Leveraging AI to Enhance Functional Programming in 2025

Functional programming has always stood out as an elegant approach to software development. Its focus on pure functions, immutability, and composability ensures clarity, predictability, and easier debugging. Languages like R, Python, and Clojure, each with their unique strengths, are particularly well-suited for functional programming paradigms. But what happens when we combine this structured approach with the power of modern AI tools? A world of efficiency and creativity opens up.

In this post, we’ll explore how to use AI to create individual functions that can later be composed into a complete program using functional programming principles. Let’s start by revisiting the core advantages of functional programming and then delve into how AI can enhance this workflow.

Advantages of Functional Programming

1. Modularity: Functions are independent and self-contained, making them easy to compose and reuse.

2. Testability: Pure functions depend solely on their inputs and produce consistent outputs, simplifying debugging and testing.

3. Concurrency: Immutability and statelessness reduce race conditions, making functional programs naturally suited for parallelism.

4. Readability and Maintainability: Code is more predictable and easier to understand when side effects are minimized.

With these principles in mind, functional programming thrives on creating reliable, isolated functions. This makes it a natural match for AI tools, which excel at generating discrete, task-specific code snippets.

Using AI to Generate Functions

AI engines can help you efficiently generate functions tailored to specific tasks. Here’s how you can incorporate AI into your functional programming workflow:

1. Defining the Task

Clearly articulate the task you want the function to perform. You can be as specific as you want. I have noticed that, the more specific you are in one single prompt, the better. On the contrary, I have noticed that when you allow the engine to get context through a long conversation it usually does more mistakes. Here are some hints:

• Start by telling which programming language it should use, and if you have some preferences in libraries include it here. Example: “Create a function in R using the purrr library that does…”.

• Continue by telling what exactly the function should do. Example: “… a function that filters even numbers from a list”.

• At this step it can help by telling the engine what to expect as an input and what to give as an output. From the previous example: “… a function that, given a list of numbers as a single string, filters even numbers and returns them as a vector”.

• It is also helpful to provide examples and give some data. Example: “for example, from the string ‘1, 2, 3, 4, 5, 6, 7’ it should return the vector with the values 2, 4 and 6 only”.

• You can also request additional parameters to take, and specify what should they do. For example: “add a parameter ‘length’ to specify the maximum length of the vector to be returned”.

The key is to be specific about inputs, outputs, and any constraints or edge cases. The example above can end up in something like this:

Using R and the library purrr, create a function that, given a string of numbers, returns a vector with only the even numbers in it. In the string, the numbers are separated by comma. For example, from the string “1, 2, 3, 4, 5, 6, 7” it should return c(2, 4, 6). Additionally, add a parameter called “length” to specify the maximum size of the final vector.

2. Testing in the REPL

Once the AI generates the function, the REPL (Read-Eval-Print Loop) environments, also known simply as consoles, allow you to test and refine it in isolation. Here’s an example workflow:

• Paste the generated function into your script with a commented note that it is a work in progress.

• Evaluate the function into the REPL. You could as well paste it directly into the REPL but it gives you less space for tuning it.

• Test it with various inputs to verify correctness.

• Debug or tune the function as necessary.

If issues arise, you can return to the AI with detailed feedback, asking it to refine or enhance the function. For example, you might request better error handling, additional parameters, optimizations for edge cases or a particular library/function to be used within it.

Building a Complete Program

Once you have a collection of tested functions, the next step is composition. Functional programming encourages composing small, tested functions into larger workflows. This step is typically less dependent on AI since you’re now applying domain-specific business logic. Here it is more of a personal choice how to place the functions together, how they call each other, and what is your final main function(s). At this engineering stage we can also tune the details of the business logic, in case that particular details are needed.

However, you can still consult the AI for advice, such as:

• Strategies for composing functions.

• Suggestions for structuring the program to maintain functional purity.

• Recommendations for efficient data flow between functions.

• Recommendations for optimization.

• Advice on how to handle sensitive information, such as login credentials.

Documentation and Unit Testing with AI

AI tools are not only great at generating code but can also help you finalize your project by:

Generating Documentation

• Ask the AI to create comprehensive docstrings or external documentation based on your code.

• Ensure the documentation highlights inputs, outputs, and edge cases, maintaining clarity and professionalism.

• You can also ask the AI engine to create simple reproducible examples for it.

Creating Unit Tests

• Request the AI to generate unit tests for each function. For example:

• In Python, ask for unittest or pytest test cases.

• In R, request testthat test scripts.

• In Clojure, ask for test functions using its native testing tools.