and generation performance. Common RAG implementation failures, such as subpar chunking, missing metadata, stale indexes, and lack of evaluation loops, are identified and addressed. Crucially, it emphasizes architectural security considerations for RAG, including retrieval-time access control, multi-tenant isolation, PII handling, secure API usage, and audit logs. Finally, it discusses strategies for keeping RAG results up-to-date through controlled data indexing, refreshing, and re-embedding.
Feb 2026The challenge isn't connecting to an LLM. It's figuring out what people actually mean.This post introduces the 'intent understanding problem' as the primary challenge in building AI-powered search, arguing that it's more critical than LLM integration or infrastructure. It details how The Fork tackles this by mapping conversational signals to searchable attributes. The post then positions Meilisearch's native 'chat route' as a solution, explaining how it parses intent, maps to the schema, generates queries, and returns results, leveraging the search engine's knowledge of the data schema. It contrasts this with building custom NLP pipelines and highlights the benefits of a native approach for conversational search.
Jan 2026Knowledge graph vs. vector database for RAG: which is best?This post provides a technical comparison of knowledge graphs and vector databases for RAG systems, detailing their respective strengths, weaknesses, and use cases. It explains how RAG works and discusses the advantages and limitations of each approach, including scalability, retrieval performance, and data modeling. The post highlights Meilisearch's hybrid search capabilities as a solution that combines keyword matching, vector embeddings, and reranking in a single engine, simplifying RAG pipeline development and reducing infrastructure overhead. It also touches on the concept of semantic search within these different paradigms.
Jan 2026Retrieval-augmented generation (RAG) for business: Full guideThis post provides a comprehensive guide to Retrieval-Augmented Generation (RAG) for business applications. It details the core mechanics of RAG, including data retrieval, augmentation, and response generation. The post outlines the benefits of RAG for businesses, such as improved decision-making, better customer support, and increased productivity. It also provides specific application examples across industries like finance, law, healthcare, technology, and retail. Furthermore, it introduces agentic RAG as a more autonomous approach to business intelligence and discusses common implementation challenges like data privacy, integration, scalability, and cost.
Dec 2025Understanding hybrid search RAG for better AI answersThis post details the implementation of hybrid search within a RAG pipeline using Meilisearch for keyword retrieval (BM25) and FAISS with Sentence Transformers for semantic retrieval. It explains how to combine results using Reciprocal Rank Fusion (RRF) and provides a Python code walkthrough for building a recipe assistant chatbot. The post also discusses the benefits of hybrid search for LLMs, such as improved accuracy and robustness, and outlines challenges like ranking and latency, offering solutions like weighted scoring and RAG evaluations.
Dec 2025Naive RAG vs. advanced RAG: What are the differences?This post differentiates between 'naive' and 'advanced' RAG. Naive RAG involves a single retriever, vector embeddings, and an LLM for generation, prioritizing speed and simplicity. Advanced RAG adds layers like pre-retrieval optimization (query rewriting), multi-stage retrieval, post-retrieval reranking/filtering, and context pruning for increased accuracy and reliability. The post details the workflows, benefits, limitations, and use cases for each, emphasizing that advanced RAG is suitable for enterprise-grade applications requiring high precision, while naive RAG is better for prototypes and simpler tools. It implicitly positions Meilisearch as a tool that can facilitate the transition to advanced RAG by supporting vector databases and hybrid search.
Nov 2025Retrieve and gain: 3 RAG use cases you can ship todayThis post details three practical RAG use case patterns: Retrieval-first for commercial flows (e.g., e-commerce), Retrieval + Generation for answering internal questions (e.g., policy/product queries), and Growth/Infrastructure for scaling RAG systems. It highlights how Meilisearch's unified hybrid search engine simplifies scaling by handling both lexical and semantic recall in one engine, reducing operational complexity compared to separate keyword and vector stores. It provides examples of how teams leverage Meilisearch for improved retrieval quality (e.g., Bookshop.org's 43% lift in search-to-purchase conversion), concise sourced answers (e.g., Louis Vuitton store associates), and scalable infrastructure (e.g., Hugging Face across 2.2M model cards). The post emphasizes metrics like search-to-purchase conversion, zero-results queries, and the importance of source-aware retrieval and snippet retrieval for generation.
Nov 2025RAG vs. long-context LLMs: A side-by-side comparisonThis post provides a comparative analysis of Retrieval-Augmented Generation (RAG) and long-context Large Language Models (LLMs). It details the fundamental differences in how each approach handles context, outlines specific scenarios where RAG is preferable (dynamic datasets, cost-efficiency, real-time retrieval, granular access control) versus when long-context LLMs are better suited (static, large datasets, deep reasoning on single documents). Key advantages and limitations of both are discussed, including resource usage, latency, accuracy, and maintenance costs. The post also references benchmark studies and explores real-world use cases for each, concluding that RAG remains a relevant and often more cost-effective solution for many applications, particularly those requiring dynamic data integration.
Nov 2025RAG indexing: Structure and evaluate for grounded LLM answersThis post details the process of RAG indexing, which involves data collection, meaningful chunking, metadata addition, embedding, and storage in a vector index. It explains the importance of indexing for reducing AI hallucinations and improving relevance, and outlines various RAG indexing strategies including hierarchical, hybrid, time-based, multi-representation, domain, and graph-enhanced indexing. Common challenges such as handling large datasets, picking the right chunk size, keeping embeddings consistent, and managing metadata are discussed, along with best practices for RAG indexing pipelines. Meilisearch's support for vector search and hybrid querying is highlighted as a key enabler for these capabilities.