Discover what agentic search is, why it beats one-shot RAG, and how to build production-grade search tools for your AI agents with @meilisearch/ai-sdk and the Vercel AI SDK.
Discover 13 RAG chunking strategies that help you optimize context windows, improve retrieval precision, and boost overall RAG pipeline performance.
Discover how RAG for medical data grounds AI in trusted clinical sources to reduce hallucinations and improve healthcare outcomes.
Learn what context distillation is, how it works in large language models, and why it helps reduce prompt size, cost, and latency in modern AI systems.
Learn how query rewriting for RAG improves retrieval accuracy, reduces hallucinations, & boosts LLM-powered search, with practical implementation guidance.
Learn what RAG guardrails are, why they matter, and how to use them to reduce hallucinations, protect data, and deploy trustworthy RAG systems in production.
Discover how RAG for structured data improves AI accuracy and how to implement it effectively.
Learn what RAG reranking is, how it works, and why it’s critical for improving relevance, accuracy, and reliability in retrieval-augmented generation systems.
Discover how RAG for customer support improves accuracy, reduces hallucinations, and powers scalable AI support systems.
Learn how AI-powered workplace search helps teams find information faster, connect siloed tools, and improve productivity across the organization.
Learn what RAG-as-a-Service is, why it matters, common use cases, key benefits, and how to evaluate providers to build more accurate AI applications faster.
Learn what self-RAG is, how it works, and why self-reflective retrieval-augmented generation reduces hallucinations and improves reliability in LLM systems.
Learn how RAG in AI works in practice, how to improve retrieval relevance, evaluate quality, secure data, and keep results up to date in production.
Intent understanding is where most conversational search implementations quietly fail. Here's why the translation layer between natural language and structured queries is where the real differentiation lives.
Learn the key differences between knowledge graphs and vector databases for RAG, when to use each, and how to combine them for optimal results.
Explore how RAG for business boosts AI accuracy and delivers smarter, context-driven insights.
Learn what hybrid search RAG is, how it blends semantic and keyword search for more accurate retrieval, and how it works, challenges, implementation & more.
Explore the key distinctions between naive RAG and advanced RAG, including how they differ in process, accuracy, scalability, performance & more.
Building real RAG systems isn’t about flashy demos – it’s about shipping features that stay fast, accurate, and reliable as your product grows.
Compare the key differences between RAG and long-context LLMs. See which approach best suits your needs, where to apply them, and more.
Guide to what RAG indexing is, how it works, key strategies, when to refresh, and how to measure performance for grounded LLM answers.
Explore the differences between semantic search and RAG. Learn when to use each, common trade-offs, benefits, evaluations, and more.
Explore the key differences between RAG and prompt engineering. See which approach best suits your needs, where to apply them, and more.
A practical guide comparing GraphRAG and Vector RAG: how they work, key differences, pros/cons, top tools, and when to combine them for better answers.
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.
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 [2026]](/_next/image?url=https%3A%2F%2Fres.cloudinary.com%2Fmeilisearch%2Fimage%2Fupload%2Fv1786462437%2Fblog%2Fcovers%2Fgraph-rag-feature-2026.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.
Discover how speculative RAG improves traditional RAG with faster drafts, smarter retrieval, and better performance for advanced AI workflows.
Learn what Corrective RAG (CRAG) is, how it works, how to implement it, and why it improves accuracy in retrieval-augmented generation workflows.
Discover 14 types of RAG (Retrieval-Augmented Generation), their uses, pros and cons, and more.
![9 advanced RAG techniques to know & how to implement them [2026]](/_next/image?url=https%3A%2F%2Fres.cloudinary.com%2Fmeilisearch%2Fimage%2Fupload%2Fv1766418889%2Fblog%2Fcovers%2Frag-techniques-feature.png&w=3840&q=75)
Discover 9 advanced RAG techniques for 2026 and learn which tools can help you implement them.
![10 best RAG tools and platforms: full comparison [2026]](/_next/image?url=https%3A%2F%2Fres.cloudinary.com%2Fmeilisearch%2Fimage%2Fupload%2Fv1785940851%2Fblog%2Fcovers%2Frag-tools-feature-2026.png&w=3840&q=75)
Discover 10 of the best RAG tools for 2026, with key features, pricing, pros and cons (based on real users), integrations, and more.
A complete guide to RAG (Retrieval-Augmented Generation). Learn what it is, how it works, the different RAG types, the components of a RAG system & more.
Learn how to enhance LLM accuracy using Retrieval Augmented Generation (RAG) with Meilisearch's hybrid search capabilities. Reduce hallucinations and improve search relevance.
