Data & Evaluation
How to work with data, embeddings, retrieval, and evaluate AI outputs. Build systems that are accurate and trustworthy.
Embeddings & RAG Explained (Plain English)
IntermediateHow AI tools search and retrieve information from documents. Understand embeddings and Retrieval-Augmented Generation without the math.
Evaluating AI Answers (Hallucinations, Checks, and Evidence)
IntermediateHow to spot when AI gets it wrong. Practical techniques to verify accuracy, detect hallucinations, and build trust in AI outputs.
Retrieval 201: Chunking, Indexing, and Hybrid Search
IntermediateGo beyond basic RAG. Advanced techniques for chunking documents, indexing strategies, re-ranking, and hybrid search.
Vector Database Examples: Real-World Use Cases and Code
IntermediatePractical examples of vector databases in action: semantic search, chatbot memory, recommendation systems, and more with code snippets.
Vector Databases 101: Storage, Indexing, and Search
IntermediateDeep dive into vector databases. How they work, when to use them, and how to choose the right one for your needs.
Evaluations 201: Golden Sets, Rubrics, and Automated Eval
AdvancedBuild rigorous evaluation systems for AI. Create golden datasets, define rubrics, automate testing, and measure improvements.