Why Your Retriever is Failing and How Context Can Save It Imagine asking “I want to buy apple” – do you mean Apple Inc. stock, the latest iPhone, or simply fruit? Without context, your retriever may serve you the wrong results. 1. What Is the Problem in Your Retriever & Embedding? Modern retrievers map queries and documents into high-dimensional vectors (embeddings) and rank by cosine similarity. But when a query is ambiguous, plain embeddings struggle:...
Improving Retrieval in RAG (via Recall, Precision, and NDCG) Introduction Retrieval-Augmented Generation (RAG) is the superhero sidekick that grounds your Large Language Model (LLM) in cold, hard facts. But here’s the dirty secret: if your retrieval sucks, your RAG system is just a fancy chatbot with a broken brain. Weak retrieval = missed documents, irrelevant results, and rankings that make no sense. This guide cuts through the noise. You’ll learn how to turbocharge your RAG retrieval with a no-fluff, step-by-step approach to maximize recall, sharpen precision, and nail NDCG....
Amazon Bedrock is a fully managed service that makes high-performing foundation models (FMs) from leading AI startups and Amazon available for your use through a unified API. You can choose from a wide range of foundation models to find the model that is best suited for your use case. Amazon Bedrock also offers a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI. With Amazon Bedrock, you can easily experiment with and evaluate top foundation models for your use cases, privately customize them with your data using techniques such as fine-tuning and Retrieval Augmented Generation (RAG), and build agents that execute tasks using your enterprise systems and data sources....
Essential Fields Be it relational or not, every table should have these 5 fields: created_at (default now()) updated_at (default now()) deleted_at (default null) created_by (not null) updated_by (not null) Just to be clear, every table should have these 5 fields and not must. Adding these fields have other side-effects such as bloat, performance and disk size. But, if you’re having these problems, i hope you’re profitable. Why should you include this fields ?...
The common optimization trick for speeding up transformer inference is KV caching 1 2. This technique is so prominent that huggingface library has use_cache flag is enabled by default 6. A few days ago, I read an awesome blog post on GPT in 60 Lines of NumPy. So, i thought, why not extend it to use the KV cache technique? So, let’s roll up our sleeves and start working on it....