Navigating AI Development: A Technical Writer’s Guide to Programming Paradigms

In a previous post, I laid out best practices for technical writers focusing on object-oriented programming (OOP). I also mentioned another important programming paradigm: functional programming (FP). While OOP is primarily utilized for software development, functional programming finds significant application in data science and financial modeling, particularly within AI.

I’ll focus on AI development in this post and explore the basics of the programming paradigms predominantly used for AI development. These are Functional, Declarative, and Imperative programming. By focusing on these programming paradigms, technical writers can significantly contribute to the effectiveness and accessibility of documentation within the rapidly evolving field of AI.

1. Functional programming
2. Declarative programming
3. Imperative programming
4. Synergy of paradigms

Functional programming

Functional programming is a predominant programming paradigm in AI development. It emphasizes the following key principles:

• Immutability – Once data is created, it cannot be changed. AI systems need immutability for data integrity, concurrency, and reproducibility.
• Stateless computation – Processes do not retain any internal state between invocations. This enables scalability, simplicity, and predictability
• Treating functions as first-class citizens – Functions can be treated like any other data type. This enables composability and flexibility in model building and data processing.

Together, these principles help AI developers manage complexity, build reliable systems, and optimize performance in large-scale and parallelized environments. They also reduce side effects and make code more predictable, easier to test, and maintain. Focusing on small, reusable, and composable functions leads to greater modularity and flexibility while avoiding shared state, enhancing parallelism and concurrency—key in AI and data processing.

In contrast, OOP’s reliance on mutable state and object interactions can introduce complexity and unintended behaviors, making functional programming a cleaner and more robust approach for specific AI applications.

Example

Consider a list of numbers representing raw data that we need to clean and process through a series of transformations:

1. Filtering out negative numbers.
2. Doubling each number.
3. Summing the resulting list.

Explanation

Pure Functions: filter_negative, double_numbers, and sum_numbers are all pure functions because they return new data without modifying the original list. Given the same inputs, they always produce the same outputs and have no side effects.

Higher-Order Function (compose): The compose function is a higher-order function that takes multiple functions as arguments and returns a new function that applies each transformation in sequence. This is a common functional programming pattern, enabling you to combine functions into a pipeline.

Immutability: The data transformations don’t modify the original list (data). Instead, each function creates and returns a new list, preserving immutability. The filter, map, and sum operations process data without side effects.

Function Composition: We build a processing pipeline using compose(filter_negative, double_numbers, sum_numbers), which chains together all the transformation functions into a single operation.

Declarative programming

Declarative programming is another programming paradigm. While functional programming is a specific style within that paradigm focused on mathematical functions and immutable data, declarative programming is a broad paradigm that emphasizes specifying desired outcomes. It focuses on expressing what the program should achieve rather than how to accomplish it. In this paradigm, developers specify the desired outcomes and the programming language or framework manages the underlying implementation details. All functional programming is declarative, but not all declarative programming is functional.

Example

Let’s look at a SQL example, which is a programming language used for databases. Suppose we have a database table called employees with the following structure:

ID	Name	Department	Salary
1	Phyllis	HR	70000
2	Dolores	IT	80000
3	Dorothy	IT	75000
4	Reginald	Sales	60000

To retrieve the names of employees in the IT department with a salary greater than 70000, you could write a SQL query like this:

Explanation

In this example, the query specifies the information needed: the names of employees in the IT department earning more than 70,000. The SQL engine determines how to retrieve that data from the employees table.

Declarative programming enhances AI development by providing a clear, high-level way to express requirements and logic. It allows developers to focus on what they want rather than how to achieve it, facilitating straightforward interactions with data, model definitions, and rule-based systems.

This approach is ideal for tasks that involve querying data, defining models, or expressing complex logic without needing to specify detailed implementation steps. This is beneficial for many AI and data processing scenarios.

Imperative programming

Imperative programming is the final programming paradigm. In this paradigm, instead of focusing on what a program should achieve, it focuses on how a program operates through a sequence of statements or instructions that change the program’s state. Developers explicitly outline the computer’s steps to achieve a specific outcome, often using control structures such as loops, conditionals, and function calls.

This paradigm is favored in scenarios where detailed control over execution, state management, and performance optimization are crucial, such as performance-intensive, real-time, and resource-constrained AI systems. It provides a straightforward approach to writing programs that are easy to understand and manage, especially for applications with complex logic and real-time requirements, which are essential in AI development.

Example

Let’s implement a function that calculates the factorial of a number using an imperative approach, emphasizing step-by-step instructions:

Explanation

The process begins with initializing the result variable to 1, which serves as the cumulative product. A for loop iterates from 1 to n, providing explicit instructions for calculating the factorial by multiplying result by each integer in that range. During each iteration, the state of the result variable is updated, reflecting the ongoing computation until the final factorial value is reached.

Imperative programming in AI development provides a framework for defining detailed, step-by-step procedures for handling data, training models, and implementing algorithms. It offers flexibility and precision, enabling developers to control the flow of operations and state updates, which is crucial for building efficient AI systems.

Synergy of paradigms

In AI development, functional programming, declarative programming, and imperative programming can work together to enhance the design, implementation, and maintenance of AI systems. By leveraging the strengths of each paradigm, developers can create more expressive and efficient code, streamline the model training process, and improve overall system performance. This integration allows for a holistic approach to solving complex AI problems while maintaining clarity and flexibility in the codebase.

Furthermore, when combined with object-oriented programming (OOP), these paradigms contribute to clearer code structure, improved maintainability, and more robust system design. This synergy enables developers to create complex systems that leverage the best practices from each paradigm while addressing specific needs in their applications.

Understanding the synergy between different paradigms allows technical writers to present a comprehensive view of the programming they document. They can discuss how functional, declarative, and imperative programming can complement each other and their roles in AI development, providing a richer context for readers.

By leveraging this knowledge, technical writers can significantly improve the effectiveness and accessibility of documentation, ensuring that it meets the needs of users in the rapidly evolving field of AI and beyond.