RAG & Retrieval

Build retrieval-augmented generation systems with embeddings, vector databases, and context engineering.

6 articles0 of 6 completed

What Are Embeddings in AI? A Technical Explanation

Embeddings turn text into numbers that capture meaning. Here's how they work, why they matter for search and RAG, and how to choose the right model for your use case.

7 min read

What Is RAG? Retrieval-Augmented Generation Explained

RAG lets AI models pull in real data before generating a response. Here's how retrieval-augmented generation works, why it matters, and where it breaks down.

7 min read

How to Choose a Vector Database in 2026

Pinecone, Weaviate, Qdrant, pgvector, or Chroma? Here's how to pick the right vector database for your AI application based on scale, infrastructure, and actual needs.

7 min read

How to Build a RAG Application (Step by Step)

A practical walkthrough of building a RAG pipeline from scratch: chunking documents, generating embeddings, storing vectors, retrieving context, and generating grounded answers.

8 min read

Fine-Tuning vs RAG: When to Use Each Approach

RAG changes what the model knows. Fine-tuning changes how it behaves. Here's when to use each approach, their real tradeoffs, and why the answer is usually both.

8 min read

Context Engineering: The Most Important AI Skill in 2026

Context engineering is replacing prompt engineering as the critical AI skill. Learn what it is, why it matters more than prompting, and how to manage state, memory, and information flow in AI systems.

6 min read

Up next

AI Agents

Build agents with tool use, memory, multi-agent orchestration, and evaluation frameworks.

→

Get Insanely Good at AI

Chapter 5: Building With AIcovers embeddings, RAG, vector databases, and the architecture patterns behind real AI products. The building blocks without the jargon.

Get the Book