Learning
Deep-dive writeups from an ongoing retrieval-research project — why an LLM agent misses the right doc, how far you get without vectors, and whether a small fine-tuned retriever beats it. Each one ends with an optional multiple-choice quiz on the core concepts.
The Retrieval Pipeline, Line by Line
Three files that turn bi-encoder architecture diagrams into something that actually runs: synthetic training pairs, contrastive training, and brute-force cosine search — with the real code for each.
The Best Wrong Answer
A hard negative is a document that looks right and isn't — and it matters more to a contrastive loss than almost anything else in the batch. The math for why, worked through step by step.
Digging for Hard Negatives
The code that mines real hard negatives per base model, a real CUDA out-of-memory crash mid-sweep, and a 12-run sweep showing it only paid off for one of three models.