Intermediate11 min readdata-eval
Embeddings & RAG Explained (Plain English)
How AI tools search and retrieve information from documents. Understand embeddings and Retrieval-Augmented Generation without the math.
embeddingsRAGsearch
Data & Evaluation
How to work with data, embeddings, retrieval systems, and evaluation frameworks for AI. These guides cover everything from preparing training data and building vector search pipelines to measuring whether your AI actually gives good answers. You'll learn practical techniques for data quality, embedding models, RAG architectures, and systematic evaluation—so you can build AI systems that are accurate, trustworthy, and measurably improving over time.
Intermediate10 min readdata-eval
Evaluating AI Answers (Hallucinations, Checks, and Evidence)
How to spot when AI gets it wrong. Practical techniques to verify accuracy, detect hallucinations, and build trust in AI outputs.
evaluationhallucinationsaccuracy
Intermediate12 min readdata-eval
Retrieval 201: Chunking, Indexing, and Hybrid Search
Go beyond basic RAG. Advanced techniques for chunking documents, indexing strategies, re-ranking, and hybrid search.
RAGretrievalchunking
Intermediate9 min readdata-eval
Vector Database Examples: Real-World Use Cases and Code
Practical examples of vector databases in action: semantic search, chatbot memory, recommendation systems, and more with code snippets.
vector-databaseexamplescode
Intermediate11 min readdata-eval
Vector Databases 101: Storage, Indexing, and Search
Deep dive into vector databases. How they work, when to use them, and how to choose the right one for your needs.
vector-databaseembeddingssearch
Advanced14 min readdata-eval
Evaluations 201: Golden Sets, Rubrics, and Automated Eval
Build rigorous evaluation systems for AI. Create golden datasets, define rubrics, automate testing, and measure improvements.
evaluationtestingquality