Discover how do you search in a database with LLM using MCP, RAG, and SQL translation. Unlock fast, natural language access to your business data now!
Discover how multimodal RAG transforms search by unifying text, images, audio, and video. Learn to build smarter, human-like AI experiences today.
Build smarter AI search with LlamaIndex RAG. Learn step-by-step how to create, optimize, and scale reliable retrieval-augmented generation systems.
Explore the key differences between fine-tuning and RAG. Find out which approach best suits your needs and learn how to improve performance, accuracy, and cost.
Learn how to build a RAG pipeline to boost AI accuracy, reduce hallucinations, and deliver reliable, real-time answers. Start building smarter AI today!
Meilisearch's innovative scoring system revolutionizes hybrid search by properly combining full-text and semantic search, delivering more relevant results than traditional rank fusion methods.
Struggling to make AI truly valuable at your company? Discover how we turned scattered AI usage into systematic success at Meilisearch, with a practical framework you can implement today.
The contrarian take on building better retrieval augmented generation systems.
![AI-powered search: What you need to know [2026]](/_next/image?url=https%3A%2F%2Fres.cloudinary.com%2Fmeilisearch%2Fimage%2Fupload%2Fv1766418744%2Fblog%2Fcovers%2Fai-powered-search-feature.png&w=3840&q=75)
Unlock the power of AI-powered search for your SaaS business. Learn key features, budgeting tips, and implementation strategies to boost user engagement
![The 10 best AI enterprise search tools and platforms [2026]](/_next/image?url=https%3A%2F%2Fres.cloudinary.com%2Fmeilisearch%2Fimage%2Fupload%2Fv1766418743%2Fblog%2Fcovers%2Fai-enterprise-search-feature.png&w=3840&q=75)
Discover the ten best AI tools for enterprise search in 2026. See how they compare in terms of features, capabilities, use cases, pricing, and more.
We're transforming how developers build search with Meilisearch AI. No more complex infrastructure – just powerful, intelligent search that works out of the box.
![What are vector embeddings? A complete guide [2026]](/_next/image?url=https%3A%2F%2Fres.cloudinary.com%2Fmeilisearch%2Fimage%2Fupload%2Fv1766418936%2Fblog%2Fcovers%2Fwhat-are-vector-embeddings-feature.png&w=3840&q=75)
Discover what you need to know about vector embeddings. See what they are, the different types, how to create them, applications, and more.
![How to choose the best embedding model for semantic search [2026]](/_next/image?url=https%3A%2F%2Fres.cloudinary.com%2Fmeilisearch%2Fimage%2Fupload%2Fv1766418762%2Fblog%2Fcovers%2Fchoosing-the-best-model-for-semantic-search-feature.png&w=3840&q=75)
Discover the best embedding model for semantic search in 2026. See our model performance, cost, and relevancy comparison for building semantic search.
![What is a vector database? What you need to know [2026]](/_next/image?url=https%3A%2F%2Fres.cloudinary.com%2Fmeilisearch%2Fimage%2Fupload%2Fv1766418937%2Fblog%2Fcovers%2Fwhat-is-a-vector-database-feature.png&w=3840&q=75)
Discover what you need to know about vector databases. See what they are, how they work, their benefits, examples, use cases, and more.
Learn how LSI works under the hood, see a practical Python implementation, and discover why this foundational technique remains relevant in today's AI-driven search landscape.
We've built a bridge between Meilisearch and AI assistants using the Model Context Protocol (MCP), enabling developers to manage search infrastructure through natural language.
Discover best practices for building a RAG system, with tips on optimizing documents, integrating AI, and why effective retrieval is key to success.
Learn how to implement predictive search in your software applications. Discover key concepts, optimization techniques, and real-world examples.
Discover how easy it can be to implement advanced multilingual search and give your users the seamless, relevant results they deserve, regardless of language.
Build a React movie search and recommendation app with Meilisearch's AI-powered search.
How we implemented incremental indexing in our vector store.
Meilisearch's AI journey began last summer with vector search and storage. Today, we unveil hybrid search with autogenerated embedders, advancing our AI capabilities.
How we implemented Meilisearch filtering capabilities with Arroy's Filtered Disk ANN
Overcoming the challenges to enhance ANN performance with Rust.
Meilisearch powers the discovery of 2M AI models, datasets, and demos in the Hugging Face repository.
This release introduces semantic and hybrid search capabilities to further enhance the search experience.
We're thrilled to release vector storage for Meilisearch to bring fast, relevant search to AI-powered applications.
