A tutorial on using the Gemini API's File Search feature for RAG in web development with JavaScript/TypeScript.
Explains Retrieval-Augmented Generation (RAG), a pattern for improving LLM accuracy by augmenting prompts with retrieved context.
Introduces Graphiti, an open-source framework for building bi-temporal knowledge graphs to give AI agents long-term memory and real-time data understanding.
A hands-on guide for JavaScript developers to learn Generative AI and LLMs through interactive lessons, projects, and a companion app.
Explores AI agents, their core components, differences from LLMs, and real-world applications, positioning them as the future of autonomous AI systems.
Explores three key methods to enhance LLM performance: fine-tuning, prompt engineering, and RAG, detailing their use cases and trade-offs.
Explains a technique using AI-generated summaries of SQL queries to improve the accuracy of text-to-SQL systems with LLMs.
Explores AI engineering architecture patterns and user feedback methods, from simple APIs to complex agent-based systems.
Analysis of Chapter 6 from Chip Huyen's 'AI Engineering' book, focusing on RAG systems and AI agents, their architecture, costs, and relationship.
An overview of Azure AI Foundry, a unified platform for building and deploying AI solutions on Microsoft Azure, covering its features and benefits.
Explores Microsoft's Graph RAG, an advanced RAG technique using knowledge graphs to answer global questions about datasets, with a hands-on setup guide.
Explains the limitations of Large Language Models (LLMs) and introduces Retrieval Augmented Generation (RAG) as a solution for incorporating proprietary data.
Explains how to use Azure OpenAI with your own data via Semantic Kernel, focusing on RAG and Azure AI Search integration.
A technical guide on using Azure AI Language Studio to summarize and optimize grounding documents for improving RAG-based AI solutions.
A simple explanation of Retrieval-Augmented Generation (RAG), covering its core components: LLMs, context, and vector databases.
A summary of a keynote talk on essential building blocks for production LLM systems, covering evaluations, RAG, and guardrails.
A technical overview of Obsidian-Copilot, a prototype AI assistant for drafting and reflecting within the Obsidian note-taking app using retrieval-augmented generation.
Explains Retrieval Augmented Generation (RAG) for using ChatGPT with custom data, including a C# implementation sample.
Explains how retrieval-augmented language models like RETRO achieve GPT-3 performance with far fewer parameters by querying external knowledge.
