The article discusses the evolution from RAG (Retrieval-Augmented Generation) to 'context engineering' in the field of AI, particularly with the rise of agents. It explores how companies like Contextual AI are building platforms to manage context for AI agents and highlights the shift from prompt engineering to managing the entire context state.
Logs, metrics, and traces aren't enough. AI apps require visibility into prompts and completions to track everything from security risks to hallucinations.
An AI-powered document search agent that explores files like a human would — scanning, reasoning, and following cross-references. Unlike traditional RAG systems that rely on pre-computed embeddings, this agent dynamically navigates documents to find answers.
FailSafe is an open-source, modular framework designed to automate the verification of textual claims. It employs a multi-stage pipeline that integrates Large Language Models (LLMs) with retrieval-augmented generation (RAG) techniques.
This article details how to build a 100% local MCP (Model Context Protocol) client using LlamaIndex, Ollama, and LightningAI. It provides a code walkthrough and explanation of the process, including setting up an SQLite MCP server and a locally served LLM.
An extensible Model Context Protocol (MCP) server that provides intelligent semantic code search for AI assistants. Built with local AI models using Matryoshka Representation Learning (MRL) for flexible embedding dimensions.
A curated repository of AI-powered applications and agentic systems showcasing practical use cases of Large Language Models (LLMs) from providers like Google, Anthropic, OpenAI, and self-hosted open-source models.
Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents.
This article explores different chunking strategies for Retrieval-Augmented Generation (RAG) systems, comparing nine approaches using the agenticmemory library to improve retrieval accuracy and reduce hallucinations.
A comprehensive overview of the current state of Multi-Concept Prompting (MCP), including advancements, challenges, and future directions.
