What Is an LLM?
Next-token prediction in plain language — and what an LLM is not.
Tokens, Context & Memory
How models read and remember text, and why long chats drift.
System, User & Assistant Roles
The anatomy of a conversation and why the system prompt is your best lever.
Sampling Controls: Temperature & Friends
Temperature, top-p and stop sequences — when to run hot vs cold.
Hallucinations & How to Reduce Them
Why models fabricate, the high-risk zones, and a concrete verification toolkit.
Embeddings & Vector Search
Meaning as a vector, and how semantic search works.
Retrieval-Augmented Generation (RAG)
Make any model answer about your data — and the failure modes to avoid.
Fine-tuning vs Prompting vs RAG
The decision framework people get wrong, with a try-in-this-order rule.
Evaluating AI Quality (Evals)
Build a golden set, pick metrics, and catch regressions before users do.
Privacy & Data Handling
What's safe to paste, training on your data, and when to run local.
Cost & Latency Tradeoffs
The cost/quality/speed triangle, model tiering, caching and batching.
Choosing a Model & Provider
A vendor-neutral way to pick a model — and read benchmarks skeptically.
Claude vs ChatGPT, Gemini & Copilot
An honest, evergreen way to compare the major assistants for your needs.
AI Media Generation (Images, Audio, Video)
Where image/audio/video generation fits — and where Claude does and doesn't.