Introducing tool use

Tool Use in LLMs (Claude)

**I. Core Concepts**

• Limitation of LLMs: By default, Claude only has access to information present in its training data. It generally lacks real-time or current event information (e. g. , current weather).
• Purpose of Tool Use: Tools enable Claude to access external, up-to-date information from the outside world to overcome its inherent knowledge limitations.

**II. The Tool Use Workflow (The Flow)** The process involves a multi-step interaction between the user, the LLM (Claude), and external server code:

• Initial Request: The user sends a question or task to Claude, along with instructions on how it can access external data.
• Decision Point: Claude analyzes the request and determines if external data is required to provide a complete answer.
• Data Request: If needed, Claude responds to the system, specifying exactly what information it requires.
• External Execution: Server-side code runs to fetch the requested data (e. g. , calling a third-party API).
• Follow-up Request: The fetched, real-time data is sent back to Claude in a follow-up request.
• Final Response: Claude generates a final, augmented response, combining the original prompt with the new, external data.

**III. Example: Current Weather**

• Goal: Answer a query like, "What is the weather in San Francisco right now? "
• Process:
• Initial query sent to Claude.
• Claude identifies the need for current weather data.
• Server code calls a weather API.
• Live weather data is returned to Claude.
• Claude generates the final, accurate weather report.

**IV. Implementation Complexity**

• **Conceptual vs. Code:** While the workflow is conceptually simple, the actual code implementation is complex.
• Implementation Steps: Developers often have to manage several distinct components, including:
• Writing the tool function.
• Defining the JSON schema specification.
• Handling the tool result.
• Ensuring the original JSON schema is included in the request.
• Challenge: These steps are often implemented non-linearly, leading to a complicated development process.

Takeaways

• LLMs like Claude are limited by their training data and require tools to access real-time or external information.
• Tool use involves a multi-step workflow where the LLM decides if external data is necessary before requesting it.
• External server code executes the request (e. g. , calling an API) and returns the fetched data to the LLM.
• The LLM generates a final, augmented response by integrating the external data with its original prompt.
• Implementation is complex, requiring developers to manage tool functions, JSON schemas, and result handling.