Introduction: Retrieval Augmented Generation (RAG) grounds LLM responses in your actual data, reducing hallucinations and enabling knowledge that wasn’t in the training set. But naive RAG—embed documents, retrieve top-k, stuff into prompt—often disappoints. Retrieval misses relevant documents, context windows overflow, and the model ignores important information buried in long contexts. This guide covers advanced RAG […]
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Retrieval Augmented Fine-Tuning (RAFT): Training LLMs to Excel at RAG Tasks
Introduction: Retrieval Augmented Fine-Tuning (RAFT) represents a powerful approach to improving LLM performance on domain-specific tasks by combining the benefits of fine-tuning with retrieval-augmented generation. Traditional RAG systems retrieve relevant documents at inference time and include them in the prompt, but the base model wasn’t trained to effectively use retrieved context. RAFT addresses this by […]
Read more →RAG Patterns: Advanced Retrieval Augmented Generation Strategies
Introduction: Retrieval Augmented Generation (RAG) has become the standard pattern for grounding LLM responses in factual, up-to-date information. But basic RAG—retrieve chunks, stuff into prompt, generate—often falls short in production. Queries get misunderstood, irrelevant chunks pollute context, and answers lack coherence. This guide covers advanced RAG patterns that address these challenges: query transformation to improve […]
Read more →Google to begin offering Cloud storage
In the “coming weeks” you will find that your Google Docs account will allow you to upload any kind of file for online storage. Google Docs will support uploading any kind of file as long as it is under 250MB in size. You will then be able to store your videos, raw images, zip files […]
Read more →Memory Systems for LLMs: Buffers, Summaries, and Vector Storage
Introduction: LLMs have no inherent memory—each request starts fresh. Building effective memory systems enables conversations that span sessions, personalization based on user history, and agents that learn from past interactions. Memory architectures range from simple conversation buffers to sophisticated vector-based long-term storage with semantic retrieval. This guide covers practical memory patterns: conversation buffers, sliding windows, […]
Read more →What Is Retrieval-Augmented Generation (RAG)?
Introduction Welcome to a fascinating journey into the world of AI innovation! Today, we delve into the realm of Retrieval-Augmented Generation (RAG) – a cutting-edge technique revolutionizing the way AI systems interact with external knowledge. Imagine a world where artificial intelligence not only generates text but also taps into vast repositories of information to deliver […]
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