The lab notebook
Field notes on AI engineering
824 articles · page 28 of 28
Temperature and Top-P Explained: Controlling LLM Randomness
A practical guide to temperature and top-p sampling, the two knobs that control how random or focused your large language model outputs are.
Tokens Explained: What LLMs Actually See When You Type
A hands-on guide to tokens, the real units LLMs read, with tokenizer code, worked examples, and why they shape cost, speed, and behavior.
What Is a System Card and Why Should You Read One?
A plain-English guide to system cards, what they document about AI models, and why reading them makes you a sharper, safer engineer.
What Is a Vector Database and Why Does RAG Need One?
A plain-English guide to vector databases, embeddings, and why retrieval-augmented generation depends on them to give language models real knowledge.
What Is an AI Moat? Building Defensibility Around LLM Products
An honest breakdown of what an AI moat really is, why most LLM wrappers have none, and how to build durable defensibility around AI products.
What Is an AI Pipeline? Breaking Down Multi-Step LLM Apps
Learn what an AI pipeline is, why single LLM calls fall short, and how to chain multi-step stages into reliable production apps.
What Is Constitutional AI? Anthropic's Approach to Alignment
A clear guide to Constitutional AI, Anthropic's alignment method that uses written principles, self-critique, and RLAIF to train safer models.
What Is Model Context and Why Context Engineering Is a Real Discipline
Learn what model context really is, why context engineering beats prompt engineering, and how to design the information window that drives every LLM output.
What Is Model Distillation? Smaller Models, Similar Performance
Model distillation trains a small student model to mimic a larger teacher, cutting cost and latency while keeping most of the accuracy.
What Is Retrieval-Augmented Generation? A Plain-English Explainer
A plain-English guide to Retrieval-Augmented Generation (RAG): what it is, how it works, and why it makes AI answers accurate.
What Is Speculative Decoding and Why Does It Speed Up LLMs?
Speculative decoding uses a small draft model to guess tokens a big model then verifies in parallel, cutting latency without changing output quality.
Zero-Shot vs Few-Shot Prompting: When to Use Each
A practical guide to choosing between zero-shot and few-shot prompting, with concrete examples, trade-offs, and a decision framework for real projects.
Structured Outputs Explained: Getting Reliable JSON from LLMs
Learn how structured outputs force LLMs to return valid, schema-conformant JSON every time, and how to wire them into production apps.
What Is an AI Model Router and When Do You Need One?
An AI model router picks the right LLM for each request so you cut cost and latency without hurting quality. Here is how it works and when to build one.