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How to Build - AI Math Assistant with LangChain Tool Calling

BeginnerGuided Project

Tired of AI getting math wrong? Frustrated by bizarre answers to simple questions like “What’s 1+1?” Take control with LangChain’s tool calling! In this hands-on project, you’ll build a custom AI math assistant that performs precise calculations—no more hallucinations. Learn to create and integrate tools for addition, subtraction, multiplication, and division, ensuring accuracy every time. With error handling, input validation, and testing, you’ll make AI truly reliable for real math. Perfect for developers looking to bridge AI with logic seamlessly!

Language

  • English

Topic

  • Artificial Intelligence

Skills You Will Learn

  • LLM, Tool Calling, Function Calling, AI Agent, Generative AI, LangChain

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 45 minutes

Platform

  • SkillsNetwork

Last Update

  • July 8, 2025
About this Guided Project

Can AI Really Do Math? 🤔 Let’s Fix That!


You’re chatting with an AI assistant and ask, “What’s 1 + 1?”. Simple, right? But instead of a straightforward answer, the AI hesitates—or worse, confidently gives you the wrong one. Why?

LLMs (Large Language Models) are not calculators—they predict answers based on text patterns rather than performing actual computations. This leads to hallucinations, where AI generates incorrect but convincing responses.


Enter Tool Calling: Giving AI Real Skills


Tool calling is a powerful feature in LangChain that allows AI models to use external tools instead of guessing. When faced with a math question, an AI agent can recognize the need for a precise answer and call a dedicated math function—just like a person reaching for a calculator instead of estimating.


What You’ll Build


In this guided project, you’ll create a custom mathematical toolkit using LangChain’s tool calling capabilities. Your AI agent will:

✅ Perform real calculations (addition, subtraction, multiplication, division)
✅ Dynamically select the right tool based on user queries
✅ Ensure accuracy with error handling and input validation
✅ Seamlessly integrate multiple tools using LangChain’s Tool class

By the end, you'll have an AI-powered assistant that doesn’t just predict answers—it computes them accurately!

Why This Matters


From AI tutoring bots to finance automation, many real-world applications require precise calculations. Tool calling ensures AI relies on real computations, making it more reliable in critical tasks.


What You’ll Learn


🔹 The fundamentals of LangChain’s tool calling and why it’s crucial for AI agents
🔹 How to design and implement custom tool functions for numerical operations
🔹 Techniques for error handling, input validation, and testing
🔹 How AI agents can orchestrate multiple tools for different tasks

What You’ll Need


✅ Basic Python knowledge
✅ Interest in AI tool integration
✅ A web browser to access the IBM Skills Network Labs environment


🚀 Let’s build an AI that actually does math—no more guessing!

Instructors

Joseph Santarcangelo

Senior Data Scientist at IBM

Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

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Karan Goswami

Data Scientist

I am a dedicated Data Scientist and an AI enthusiast, currently working at IBM's Skills Builder Network. Learning how some simple mathematical operations could be used to make predictions and discover patterns sparked my curiosity, leading me to explore the exciting world of AI. Over the years, I’ve gained hands-on experience in building scalable AI solutions, fine-tuning models, and extracting meaningful insights from complex datasets. I'm driven by a desire to apply these skills to solve real-world problems and make a meaningful impact through AI.

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Kunal Makwana

Data Scientist

I’m a passionate Data Scientist and AI enthusiast, currently working at IBM on innovative projects in Generative AI and machine learning. My journey began with a deep interest in mathematics and coding, which inspired me to explore how data can solve real-world problems. Over the years, I’ve gained hands-on experience in building scalable AI solutions, fine-tuning models, and leveraging cloud technologies to extract meaningful insights from complex datasets.

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