Artificial IntelligenceIT automationQuantumBryan ClarkBJ HargraveErika Russiretrieval augmented generationGranite® 4.0quantum computingquantum computersLangChainlarge language models (LLMs)fine-tuningautonomous agentsvectorLangGraphframeworkembeddingsIBM Cloud® accountdownloadingthis recipeGitHubwatsonx.ai Runtime(API) keywatsonx.aiHugging Facethis embeddings model recipethis vector store recipefoundation modelswatsonx.aiLLM component recipeIBM paperLangSmithhereTutorial
IBM Developer: RAG tutorials
Explore all IBM Developer retrieval augmented generation (RAG) tutorials.
Start learningTechsplainers Podcast
Retrieval augmented generation (RAG) explained
Techsplainers by IBM breaks down the essentials of RAG, from key concepts to real‑world use cases. Clear, quick episodes help you learn the fundamentals fast.
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IBM RAG Cookbook
Explore a comprehensive collection of best practices, considerations and tips for building RAG solutions tailored to business applications.
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IBM Developer: RAG articles
Explore all IBM Developer retrieval augmented generation (RAG) articles.
Get startedArchitecture
Retrieval augmented generation (RAG) architecture
Discover proven architecture patterns that accelerate the creation of technology solutions to meet your business challenges.
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Build a RAG agent to answer complex questions
Use Python, LangGraph, watsonx.ai®, Elasticsearch and Tavily to build a customized, modular agentic AI system.
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Quick start: Prompt a foundation model with the retrieval-augmented generation pattern
Learn how to use foundation models in IBM watsonx.ai to generate factually accurate output grounded in information in a knowledge base by applying the retrieval augmented generation pattern.
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