Why Function-Calling GenAI Must Be Built by AI, Not Manually Coded

Introduction

The rise of large language models (LLMs) has opened the door to function-calling systems where user prompts can trigger specific actions — like retrieving data, processing tasks, or interacting with APIs — using natural language. This is a significant leap forward in automating complex operations. However, building and maintaining these systems is not as straightforward as traditional programming due to the unpredictable nature of LLMs and the vast complexity inherent in function-calling applications. As a result, it is essential to understand why such systems must be built dynamically by AI rather than through manual coding efforts.

Several key reasons underpin this argument:

1. Function-calling applications are nonlinear systems.
2. The range of possible inputs is effectively infinite.
3. Updates to LLMs or switching models require rebuilding the function-calling application.

Each of these challenges drives the need for a dynamic, AI-driven approach to creating and maintaining function-calling systems.

The Nature of Function-Calling: Nonlinear Complexity

Building a function-calling system has two distinct phases. The first phase closely mirrors traditional programming tasks: analyzing sample prompts, designing functions, learning about the target APIs, and implementing the necessary function calls. While this phase is complex, it is manageable through familiar methodologies.

The second phase, however, is where things get unpredictable. In function-calling systems powered by LLMs, small variations in user input can result in drastically different outputs. This nonlinearity means that even after designing and implementing a set of functions, the system’s performance can vary significantly based on how the LLM interprets user requests. Unlike traditional systems, where developers can anticipate the outcomes of specific inputs, LLM-based systems require trial and error to see what function/parameter names and descriptions work best. This iterative experimentation introduces a kind of fuzzy logic that traditional development processes are not equipped to handle.

Once a system reaches the stage of testing against user prompts, developers often find themselves in a cycle of tweaking, adjusting, and redesigning, because the LLM may respond in unexpected ways to even slight changes in inputs. Thus, manual coding becomes inefficient, as each prompt may necessitate a redesign of the function set to address unforeseen behaviors.

Infinite Inputs in GenAI Apps vs. Finite Inputs in Traditional Apps

Traditional software development relies on clearly defined input parameters, often restricted by validation rules that ensure consistent, predictable behavior. Developers can manage finite sets of inputs and design systems to reject anything that falls outside those boundaries. This predictability is a hallmark of traditional programming.

LLM-based systems, however, operate in a world of infinite inputs. Users may phrase the same request in countless ways — one user may ask, “Find me a flight to New York tomorrow,” while another could say, “Book a plane ticket to NYC for October 24th.” Each of these requests might vary in structure, phrasing, or intent, and the GenAI system must be able to interpret and handle all possible variations.

This level of complexity is impossible to fully constrain or predict. There’s no way to build traditional validation rules that can account for every possible input, and as such, unforeseen prompts can always emerge that break the system. Unlike deterministic programming, where inputs and outcomes are largely controllable, LLM-based systems will continually encounter new inputs that expose gaps in the function-calling logic. When this happens, manually coded solutions can quickly become obsolete, and the entire function set may require a redesign to accommodate new variations.

Updates to LLMs Require Rebuilding Function-Calling Applications

Switching to a different LLM or updating the existing model introduces another layer of complexity. Every LLM reacts differently to inputs. This means that switching from one version of an LLM to another — or from one model to a completely different one — can break the functionality of previously coded systems. What worked well with one model may perform poorly or fail entirely with another.

This inherent variability makes manual coding impractical. Any hard-coded solutions tied to specific LLM behaviors will need to be rewritten when changes occur. In contrast, an AI-driven approach, where function calls are dynamically generated based on high-level guidance, allows systems to adapt seamlessly to LLM updates or replacements. This adaptability is crucial for maintaining the effectiveness of function-calling systems over time.

The Approach: Preserve Essential Insights, Not Ephemeral Code

Manual coding in function-calling systems must be avoided because, as explained earlier, it simply will not work in the long term. The nature of LLM-based systems — where inputs are infinite and constantly evolving, and where updates or changes to the LLM require function redesigns — means that any manually written code will need to be rewritten repeatedly. In this context, code must be treated as a throwaway artifact. It needs to work, but it is not something that should be reviewed, optimized for long-term use, or treated with the same permanence as traditional code. The goal is to generate functioning code that solves the immediate problem, but it should be understood from the outset that this code will not live forever.

Instead of focusing on writing code that will be maintained like traditional software, the focus must be on guiding the AI to generate a function set that works for the given task. While the LLM can often figure out the function calls on its own, it may run into challenges where it needs specific hints or guidance. For example, if an API supports a token parameter, the LLM might mistakenly try to use that parameter for authentication by placing it in the wrong location, such as the request body, instead of the header. In this case, the user might need to intervene and give the LLM a hint — telling it not to use the token parameter in a specific way.

The real value lies in capturing these hints or guidance provided by the user during the process of function generation. These insights help the LLM avoid common mistakes and refine its approach. By documenting and preserving this guidance, future regenerations of the function set can apply these same insights, ensuring that the new set of functions benefits from the lessons learned previously.

In essence, the approach to building function-calling systems is not about perfecting the code itself. Instead, it’s about capturing and refining the rules, hints, and context that guide the LLM in generating the code. This allows the system to adapt more easily to changes in prompts, inputs, or even LLM updates, as the foundational knowledge remains intact and is reapplied when needed.

Conclusion

Function-calling systems powered by GenAI and LLMs require a fundamentally different approach than traditional programming. The non-deterministic behavior of LLMs, the infinite variability of user inputs, and the constant need to rebuild function sets for updated models all highlight the unsustainability of manual coding. Instead, these systems demand a dynamic, no-code approach that is adaptive and responsive to change.

By focusing on dynamic function generation rather than hard-coded solutions, function-calling systems can evolve alongside advancements in AI technology. The future of GenAI-driven applications lies in leveraging AI itself to build, maintain, and improve the systems that deliver real-world functionality — ensuring that these applications remain resilient, flexible, and effective in the face of constant change.