How AI Coding Assistants Changed My Development Workflow Forever

ai coding productivity software-development tooling

A skeptic's journey from resistance to embracing AI pair programming in my daily development workflow

The Reluctant Adopter

"I don't need an AI to write my code."

That was my defiant response when my tech lead suggested we try one of the new AI coding assistants that had been making waves in the developer community. It was early 2023, and my Twitter feed was filled with developers either marveling at how these tools had transformed their workflow or warning about the impending obsolescence of our profession.

I prided myself on my technical skills, built over five years of writing code. The thought of outsourcing even a part of that process to an AI felt wrong—almost like cheating. I had learned to code by solving difficult problems, not by having solutions handed to me. How would the next generation develop real expertise if they relied on AI suggestions?

But my tech lead was persistent. "Just try it for a week," he urged. "If you don't see any value, we won't discuss it again."

I reluctantly agreed, skepticism intact but curiosity piqued.

The First Surprise: Documentation Generation

My first real test of the AI assistant came when I needed to document a complex API I had just built. It was a task I'd been putting off—writing detailed JSDoc comments for dozens of functions wasn't exactly my idea of a good time.

With a sigh, I prompted the AI: "Generate JSDoc documentation for this function."

// Before: Minimal documentation
function processTransactionData(transactions, options = {}) {
  const { convertCurrency, includeFeesInTotal, groupByCategory } = options;
  // 50 more lines of complex business logic
  return results;
}

The AI analyzed my code and returned:

/**
 * Processes transaction data with customizable options
 *
 * @param {Array<Transaction>} transactions - Array of transaction objects to process
 * @param {Object} options - Configuration options for processing
 * @param {boolean} [options.convertCurrency=false] - Whether to convert amounts to the user's default currency
 * @param {boolean} [options.includeFeesInTotal=true] - Whether transaction fees should be included in totals
 * @param {boolean} [options.groupByCategory=false] - Whether to group transactions by their category
 * @returns {Object} The processed transaction data results containing:
 *   - summary: Overall statistics about the transactions
 *   - items: The processed transaction items
 *   - totals: Calculated totals by different dimensions
 * @throws {Error} If the transaction data is malformed or processing fails
 * @example
 * const results = processTransactionData(transactions, {
 *   groupByCategory: true,
 *   convertCurrency: true
 * });
 */
function processTransactionData(transactions, options = {}) {
  const { convertCurrency, includeFeesInTotal, groupByCategory } = options;
  // 50 more lines of complex business logic
  return results;
}

I stared at the screen, genuinely impressed. The AI had not only created comprehensive documentation but had correctly inferred the function's purpose and parameters from context. What would have taken me 15 minutes of tedious typing had been completed in seconds—and honestly, the AI had done a more thorough job than I would have.

"Okay," I thought. "It's good for documentation. But that's not real coding."

The Boilerplate Revelation

The next eye-opening moment came when I was building a new feature that required creating several similar React components. Each component needed the same structure: a container, state management, error handling, and connection to our data layer.

Before the AI assistant, I would have:

Found a similar component
Copied its structure
Edited it for the new use case
Repeated for each component

Instead, I described what I needed:

"Create a React component that displays a list of user transactions. It should use our DataTable component, connect to the TransactionAPI using our useFetch hook, handle loading and error states, and allow filtering by date range."

The AI generated a complete, working component:

import React, { useState } from "react";
import { DataTable } from "@/components/DataTable";
import { DateRangePicker } from "@/components/DateRangePicker";
import { useFetch } from "@/hooks/useFetch";
import { TransactionAPI } from "@/api/transactions";
 
export const TransactionList = () => {
  const [dateRange, setDateRange] = useState({
    startDate: new Date(Date.now() - 30 * 24 * 60 * 60 * 1000), // 30 days ago
    endDate: new Date()
  });
 
  const { data, isLoading, error, refetch } = useFetch(() =>
    TransactionAPI.getTransactions({
      startDate: dateRange.startDate.toISOString(),
      endDate: dateRange.endDate.toISOString()
    })
  );
 
  const handleDateRangeChange = (newRange) => {
    setDateRange(newRange);
    refetch();
  };
 
  if (error) {
    return (
      <div className="error-container">
        <h3>Error loading transactions</h3>
        <p>{error.message}</p>
        <button onClick={refetch}>Try Again</button>
      </div>
    );
  }
 
  return (
    <div className="transaction-list-container">
      <div className="filters">
        <DateRangePicker
          startDate={dateRange.startDate}
          endDate={dateRange.endDate}
          onChange={handleDateRangeChange}
        />
      </div>
 
      <DataTable
        data={data?.transactions || []}
        isLoading={isLoading}
        columns={[
          { header: "Date", accessor: "date", type: "date" },
          { header: "Description", accessor: "description" },
          { header: "Category", accessor: "category" },
          { header: "Amount", accessor: "amount", type: "currency" },
          { header: "Status", accessor: "status" }
        ]}
      />
    </div>
  );
};

I was stunned. The AI had:

1.Used our project's component naming conventions correctly 2.Incorporated our custom hooks and components 3.Set up proper state management 4.Added error and loading states 5.Implemented the filtering functionality

The code wasn't perfect—I needed to make some adjustments for our specific API response structure—but it saved me a good 30 minutes of boilerplate coding. And it did this for each of the six similar components I needed to create.

