RAG
Retrieval pipelines: chunking, embeddings, vector search and reranking.
9 posts
Embeddings in machine learning: what they are, how they work, and when to use them
Learn what an embedding is, how embedding models work, and how to use embeddings for semantic search, RAG pipelines, and AI agent memory in production.
Jun 22, 2026ArticleAgentic RAG: how it works, core architectures, and production tradeoffs
Learn how agentic RAG improves on traditional retrieval with multi-hop reasoning, self-correction, and production-ready orchestration in TypeScript.
Jun 22, 2026ArticleRAG chatbot: a complete guide for TypeScript developers
Learn how to build a RAG chatbot in TypeScript, from embeddings and chunking to retrieval, evals, and deployment with Mastra.
Jun 22, 2026ArticleRAG framework guide: how retrieval-augmented generation works and which tools to use
Learn how a RAG framework connects LLMs to external data. Covers RAG architecture, tools like LangChain and LlamaIndex, and how to build a pipeline.
Jun 22, 2026ArticleRAG platforms: what they are, how they work, and how to choose one
Learn what a RAG platform does, how RAG pipelines work end to end, and what to evaluate when choosing one for production AI agents.
May 26, 2026BlogBuild a RAG agent with Mastra and Elasticsearch
How Elastic's reference build wires Mastra's agent, tool, and memory primitives onto Elasticsearch's vector store to answer questions over a corpus of 500 sci-fi movies.
Jan 1, 2026BlogBuilding a research paper assistant with RAG
Build an AI research assistant that analyzes academic papers and answers questions using retrieval-augmented generation.
Apr 11, 2025BlogPDF-to-Blog: Giving documents a second life with Mastra and Mistral OCR
A technical guide showing how to build a PDF-to-blog converter using Mastra and Mistral OCR
Jan 16, 2025BlogRAG tutorial: Setting up a RAG pipeline
A guide to setting up a simple RAG pipeline with Mastra
