What the model knows, and what it needs

A confident answer is not a receipt

Imagine the assistant says, “Delivery is $2 and the restaurant is open.” Before trusting it, ask: where did those facts come from? If neither you nor a connected source supplied them, the response has not established they are current.

A large language model (LLM) learns patterns during training and generates responses from its available context. It can interpret “Thai would be nice” as a preference. That capability does not itself provide current restaurant availability.

Keep three things separate: instructions describe the task, user input describes the request, and external evidence supplies facts such as a current menu.

Check your understanding

Tokens are pieces; context is the working space

Models process text as tokens, which may be whole words, parts of words, or punctuation. A token is not always a word. The context window limits the information processed in a request; the exact accounting depends on the model.

Think of context as the material available on a desk. The current request, relevant prior messages, instructions, and tool results may all occupy space. A long conversation does not guarantee that every earlier message remains available.

When shortening a dinner conversation, preserve active requirements and corrections. Keep “vegetarian” and “$25 including fees”; remove irrelevant small talk first. Summaries can also make mistakes, so inspect them. See Anthropic’s context-window documentation for a provider example.

Check your understanding

Which information should survive when you shorten the conversation?

Tools provide a connection to the outside

An API is a defined way for software to request information or an action from other software. A tool makes a capability available to an assistant. The application must execute and check a tool request; naming a tool in a prompt does not connect it.

A future menu tool could supply prices and timestamps. For this module, label all restaurant examples as fictional. Do not claim that a working DoorDash integration exists.

This is a workflow: the application follows a defined sequence. We can add agent-directed choices when the problem needs them. This distinction follows Anthropic’s foundational workflow guidance.

Check your understanding

Match the information to its appropriate source.

Key takeaways

• Model confidence does not establish that a fact was verified.
• Tokens are processing units, not a fixed synonym for words.
• Preserve current requirements when managing limited context.
• Tools need actual application connections and checked results.