Like many software engineers, I’ve spent the last six months embracing AI coding assistants in my daily work. When GitHub Copilot and similar tools first entered our department, expectations were high. Management anticipated dramatic productivity increases, and many developers were excited about the promise of accelerated development cycles.

Six months later, I’m sharing my unfiltered experience - both the impressive wins and the frustrating challenges that are rarely discussed in the glowing case studies published by AI providers.

The Mixed Reality of AI Coding Tools

While success stories abound in marketing materials (with GitHub reporting productivity increases of up to 55% in controlled studies), my on-the-ground experience has been more nuanced. The benefits are real, but they come with significant limitations that aren’t mentioned in promotional literature.

What Works Well

Several use cases have consistently delivered value:

  1. Documentation generation: AI assistants excel at creating clear, comprehensive documentation for existing code. What used to take hours of tedious writing now happens in minutes.

  2. Refactoring support: When restructuring code without changing functionality, AI tools provide valuable suggestions and can handle much of the mechanical transformation work.

  3. Test data generation: Creating realistic test fixtures and edge cases has become remarkably easier, with the AI coming up with scenarios I might have overlooked.

The Frustrating Reality

However, when it comes to generating new, functional code, the experience has often been disappointing:

  1. Unexpected code constructs: The AI frequently generates code using patterns and approaches that don’t align with our codebase’s established architecture, requiring significant rework.

  2. Hallucinated API usage: One of the most frustrating issues is the AI confidently generating code that uses non-existent APIs or methods. As one researcher noted, these tools “generate correct code only 46.3% of the time” (Yetistiren et al., 2023).

  3. Extensive QA overhead: The need to thoroughly review every AI suggestion often negates the productivity gains. The verification process can be more time-consuming than writing the code from scratch.

These challenges align with independent research findings. A study from GitClear found “disconcerting trends for maintainability” with “code churn projected to double in 2024 compared to its 2021, pre-AI baseline” - suggesting AI-generated code often requires significant rework.

Changing My Approach

After months of trial and error, I’ve realized that the problem isn’t necessarily with the AI tools themselves, but with how I’ve been using them. Two critical insights have emerged:

1. From “How” to “What”

The most profound shift has been in how I communicate with AI assistants. Initially, I approached them as I would an autocomplete tool - telling them how to implement something step by step. This led to disappointing results.

I’m now focusing more on describing what I want to achieve, providing clear requirements and constraints rather than implementation details. This declarative approach yields better results, particularly for complex tasks. For example:

Instead of this (imperative approach):

Write a function that loops through an array of users, filters out the inactive ones, and sorts the remaining users by their last login date

I now do this (declarative approach):

Create a function that returns active users sorted by most recent login. 
Requirements:
- Input: Array of user objects with properties {id, name, isActive, lastLoginDate}
- Output: Filtered array containing only active users (isActive=true)
- Sort order: Most recent login first (lastLoginDate in descending order)
- Performance: Should handle large user sets efficiently
- Error handling: Return empty array for null/undefined inputs

The difference in quality is remarkable. By providing clear specifications and constraints, the AI generates code that’s much closer to production-ready.

2. Investing Time in Training and Experimentation

My second insight is that effectively using AI as a pair programmer requires substantial time investment to get to know each other. As Marcio Frayze aptly put it in his blog post:

Copilot’s slogan is: “Your AI pair programmer”. And yes, it felt like there was a person programming with me all the time - but an annoying one, who wouldn’t shut up for a second. 📣 It was like doing pair programming, but with someone who has no idea what it means to work this way. Who understands absolutely nothing about the problem I want to solve. And when something goes wrong, blames me.

I’ve started dedicating specific blocks of time to experiment with different prompting techniques, exploring the AI’s capabilities across various use cases. This structured experimentation has been invaluable in understanding the tool’s strengths and weaknesses.

Independent research supports this approach. A study analyzing GitHub discussions and Stack Overflow posts about Copilot found that “the main limitation encountered by practitioners when using Copilot is difficulty of integration” into existing workflows. This integration challenge requires dedicated learning time.

Looking Forward: The Promise of AI Agents

As Microsoft continues to evolve GitHub Copilot with new capabilities like agent mode and MCP (Microsoft Copilot Platform), I believe we’re approaching an inflection point where AI tools will fundamentally change our profession.

These advancements promise more autonomous coding assistance, with AI that can understand project context, propose architectural changes, and handle multi-step coding tasks with less human intervention. The key will be learning how to effectively collaborate with these more powerful assistants.

Conclusion: Cautious Optimism

Six months in, I remain convinced that AI coding assistants represent the future of software development. However, the path to realizing their full potential is longer and more complex than the marketing materials suggest.

Real productivity gains come not from simply installing these tools, but from:

  1. Fundamentally changing how we communicate our intent to AI systems
  2. Dedicating substantial time to learning effective interaction patterns
  3. Restructuring our development workflows to leverage AI’s strengths while mitigating its limitations

As we collectively navigate this transition, sharing honest experiences - both positive and negative - will be crucial for our profession’s evolution. The AI revolution in software development is real, but it’s a marathon, not a sprint.