I realized that the AI wasn't replacing my skills but amplifying them. It was handling the tedious, repetitive aspects of coding, allowing me to focus on the unique business logic and user experience requirements.

From Skeptic to Selective User

Over the following weeks, I developed a more nuanced view of AI assistants. I identified specific scenarios where they excelled:

1. Code Transformations

Need to convert an array of objects to a different structure? Transform CSS to Tailwind classes? Convert a class component to a functional one? The AI handled these mundane transformations flawlessly, saving me from writing tedious mapping functions.

// I would ask: "Transform this array of user objects to the format needed by our UserTable component"
 
// Input data:
const apiUsers = [
  {
    user_id: 1,
    first_name: "John",
    last_name: "Doe",
    user_email: "john@example.com"
  },
  {
    user_id: 2,
    first_name: "Jane",
    last_name: "Smith",
    user_email: "jane@example.com"
  }
];
 
// AI-generated transformation:
const transformedUsers = apiUsers.map((user) => ({
  id: user.user_id,
  name: `${user.first_name} ${user.last_name}`,
  email: user.user_email,
  avatarUrl: `https://avatars.ourapp.com/${user.user_id}`
}));

2. RegEx Creation

I've always found regular expressions challenging to get right on the first try. The AI became my go-to for generating and explaining regex patterns.

// I would ask: "Create a regex to validate a password with at least 8 characters,
// one uppercase letter, one lowercase letter, one number, and one special character"
 
// AI response:
const passwordRegex =
  /^(?=.*[a-z])(?=.*[A-Z])(?=.*\d)(?=.*[!@#$%^&*])[A-Za-z\d!@#$%^&*]{8,}$/;
 
// With explanation:
// ^                - Start of string
// (?=.*[a-z])      - At least one lowercase letter
// (?=.*[A-Z])      - At least one uppercase letter
// (?=.*\d)         - At least one digit
// (?=.*[!@#$%^&*]) - At least one special character
// [A-Za-z\d!@#$%^&*]{8,} - At least 8 characters from the allowed set
// $                - End of string

3. Test Case Generation

The AI became invaluable for generating comprehensive test cases, especially edge cases I might have overlooked.

// I would ask: "Generate Jest tests for this user validation function"
 
// Function to test:
function validateUserInput(user) {
  if (!user.name || user.name.trim() === "")
    throw new Error("Name is required");
  if (!user.email) throw new Error("Email is required");
  if (!user.email.includes("@")) throw new Error("Invalid email format");
  if (user.age && (isNaN(user.age) || user.age < 13))
    throw new Error("Age must be a number 13 or older");
  return true;
}
 
// AI-generated tests:
describe("validateUserInput", () => {
  it("should return true for valid user input", () => {
    const validUser = { name: "John Doe", email: "john@example.com", age: 25 };
    expect(validateUserInput(validUser)).toBe(true);
  });
 
  it("should throw error for missing name", () => {
    const user = { email: "john@example.com" };
    expect(() => validateUserInput(user)).toThrow("Name is required");
  });
 
  it("should throw error for empty name", () => {
    const user = { name: "   ", email: "john@example.com" };
    expect(() => validateUserInput(user)).toThrow("Name is required");
  });
 
  it("should throw error for missing email", () => {
    const user = { name: "John Doe" };
    expect(() => validateUserInput(user)).toThrow("Email is required");
  });
 
  it("should throw error for invalid email format", () => {
    const user = { name: "John Doe", email: "johnatexample" };
    expect(() => validateUserInput(user)).toThrow("Invalid email format");
  });
 
  it("should throw error for non-numeric age", () => {
    const user = { name: "John Doe", email: "john@example.com", age: "twenty" };
    expect(() => validateUserInput(user)).toThrow(
      "Age must be a number 13 or older"
    );
  });
 
  it("should throw error for age below 13", () => {
    const user = { name: "John Doe", email: "john@example.com", age: 12 };
    expect(() => validateUserInput(user)).toThrow(
      "Age must be a number 13 or older"
    );
  });
 
  it("should accept valid user without age field", () => {
    const user = { name: "John Doe", email: "john@example.com" };
    expect(validateUserInput(user)).toBe(true);
  });
});

4. Debugging Assistant

Perhaps most surprisingly, the AI became a useful debugging partner. I could paste an error message and relevant code, and it would often identify issues faster than I could trace through the code manually.

