Lesson 958 of 2116
Tool-Calling Prompt Design: Function Calling and Disambiguation
When models call tools, the tool description is the contract. Sloppy descriptions mean the model picks the wrong tool, calls it incorrectly, or doesn't call it when it should. Here's how to write descriptions that get reliable invocation.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2Disambiguation Prompts for Tool-Calling Agents
- 3The premise
- 4Disambiguating tool arguments in prompts to reduce wrong calls
Concept cluster
Terms to connect while reading
Section 1
The premise
Tool descriptions are the contract between your app and the model; sloppy descriptions produce unreliable behavior at scale.
What AI does well here
- Write tool descriptions in second-person ('Use this when...', 'Do not use this for...')
- Specify both positive and negative use cases ('Use for X. Do NOT use for Y.')
- Document parameter constraints in the schema and reinforce them in the description
- Test tool selection against scenarios designed to be ambiguous
What AI cannot do
- Make every tool selection deterministic (some ambiguity always exists)
- Replace error handling for malformed tool calls
- Substitute for user-facing confirmation on consequential actions
Key terms in this lesson
Section 2
Disambiguation Prompts for Tool-Calling Agents
Section 3
The premise
Eager tool-callers cause expensive mistakes; disambiguation prompts cut error rate cheaply.
What AI does well here
- Have the model list candidate interpretations before acting.
- Ask a clarifying question when ambiguity is high.
- Confirm destructive actions verbatim.
What AI cannot do
- Eliminate over-eagerness without explicit instructions.
- Disambiguate without enough context to know what to ask.
Section 4
Disambiguating tool arguments in prompts to reduce wrong calls
Section 5
The premise
Most wrong tool calls come from two args the LLM cannot tell apart, not from a misread instruction.
What AI does well here
- Use distinct names: customer_id vs customer_email, not 'id1' / 'id2'
- Include a 1-line example value per argument
What AI cannot do
- Fix a tool whose semantics genuinely overlap
- Compensate for missing argument types
Section 6
AI Tool-Use Prompting: Steering Function Calling Behavior
Section 7
The premise
Tool-use prompting requires clear tool descriptions, explicit selection criteria, and guidance on when NOT to call tools — preventing both unnecessary calls and missed-opportunity skips.
What AI does well here
- Calling tools when descriptions clearly match the user request
- Extracting arguments from explicit user input
- Avoiding tool calls when the answer is obvious from context
- Combining results from multiple tool calls coherently
What AI cannot do
- Choose between near-duplicate tools without distinguishing descriptions
- Always avoid tool calls on questions it should refuse instead
End-of-lesson quiz
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