--- title: Best Meilisearch posts of 2025 | daily.dev description: The most upvoted Meilisearch posts of 2025, curated by the daily.dev community. canonical: https://daily.dev/tags/meilisearch/best-of/2025 twitter:card: summary_large_image twitter:site: @dailydotdev og:url: https://daily.dev/tags/meilisearch/best-of/2025 og:type: website og:site_name: daily.dev og:title: Best Meilisearch posts of 2025 | daily.dev og:description: The most upvoted Meilisearch posts of 2025, curated by the daily.dev community. og:image: https://media.daily.dev/image/upload/s--VAY5ToZt--/f_auto/v1724209435/public/daily.dev%20-%20open%20graph --- # Best of Meilisearch — 2025 1. 1 [](/posts/9-advanced-rag-techniques-to-know-how-to-implement-them-8orydew6h "9 advanced RAG techniques to know & how to implement them") Article Meilisearch · 1y 9 advanced RAG techniques to know & how to implement them Advanced RAG techniques optimize retrieval-augmented generation systems beyond basic implementations. Nine key techniques include text chunking (semantic vs fixed-size), reranking with cross-encoders, metadata filtering, hybrid search combining keyword and vector methods, query rewriting for better intent understanding, autocut for dynamic text trimming, context distillation for focused summaries, and fine-tuning both LLMs and embedding models. These methods address common issues like noisy results, irrelevant context, and poor ranking. Implementation tools include Meilisearch for hybrid search, LangChain for workflow orchestration, Weaviate for vector search, and Pinecone for scalable vector databases. Evaluation focuses on retrieval accuracy, latency, precision-recall balance, and user satisfaction metrics. 55 2. 2 [](/posts/meilisearch-1-13-7ni8qfv3a "Meilisearch 1.13") Article Meilisearch · 2y Meilisearch 1.13 Meilisearch 1.13 is now available, featuring a stable AI-powered search, remote federated search for easier sharding, and simplified version upgrades. Notable changes include breaking modifications to accepted Ollama URLs and error codes. New experimental features include a dumpless upgrade process and new stats fields, enhancing the overall search capabilities and performance. 32 1 3. 3 [](/posts/how-to-build-a-rag-pipeline-a-step-by-step-guide-d87dhlifl "How to Build a RAG Pipeline: A Step-by-Step Guide") Article Meilisearch · 1y How to Build a RAG Pipeline: A Step-by-Step Guide RAG (Retrieval-Augmented Generation) pipelines combine search engines with large language models to provide accurate, grounded responses by retrieving relevant information before generating answers. The guide covers building a complete RAG system from data ingestion and chunking through embedding generation, vector storage with Meilisearch, and integration with generative models. Key considerations include choosing appropriate tools, optimizing chunking strategies, monitoring performance, managing costs, and implementing security measures for production deployments. 26 4. 4 [](/posts/introducing-meilisearch-chat-g4hpnbekd "Introducing Meilisearch Chat") Article Meilisearch · 51w Introducing Meilisearch Chat Meilisearch launches a new /chat endpoint that transforms existing search indexes into conversational AI experiences. The feature provides built-in RAG capabilities without requiring separate vector databases or complex infrastructure. It offers an OpenAI-compatible API that handles the entire retrieval and generation workflow automatically, enabling developers to add natural language question-answering to applications in days rather than months. Use cases include e-commerce product discovery, customer support, internal knowledge bases, and developer documentation. 16 2 5. 