Tutorials, product updates, and insights from the Meilisearch team
Introducing Meilisearch Chat: turn your Meilisearch index into a conversational AI with a single /chat endpoint. Faster launches, lower costs, and direct answers your users actually want.
Discover the main differences between Retrieval-Augmented Generation (RAG) and Cache-Augmented Generation (CAG), and which one is best for you.
Discover how to build RAG applications in Ruby with this developer’s guide, covering essentials, tools, step-by-step setup, and how Ruby compares to Python.
Step-by-step guide to building RAG applications with Ruby on Rails, covering core concepts, pitfalls, and best practices for production-ready AI apps.
Your monthly recap of everything Meilisearch. September 2025 edition.
Discover what RAG evaluation is, what methodologies, frameworks and best practices are used, how to implement it and more.
A guide to modular RAG. Discover what it is, how it works, its advantages and disadvantages, how to implement it, and much more.
![What is GraphRAG: Complete guide [2025]](/_next/image?url=https%3A%2F%2Fres.cloudinary.com%2Fmeilisearch%2Fimage%2Fupload%2Fv1766418786%2Fblog%2Fcovers%2Fgraph-rag-feature.png&w=3840&q=75)
Discover how GraphRAG improves traditional RAG by using graph-based reasoning to deliver more accurate, explainable, and context-rich AI responses.
Discover what agentic RAG is, how it works, the benefits, the challenges, the drawbacks, common tools used in agentic RAG pipelines & much more.
Walk through a practical RAG workflow with Meilisearch – query rewriting, hybrid retrieval, and LLM response generation – simplified by a single, low-latency platform.
Learn how adaptive RAG improves retrieval accuracy by dynamically adjusting to user intent, query type, and context – ideal for real-world AI applications.
