0Fundamentals
13 LLM fundamentals for engineers
The minimum you need on tokens, context, tools, embeddings, and evals before you design any LLM system.
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- 13
- Fundamental
How to read this catalog
The five pillars, how pages connect, and where to start for your role.
- Fundamental
LLMs for engineers
The model as a probabilistic function: what goes in, what comes out, and what it never promises.
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Tokens and the context window
Why everything costs tokens, why the middle of the context fades, and how to measure it.
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Sampling and temperature
Where answer variation comes from and when you want more or less of it.
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The prompt as an interface
Treat the prompt like an API contract: input, output, version, and test.
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Tools and function calling
How the model asks your code to act and who decides what happens next.
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Embeddings
Text turned into vectors: what similarity captures and where it misleads.
- Fundamental
Chains, agents, and workflows
The difference between code choosing the path and the model choosing the path.
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Choosing a stack
The layers of an LLM system, and when LangChain, LangGraph, a provider SDK, or another library earns its place.
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What an eval is
Tests for systems that never answer the same way twice, from unit case to benchmark.
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Cost and latency
Do the math on every call before you ship.
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Prompt injection in practice
Why any text the model reads can become a command, and what to do about it.
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Observability for AI
Traces, spans, and logs that let you answer: why did the agent do that?