
A framework for building RAG pipelines that introduces complexity in response to observed failure modes, from lexical and hybrid search to reranking and agentic information seeking

A framework for building RAG pipelines that introduces complexity in response to observed failure modes, from lexical and hybrid search to reranking and agentic information seeking

Quick and simple tips to help you write better agent instructions

How to apply the latest context engineering guidelines to your day-to-day data science work

As AI handles more of the execution, what work should belong to agents vs humans and why does that distinction matter?

A practical guide to getting better code, not just more code

Why most agents are just flowcharts in disguise, and what to build instead.

Watermarks act at the model’s moments of doubt, and so do the safety checks that catch AI mistakes

How DFlash trades spare compute for saved memory bandwidth, and why its gains shrink as concurrency rises

LoRA fine-tuning solved our under-labeling problem. Whether it makes sense for you depends on three questions.

A walkthrough of and the maths behind using low-capacity networks to acquire fine-grained scoring when only categorical labelling is available for training