Language Models

Coverage of large language models and NLP — retrieval-augmented generation, evaluation, prompting, agents, and the engineering behind language systems.

Abstract diagram of interconnected nodes and data flows representing a multi-agent orchestration system

Language Models June 26, 2026

Agentic System Failures Are Orchestration Problems, Not Model Problems

Multi-agent deployments fail mostly on orchestration, not weak models — race conditions, memory gaps, timeouts. Evaluation must look beyond task success.

Researcher at a desk reviewing evaluation score charts with mismatched model performance data

Language Models June 26, 2026

Benchmark Contamination Is Breaking Model Evaluation: What a Credible Pipeline Looks Like

Benchmark contamination makes MMLU and HumanEval unreliable for frontier models. What a credible, contamination-resistant evaluation pipeline requires.

Researcher reviewing benchmark score charts on dual monitors in a dim lab

Language Models June 10, 2024

Evaluating LLMs: What Benchmarks Measure and Where They Fail

A technical guide to LLM evaluation: what MMLU, HELM, GSM8K, and HumanEval actually measure, benchmark contamination, and how to build reliable task-specific …

Abstract glowing data window stretching across a dark grid of document fragments pulled into focus

Language Models April 25, 2024

Long-Context Retrieval: What Actually Works When the Context Window Isn't Enough

Why bigger context windows don't fix retrieval quality, what 'lost in the middle' means in practice, and when to use RAG, long context, or both.

Diagram of a retrieval augmented generation pipeline showing vector search and context assembly

Language Models January 22, 2024

Retrieval-Augmented Generation in Practice: Building RAG That Works

A technical guide to retrieval augmented generation: chunking, embeddings, vector search, reranking, failure modes, and how to evaluate your RAG pipeline.