Guide to LLM resources
Understand each resource type: what it is for, when to use it and how.
Production — run the model in production, without changing it
A context is a set of instructions and examples given to the AI model before the user's question.
A prompt is a reusable text template with variables in double braces, for example {{product}}.
An agent is an AI assistant that chains steps to complete a task: it follows instructions, uses MCP servers and contexts, and respects guardrails..
A RAG (retrieval-augmented generation) pipeline lets a model answer from your documents: they are chunked and embedded, then for each question the useful excerpts are retrieved and given to the model..
Training — change the model by training it
A dataset is a set of examples in JSONL format (one JSON line per example) used to train or evaluate a model..
LoRA adapts an existing model to a task without fully retraining it: only small adapters are trained.
An evaluation is a set of test cases (input and expected answer) with a scoring method.
Extensions — plug tools into the model
An MCP (Model Context Protocol) server gives an AI assistant new abilities: read files, query a database, call a service.
A skill is an instruction pack (SKILL.md file) that an assistant like Claude loads only when the task calls for it.
A harness is the environment that runs an agent: its instructions file (e.g.