5 [](/posts/rag-vs-cag-the-smarter-choice-for-your-ai-stack-vgmsfyzmg "RAG vs. CAG: The Smarter Choice for Your AI Stack") Article Meilisearch · 52w RAG vs. CAG: The Smarter Choice for Your AI Stack Retrieval-Augmented Generation (RAG) fetches live data from external sources for accurate, up-to-date responses but is slower and more expensive. Cache-Augmented Generation (CAG) uses pre-stored information for faster, cost-effective answers but risks serving outdated content. RAG suits scenarios requiring real-time accuracy like financial updates or unpredictable queries, while CAG excels at repetitive tasks like FAQ chatbots. Hybrid approaches combine both, using cached responses for speed while falling back to live retrieval when needed. The choice depends on query patterns, budget, performance requirements, and data freshness needs. 15 1 6. 6 [](/posts/adaptive-rag-explained-what-to-know-in-2025-uwxy45fzc "Adaptive RAG explained: What to know in 2025") Article Meilisearch · 1y Adaptive RAG explained: What to know in 2025 Adaptive RAG improves upon traditional Retrieval-Augmented Generation by dynamically deciding when and how to retrieve information based on query complexity. Unlike standard RAG that blindly retrieves documents for every query, adaptive RAG analyzes questions first, skips retrieval for simple prompts, and uses multiple search strategies for complex queries. The approach includes query classification, strategy selection, and iterative retrieval, offering benefits like improved accuracy, better efficiency, and reduced hallucinations. Common applications include customer support systems, conversational AI, and knowledge management platforms. 11 7. 7 [](/posts/what-is-agentic-rag-how-it-works-benefits-challenges-more-hjychu5aw "What is agentic RAG? How it works, benefits, challenges & more") Article Meilisearch · 1y What is agentic RAG? How it works, benefits, challenges & more Agentic RAG enhances traditional retrieval-augmented generation by adding agent-like decision-making capabilities. Instead of static retrieval, it uses planning, validation, and iterative evaluation to determine when and how to retrieve context. The system works through query understanding, retrieval planning, context evaluation, and feedback loops. Key benefits include improved accuracy, better reasoning, and explainability, while drawbacks involve higher computational costs and implementation complexity. Common tools include LangChain, LlamaIndex, and Meilisearch for building these intelligent pipelines. 10 [See all Meilisearch archives](/tags/meilisearch/best-of) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@graph":[{"@type":"CollectionPage","@id":"https://daily.dev/tags/meilisearch/best-of/2025#page","url":"https://daily.dev/tags/meilisearch/best-of/2025","name":"Best Meilisearch Posts of 2025","description":"The most upvoted Meilisearch posts of 2025, curated by the daily.dev community.","isPartOf":{"@type":"WebSite","url":"https://daily.dev"}},{"@type":"ItemList","@id":"https://daily.dev/tags/meilisearch/best-of/2025#items","numberOfItems":7,"itemListElement":[{"@type":"ListItem","position":1,"url":"https://daily.dev/posts/9-advanced-rag-techniques-to-know-how-to-implement-them-8orydew6h","name":"9 advanced RAG techniques to know & how to implement them"},{"@type":"ListItem","position":2,"url":"https://daily.dev/posts/meilisearch-1-13-7ni8qfv3a","name":"Meilisearch 1.13"},{"@type":"ListItem","position":3,"url":"https://daily.dev/posts/how-to-build-a-rag-pipeline-a-step-by-step-guide-d87dhlifl","name":"How to Build a RAG Pipeline: A Step-by-Step Guide"},{"@type":"ListItem","position":4,"url":"https://daily.dev/posts/introducing-meilisearch-chat-g4hpnbekd","name":"Introducing Meilisearch Chat"},{"@type":"ListItem","position":5,"url":"https://daily.dev/posts/rag-vs-cag-the-smarter-choice-for-your-ai-stack-vgmsfyzmg","name":"RAG vs. CAG: The Smarter Choice for Your AI Stack"},{"@type":"ListItem","position":6,"url":"https://daily.dev/posts/adaptive-rag-explained-what-to-know-in-2025-uwxy45fzc","name":"Adaptive RAG explained: What to know in 2025"},{"@type":"ListItem","position":7,"url":"https://daily.dev/posts/what-is-agentic-rag-how-it-works-benefits-challenges-more-hjychu5aw","name":"What is agentic RAG? 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