Tokens, embeddings, attention and next-token prediction — the mechanics that explain almost every LLM behaviour.
Python
Claude
OpenAI
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Every lesson in LLMs, Prompt Engineering & Context EngineeringYour prompt is cut into tokens, turned into vectors, and then every token decides which other tokens matter. Those three steps explain why good prompts work.
Temperature, top-p, and why the same question gets different answers.
Counting tokens, estimating cost, and what happens when the context fills up.
Quality, latency, price and context limits — how to weigh them for a real product.
Clear instructions, examples, step-by-step reasoning and structured output — proven techniques, no incantations.
What goes into the context window, where it comes from and in what order — the core skill of building LLM applications.
Calling APIs, streaming, tool calling, caching, and keeping cost and latency under control in production.
Eval sets, LLM-as-judge and regression tests for prompts — replacing "looks good to me" with a number.
