Why My First ML Project Was a Wake-up Call
I remember it like it was yesterday, the time I decided to explore my first serious ML project. You know how you sometimes get way in over your head? Well, I was knee-deep in a quagmire. I had grand ideas of applying every pre-built tool and library under the sun. My confidence was sky-high, but reality soon slapped me hard. I spent more time reading documentation than actually writing code. I learned the hard way that distinguishing between function calling and tool use is essential if you want your project to succeed.
Function Calling: The Bread and Butter of Coding
Okay, so let’s start with function calling. You know the drill: write a function, define its inputs, code its logic, and call it when needed. This is the fundamental unit of work in programming. It embodies simplicity and clarity. While you don’t always need to reinvent the wheel, defining your own functions allows for better control and understanding of the process. I’ve been in situations where someone floods the codebase with third-party functions without completely understanding them. It leads to a lot of “black box” scenarios and annoying bugs. It’s like trusting someone else to write your story—you lose your voice.
Advantages of function calling:
- More control over your code.
- Reduces dependency on external libraries.
- Easier debugging and maintenance.
But let’s not pretend that function calling doesn’t have its pitfalls. If you overdo it or write sloppy functions, you could end up with a tangled mess of spaghetti code.
Tool Use: Convenience vs. Complexity
On the flip side, we have tool use. Software tools and libraries are built to make life easier, but sometimes they bring more baggage than benefits. Don’t get me wrong; tools are fantastic when you need to perform complex tasks without reinventing the wheel. But there’s a caveat. You become part of the tool’s ecosystem, which can be a blessing or a curse. I once worked with a tool that was supposed to “simplify” data processing. Ha. It did the opposite. The tool had hidden quirks and a steep learning curve. By the end, I was not just using the tool; I was fighting with it.
Advantages of tool use:
- Speeds up development process.
- Utilize pre-built, tested solutions.
- Focus on business logic rather than technical implementation.
But there’s always a trade-off. Relying too heavily on tools can lead to technical debt and make it impossible to switch gears if the tool lacks certain functionalities or becomes obsolete.
Finding the Right Balance
In our work as ML engineers, finding the right balance between function calling and tool use is crucial. It’s almost like a delicate dance. Too much of one can lead to chaos. But when you get it right, everything flows. Remember that time we tried to implement a new recommendation system? We’d decided to use pre-existing libraries for collaborative filtering but coded our own algorithms for content-based filtering. The result was a hybrid system that maximized efficiency without sacrificing control. We nailed it by asking ourselves: What do we need to own in this process, and what can we delegate?
Steps to achieve balance:
- Clearly define project requirements before choosing tools.
- Ask yourself if you understand the tool well enough to troubleshoot issues.
- Consider future scalability and adaptability when relying on external libraries.
FAQs: Clearing Up the Confusion
- Can I rely solely on tools for my project? You could, but it’s a risk. Tools are great, but dependency on them can lead to technical debt and limit flexibility.
- When should I write my own functions? Write your own functions when you need full control over the process or when existing solutions don’t fit your specific needs.
- How do I balance function calling and tool use? Evaluate your project’s requirements and constraints carefully. Opt for a mixed approach whenever feasible to maximize both efficiency and control.
The next time you’re staring at a new ML project, take a moment to think about function calls versus tool use. Your code quality and sanity might just depend on it.
Related: Mastering Agent Streaming Response Patterns · Navigating Agent Workflow Orchestration Patterns · Mastering Agent Tool Calling Patterns in ML Design
🕒 Last updated: · Originally published: February 5, 2026