Meilisearch latest news and company updates

How to Build RAG Applications on Rails: Step-by-Step Guide
Step-by-step guide to building RAG applications with Ruby on Rails, covering core concepts, pitfalls, and best practices for production-ready AI apps.

Meilisearch September Updates
Your monthly recap of everything Meilisearch. September 2025 edition.

RAG evaluation: Metrics, methodologies, best practices & more
Discover what RAG evaluation is, what methodologies, frameworks and best practices are used, how to implement it and more.

Modular RAG: What it is, how it works, architecture & 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%2Funable-actionable-car.media.strapiapp.com%2FWhat_is_Graph_RAG_Complete_Guide_aa920eb919.png&w=1200&q=75)
What is GraphRAG: Complete guide [2025]
Discover how GraphRAG improves traditional RAG by using graph-based reasoning to deliver more accurate, explainable, and context-rich AI responses.

What is agentic RAG? How it works, benefits, challenges & more
Discover what agentic RAG is, how it works, the benefits, the challenges, the drawbacks, common tools used in agentic RAG pipelines & much more.

From RAG to riches: Building a practical workflow with Meilisearch’s all-in-one tool
Walk through a practical RAG workflow with Meilisearch – query rewriting, hybrid retrieval, and LLM response generation—simplified by a single, low-latency platform.

Adaptive RAG explained: What to know in 2025
Learn how adaptive RAG improves retrieval accuracy by dynamically adjusting to user intent, query type, and context—ideal for real-world AI applications.

Speculative RAG: A faster retrieval-augmented generation
Discover how speculative RAG improves traditional RAG with faster drafts, smarter retrieval, and better performance for advanced AI workflows.