Standard RAG systems retrieve text chunks that are semantically similar to a query. This works well when the answer is contained in a single passage. It breaks down for questions that require conne...

Evaluating LLM outputs at scale is one of the hardest unsolved problems in production AI. Human evaluation is the gold standard but it is slow, expensive, and does not scale. Automated metrics like...

Free-text LLM output is hard to integrate into production systems. Parsing prose, extracting fields, and handling unexpected formats adds fragility to every downstream component. Structured output ...

Mixture of Experts (MoE) is one of the most important architectural ideas behind the largest and most efficient modern language models. It enables scaling model capacity without proportionally incr...

Retrieval-Augmented Generation (RAG) is often described as “connect your LLM to your data.” That description understates the engineering problem. A production RAG system involves at least six disti...

One of the biggest gaps in LLM education is that most content explains individual concepts in isolation. Prompting is explained in one place, retrieval in another, evaluation in another, and observ...

A serious understanding of LLM systems eventually runs into infrastructure reality. Large models are not only machine learning artifacts. They are distributed systems. Training and serving them at ...

A model that only generates text can answer questions. A model that can safely invoke tools can take actions — query databases, call APIs, run code, trigger workflows. This is the architectural shi...

Many discussions about LLM performance focus on architecture and model size. That is understandable, but incomplete. In practice, one of the strongest determinants of model quality is the data pipe...

Many engineers work with LLMs for months before they develop a precise intuition for three mechanisms that dominate model behavior in practice: tokenization, decoding, and context construction. The...