Retrieval-augmented generation (RAG)

Learn to build context-aware AI agents using watsonx Orchestrate and Astra db with vector search, embeddings, and automated data retrieval.

Elasticsearch has transformed from a simple search engine into a powerful AI-powered platform capable of handling diverse search requirements. By mastering indexing, analyzers and hybrid search techniques, developers can build sophisticated search experiences that combine the precision of keyword search with the contextual understanding of semantic search.

The Agentic RAG architecture represents a significant advancement over traditional RAG systems, particularly for advanced technical domains. With the use of specialized agents, hybrid data structures, and intelligent orchestration, we achieved 95% accuracy while significantly improving user experience and operational efficiency.

In this tutorial, build an intelligent documentation assistant that lets you chat with your project’s Markdown documentation (those .md files you see in GitHub, like READMEs). Using JavaScript, and a tool called LangChain, and a local AI system called Ollama, we’ll create a CLI that connects to your GitHub repository, pulls in your documentation, and answers your questions in plain language. It’s like having a super-seasoned teammate who knows every word of your project’s docs, akin to a pair programming buddy in your day to day workflows.

Learn how Quarkus combined with LangChain4j provides a seamless way to build AI-powered applications that start in milliseconds and consume minimal resources.

ColBERT (Contextualized Late Interaction over BERT) is a retrieval model that is designed to strike a balance between the efficiency of traditional methods like BM25 and the accuracy of deep learning models like BERT, an open source deep learning model used for natural language understanding. ColBERT uses the late interaction and MaxSim scoring method to rank the documents based on their relevance to the query. This scoring method furnishes better retrieval because of fine-grained matching and context awareness.

Boost mainframe skills with watsonx Assistant for Z. Use AI to simplify IBM Z operations, automate tasks, and speed up learning for developers and IT teams.

Discover the top 5 most popular blogs, articles, and tutorials for the first half of 2025 for one of the most popular programming languages for the generative AI space: Python.

This tutorial will show you an implementation of Agentic Retrieval-Augmented Generation (RAG). It can perform multi-step workflows like combining document search and web search to perform complex tasks like business research, feature comparison, news retrieval based on projects, personal knowledge management, and more.

Learn how to use HNSW for fast, scalable AI retrieval in RAG pipelines, boost search efficiency, optimize embeddings, and improve large-scale AI applications.