Fundamentals
How LLMs work
Text in, probabilities out, repeat in a loop.
Tokens & tokenization
Subword chunks of input and output.
Context windows
The model’s working memory and hard limits.
Inference parameters & sampling
How to produce predictable or creative outputs.
Context engineering
Structuring prompts for optimal results.
Inference metrics
TTFT and TPS, how fast an LLM feels.
Core capabilities
Function calling & tool use
Model plans, your code runs the tool.
Structured outputs & JSON mode
Force schema-valid output via constrained decoding.
Customization & deployment
Fine-tune vs. prompt
Hone a model for a particular task.
Choosing a deployment option
Serverless, dedicated endpoints, or containers.
Quantization
The JPEG quality slider for a model.