RAG & Retrieval

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

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Embeddings and Semantic Search

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

RAG: Grounding Models in Your Data

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

Choosing a Vector Database

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

Building a RAG Application

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

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

Continued Pretraining vs RAG

Continued pretraining bakes knowledge into model weights. RAG injects it at query time. When to use each, where each breaks down, and why you often need both.

8 min read

Context Engineering in Practice

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

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AI Agents

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

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