Vector Databases Explained for AI Agents
Learn how vector databases store embeddings, power semantic search, and support RAG and memory without replacing a knowledge base or relational database.
Topic
#knowledge-amp-rag
9 articles — updated daily
Reranking in RAG: Why Retrieval Quality Matters
Learn why RAG pipelines rerank retrieved candidates, how cross-encoders and other methods improve ordering, and what reranking cannot fix.
Hybrid Search vs Dense vs Sparse Retrieval
Compare sparse, dense, and hybrid retrieval by matching signal, strengths, failure modes, fusion methods, and the evidence needed to choose a RAG baseline.
Chunking Strategies for RAG
Compare fixed-size, recursive, semantic, and document-aware chunking for RAG, with practical guidance on chunk size, overlap, metadata, and evaluation.
Embeddings Explained for AI Agents
A beginner-friendly mental model of embeddings, vectors, similarity, and how AI agents use them for retrieval and memory without confusing similarity with truth.
RAG vs Agent Memory
A practical comparison of external knowledge retrieval and agent memory, including their overlap, different data lifecycles, and shared vector infrastructure.
RAG vs AI Agent: What’s the Difference?
A decision-focused comparison of RAG knowledge retrieval and AI-agent execution, including when a simple RAG pipeline is enough and when an agent is justified.
What Is RAG? Retrieval-Augmented Generation Explained
A practical introduction to retrieval-augmented generation, why external knowledge matters, and where RAG fits beside fine-tuning, memory, and AI agents.
How RAG Works: From Query to Retrieved Context
A step-by-step guide to the complete RAG pipeline, from document chunking and indexing through retrieval, reranking, context construction, and grounded generation.