Vector Databases

Introduction Retrieval-Augmented Generation (RAG) has revolutionized how large language models (LLMs) handle knowledge-intensive tasks by combining retrieval from external data sources with generative capabilities. Unlike traditional LLMs limited to their training data, RAG pipelines enable models to access up-to-date, domain-specific information, reducing hallucinations and improving accuracy.[1][3][7] This blog post dives deep into RAG pipelines, exploring their architecture, components, implementation steps, best practices, and production challenges, complete with code examples and curated resource links. ...

Table of Contents Introduction What Are Vector Databases? Why Vector Databases Matter for LLMs Core Concepts: Embeddings, Similarity Search, and RAG Top Vector Databases Compared Getting Started: Installation and Setup Practical Python Examples Indexing Strategies Querying and Retrieval Performance and Scaling Considerations Best Practices for LLM Integration Conclusion Top 10 Learning Resources Introduction The explosion of large language models (LLMs) has fundamentally changed how we build intelligent applications. However, LLMs have a critical limitation: they operate on fixed training data and lack real-time access to external information. This is where vector databases enter the picture. ...

Why Most RAG Systems Fail Most Retrieval-Augmented Generation (RAG) systems do not fail because of the LLM. They fail because of bad chunking. If your retrieval results feel: Random Hallucinated Incomplete Loosely related to the query Then your embedding model and vector database are probably fine. Your chunking strategy is the real bottleneck. Chunking determines what the model is allowed to know. If the chunks are wrong, retrieval quality collapses — no matter how good the LLM is. ...

By 2025, Large Language Models (LLMs) have evolved from isolated text-generation systems into general-purpose reasoning engines embedded deeply into modern software systems. This evolution has been driven by: Agentic workflows Retrieval-augmented generation Standardized tool interfaces Long-context reasoning Stronger evaluation and observability layers This article provides a system-level overview of the most important LLM tools and concepts shaping 2025, with direct links to specifications, repositories, and primary sources. 1. Frontier Language Models & Architectural Shifts 1.1 Frontier Closed-Source Models Closed-source models lead in reasoning depth, multimodality, and safety research. ...

Introduction Retrieval-Augmented Generation (RAG) has become a foundational pattern for building accurate, scalable, and fact-grounded applications with large language models (LLMs). At its core, RAG combines a retrieval component (to fetch relevant pieces of knowledge) with a generation component (the LLM) that produces answers conditioned on that retrieved context. This article breaks RAG down from first principles: the indexing and retrieval stages, the augmentation of prompts, the generation step, common challenges, practical mitigations, and code examples to get you started. ...