Tool Use

Extend your language model to call external tools—people, objects, or resources in the room—when it needs information beyond what’s in the grid.

You will need

• a completed model from an earlier lesson
• pen, paper, and dice as per Generation
• people or props to serve as “tools” (see examples below)

Your goal

Generate text where the model calls at least two different tools during generation. Stretch goal: design your own tool and integrate it into your model.

Key idea

Language models can recognise special tokens that trigger external actions. Instead of sampling the next word, the model pauses, calls a tool, and incorporates the result before continuing.

Setting up tools

Before generation, designate people or objects as tools. Each tool has:

• a trigger word that appears in your model’s vocabulary
• a capability (what it can do)
• a return format (what it gives back)

Example tools

Trigger word	Tool	Capability	Returns
VOTE	the room	ask a yes/no question	“yes” or “no” (majority)
POLL	show of hands	ask a multiple-choice question	the winning option
LOOKUP	someone with a phone	search for a fact	a short answer
CALCULATE	someone with a calculator	do arithmetic	a number
TIME	a clock or watch	check the current time	the time
COLOUR	a physical object	observe something	a colour word
ASK	a designated expert	answer a domain question	a short phrase

Algorithm

1. Add trigger words to your model’s vocabulary (new rows and columns).
2. Train or manually add counts so trigger words can appear in generation.
3. During generation, when you sample a trigger word:
   • pause generation
   • formulate a question or request based on context
   • the tool “executes” and returns a result
   • write down the result as the next word(s)
   • continue generation from there

Example session

Model trained on: “The answer is VOTE. We should CALCULATE the total.”

Generation with tools:

1. Start with “The”
2. Sample → “answer”
3. Sample → “is”
4. Sample → VOTE (trigger!)
   • Pause. Ask the room: “Should we continue?”
   • Room votes: majority says “yes”
   • Write down “yes”
5. Sample from “yes” row → “.”
6. Continue: “We” → “should” → CALCULATE (trigger!)
   • Pause. Ask: “What is 7 times 8?”
   • Calculator person says: “56”
   • Write down “56”
7. Sample from “56” row (if it exists) or treat as end

Generated text: “The answer is yes. We should 56”

(The grammar breaks down, but that’s fine—the point is demonstrating the mechanism.)

Designing good tool triggers

For the activity to work well:

• add trigger words to rows where they make sense contextually (e.g., VOTE after “is” or “the”)
• keep tool responses short (one or two words) so generation can continue
• have the tool operator ready before you start generating

Instructor notes

Discussion questions

• when should a model use a tool vs try to answer itself?
• what happens if a tool returns something unexpected?
• how does the model “know” to call a tool? (it doesn’t—it just samples the trigger word)
• what tools would be most useful for different kinds of text?
• could a tool’s response change what the model generates next?

Classroom variations

Simple version: use just one tool (VOTE) and have the whole class participate. The model generates until it hits VOTE, then you ask a question and count hands.

Advanced version: set up multiple tools around the room. Different students operate different tools. The generator doesn’t know which tool will be called next.

Adversarial version: the tool operators can return unhelpful or surprising answers. How does this affect the generated text?

Connection to current LLMs

Tool use (also called “function calling”) is how modern AI assistants perform actions in the world:

• mechanism: LLMs learn special tokens that signal “call this tool now”, exactly like your trigger words
• examples: web search, code execution, API calls, database queries, image generation
• agentic AI: systems that can plan and execute multi-step tasks by chaining tool calls
• JSON formatting: modern models output structured tool calls (function name, parameters) rather than just trigger words

The key insight: the model doesn’t “know” anything the tool returns—it just learns when to ask. Your classroom tools demonstrate this perfectly: the model samples VOTE not because it knows the answer, but because the training data included VOTE in that context. The actual knowledge comes from outside the model.

This is why tool-using AI can do things like search the web for current information, run calculations it couldn’t do in its head, or control robots and software. The model’s job is to know when to call a tool and how to use the result—not to contain all knowledge itself.