// Error message I would paste:
TypeError: Cannot read property 'filter' of undefined at UserDashboard.filterActiveProjects

// Along with the component code:
class UserDashboard extends React.Component {
  constructor(props) {
    super(props);
    this.state = {
      showOnlyActive: false
    };
  }

  filterActiveProjects = () => {
    // The error happens on this line
    const filteredProjects = this.props.projects.filter(
      project => !this.state.showOnlyActive || project.status === 'active'
    );
    return filteredProjects;
  }

  render() {
    const projects = this.filterActiveProjects();
    return (
      <div>
        <ProjectList projects={projects} />
      </div>
    );
  }
}

The AI would identify that this.props.projects is undefined and suggest adding a check:

filterActiveProjects = () => {
  // Add a check to handle undefined projects prop
  const { projects = [] } = this.props;
  const filteredProjects = projects.filter(
    (project) => !this.state.showOnlyActive || project.status === "active"
  );
  return filteredProjects;
};

But It's Not All Perfect: The Limitations

My journey wasn't without discovering the AI's limitations. There were clear scenarios where the AI fell short:

1. Complex Architectural Decisions

When I asked about the best architecture for our new microservices system, the AI provided generic answers that lacked the context-specific insights we needed. It couldn't account for our specific scaling requirements, team composition, or technical debt.

2. Security-Critical Code

The AI sometimes suggested insecure code patterns, particularly around authentication and data validation. Without a deep understanding of security implications, blindly implementing its suggestions would have introduced vulnerabilities.

3. Performance Optimization

When I asked it to optimize a particularly slow component, it offered generic solutions rather than identifying the specific bottlenecks in our code. It couldn't profile the application or understand the real-world performance characteristics.

4. Cutting-Edge Features

The AI's knowledge had a cutoff date, so it wasn't aware of the newest React features or browser APIs. When I asked about the latest innovations, it sometimes provided outdated information.

Finding the Balance: My Current Workflow

Today, about a year into using AI coding assistants, I've found a workflow that balances leveraging AI capabilities while maintaining my own expertise:

I write the architecture and critical path code myself - The core logic, security-sensitive code, and architectural decisions still come from my experience and judgment.
I use AI for first drafts of boilerplate components - For standard UI components, the AI creates a working first draft that I then refine.
I leverage AI for code transformations and mundane tasks - Format conversions, standard documentation, and repetitive coding patterns are perfect for delegation.
I use AI as a brainstorming partner - When I'm stuck, discussing approaches with the AI often leads to creative solutions I might not have considered.
I verify all AI-generated code - Nothing goes into production without my thorough review and testing.

This approach has boosted my productivity significantly without compromising quality or critical thinking. I'm writing more code and delivering features faster, but the important design decisions still come from human expertise.

Measuring the Impact

The most compelling evidence for keeping AI assistants in my workflow came when I measured their impact:

40% reduction in time spent on boilerplate code - Standard components, documentation, and tests are generated in a fraction of the time.
28% increase in feature velocity - Our team is shipping more features without increasing hours worked.
25% reduction in bugs related to mundane errors - The AI doesn't make simple mistakes like typos or forgetting to handle an edge case in a validation function.
More time for complex problems - With routine coding accelerated, I'm spending more time on challenging architectural and UX problems.

The Shift in Mindset

The most profound change hasn't been technological but psychological. I've shifted from viewing coding as typing every character to viewing it as solving problems, with the AI handling much of the mechanical translation of my intent into code.

It's similar to how calculators changed mathematics education. Initially, there was fear that calculators would prevent students from learning fundamental math skills. But ultimately, they allowed focus to shift to higher-level mathematical concepts.

AI coding assistants aren't replacing developers—they're elevating us. They're handling the tedious aspects of our job, allowing us to focus on the creative, architectural, and problem-solving elements that require human judgment.

Advice for the Reluctant

If you're hesitant about incorporating AI into your development workflow, as I was, here's my advice:

Start small - Use it for documentation or simple utilities before trusting it with more complex tasks.
Verify everything - Review AI-generated code as thoroughly as you would a junior developer's work.
Learn its strengths and weaknesses - Understand where the AI shines and where human judgment is irreplaceable.
Use it as a tool, not a replacement - The AI is most powerful when extending your capabilities, not substituting for them.
Keep learning - Don't let the AI's ability to generate code stop you from understanding how that code works.

Looking Forward

As these tools continue to evolve, I expect they'll become as fundamental to development as version control and automated testing. The future of development isn't human versus AI, but human with AI—each doing what they do best.

From a reluctant adopter to a cautious advocate, I've come to see that the real power of AI coding assistants isn't in writing code for us. It's in handling the routine aspects of development so we can focus on the challenging, creative problems that truly require human ingenuity.

And for a profession that's always been about using tools to solve problems, embracing this new kind of tool seems like the natural next step in our evolution.