RAG-as-a-Service: what it is, use cases, providers & more
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.
Maya Shin
Tutorials, product updates, and insights from the Meilisearch team
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.
The company uses Meilisearch to deliver fast, faceted inventory search across multi-location dealer marketplaces - without fighting the tool.
Learn what self-RAG is, how it works, and why self-reflective retrieval-augmented generation reduces hallucinations and improves reliability in LLM systems.
The good, the bad, and the leaky: jemalloc, bumpalo, and mimalloc in Meilisearch
Where Meilisearch is heading next: serverless indexes, an AI gateway with our own models, a richer Cloud dashboard, and a more capable chat engine — all converging into one information retrieval platform.
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.
How this global electronics manufacturer elevates product discovery with faster, more relevant search at scale
We patched LMDB to support nested read transactions on uncommitted writes - eliminating full database scans and making Meilisearch's vector store 3× faster
Find out what Search-as-a-Service is, how it works, key pros and cons, top providers, how to choose the right one, and more.
Learn what an index file is, how it works, where it’s used, and why it’s essential for fast data retrieval in systems like databases and search engines.
Learn what specialty search engines are, how they work, and why they offer more relevant, focused results for specific industries, tasks, and user needs.