Writing on AI engineering

RAG That Thinks

Iterative, adaptive, FLARE, Self-RAG, corrective, speculative, and agentic RAG, each with its own analogy and its own visualizer.

The control-flow axis of RAG, pattern by pattern, each with an everyday analogy and an interactive visualizer: iterative and multi-hop RAG, adaptive RAG, FLARE, Self-RAG, corrective RAG (CRAG), speculative RAG, and agentic RAG. How each adds a loop, and then adds judgment to the loop, and how to stop at the least autonomous pattern that answers your questions.

• RAG

How RAG Reads a Corpus

Fixed-size, structure-aware, sentence-window, parent-document, summary indexing, RAPTOR, and late chunking, each with its own analogy and visualizer.

The indexing axis of RAG: why chunking is the quiet make-or-break decision, and the ladder of strategies, each with an everyday analogy and interactive visualizer: fixed-size chunks, structure-aware splitting, sentence-window retrieval, parent-document (small-to-big), summary indexing, RAPTOR's hierarchical tree, and contextual and late chunking. How to decouple the unit you search from the unit you read.

• RAG

How RAG Finds Things

Sparse, dense, hybrid, learned-sparse, late interaction, re-ranking, and contextual retrieval, each with its own analogy and its own visualizer.

The retrieval axis of RAG, method by method: sparse keyword search (BM25), dense embeddings, learned-sparse (SPLADE), late interaction (ColBERT), hybrid fusion with RRF, cross-encoder re-ranking, and contextual retrieval. Each gets an everyday analogy, the real definition, and its own interactive visualizer. How each decides what counts as a match, a worked reciprocal-rank-fusion example, and why real systems combine several.

• RAG

A Field Guide to RAG

RAG is not one technique. It is a design space, and this is the map of it.

Retrieval-augmented generation started as a single idea and turned into a whole family of designs. This is the opening map: naive RAG, the advanced pre- and post-retrieval tricks, the modular mindset, and the five axes every variant moves along. It anchors a five-part series that walks every kind of RAG.

• RAG

Learn Prompt Engineering in 10 Minutes

A working engineer's primer on the prompt patterns that actually move accuracy in production LLM systems: system-prompt structure, few-shot, chain-of-thought, structured output, and the anti-patterns that quietly hurt.

• Loop Engineering

RMSE: What Does It Mean?

A short, intuitive take on Root Mean Squared Error, why squaring makes it punish large misses harder than small ones, and how to read it when judging a regression model.