Debugging AI-Generated Code: How We Test, Fix, and Ship Safely

Like many developers today, we at API DOTS lean on AI coding tools to scaffold projects, explore unfamiliar APIs, and quickly prototype for client engagements. This trend is becoming increasingly common as teams adopt AI-powered software to accelerate development workflows.

Yet that magic comes with risks: AI‑generated code often compiles and runs but can hide subtle bugs, security vulnerabilities, or inefficient patterns. Overreliance on AI can also create a false sense of confidence, leading to fragile solutions slipping into production.

In this post, we are sharing how our team debugs and refines AI‑generated code in real client projects. We’ll walk you through the practices we use, from crafting system prompts to performance testing and handling hallucinations. 

To show the process, we have included a simple example of AI‑generated code that needs fixing. These are the lessons we apply every day as we integrate AI into our development workflows at API DOTS.

1. Setting Up Success with System Prompts

Every AI session starts with context. When we first began experimenting with coding assistants, we would drop into a chat with only a short task description and be disappointed when the output missed the mark.

We’ve since learned that system prompts (predefined instructions placed in the project root) provide the tool with the necessary context. Our system prompt reminds the assistant to:

• Check the associated story or ticket before starting work.
• Create a feature branch at the beginning of a session.
• Write unit tests for any code changes.
• Ask before running a local server.

By setting these expectations up front, we save time and reduce the number of corrections required later. Just as importantly, everyone on our team benefits from consistent behavior across sessions.

2. Integrating MCP into the Workflow

AI tools are helpful on their own, but connecting them to existing systems makes them far more powerful, particularly when teams focus on AI integration within their SaaS platforms.

We adopted the Model Context Protocol (MCP) after reading about Anthropic’s open standard. With an MCP server wired to our Git repository and agile board, we can ask the AI to:

• Move stories on our board or create new ones.
• Open feature branches and list active pull requests.
• Fetch Git history for a file or determine which PRs need review.

With MCP, the AI becomes a true teammate rather than a toy. When we ask for a fix, it can open the relevant branch, scan the commit history, and update the task on our board. This context reduces hallucinations and increases productivity.

3. Validating Code with Scanning Tools

Even when AI produces code that appears correct, we never merge it without automated scanning. Several tools have become staples in our workflow:

• CodeQL performs vulnerability scans, checking for injection attacks and insecure patterns. We run it as a GitHub Action on every pull request.
• TypeScript‑eslint enforces type safety and style in our TypeScript projects.
• SonarQube and OWASP Dependency Check identify anti-patterns and known vulnerabilities in dependencies.
• GitHub Copilot PR Review summarizes changes and catches edge cases we might miss.

These scanners act as guardrails. They highlight suspicious code paths before humans review the pull request, which is especially valuable when AI‑generated code introduces outdated patterns or hidden issues.

4. Performance Testing

Performance validation is particularly important in modern systems where scalability is expected from day one, especially when building software tailored to real business workflows and long-term growth.

One of our biggest surprises when shipping AI‑generated code was discovering that “working” solutions sometimes fell apart under load. We now treat performance as a core requirement for any AI‑assisted feature. Our process includes:

1. Benchmarking: We compare runtime against an established benchmark to ensure new code consistently meets performance targets.
2. Load testing: We simulate concurrent users to observe memory and CPU usage.
3. Code analysis & profiling: Tools like profilers isolate slow sections and highlight unnecessary loops or brute‑force algorithms.
4. Realistic test environments: Our staging environment mirrors production to avoid performance surprises.

We’re continually amazed by how much tuning is required even for small AI‑generated functions. Treating performance testing as a first‑class citizen prevents unpleasant surprises when the code scales.

5. Working Iteratively to Catch Hallucinations

AI models sometimes generate code that looks plausible but is wrong — known as hallucinations. We’ve found that iterative development mitigates this risk. Instead of dropping a huge chunk of AI code into production, we:

• Break tasks into small user stories.
• Capture context in each story and update requirements as they evolve.
• Validate each code change individually before moving on.

This agile approach means that if the AI misinterprets a requirement, we catch it quickly. Working in small increments also makes it easier to debug and fix issues.

Beyond Basics: Modern Best Practices (2026)

While the steps above come directly from our day‑to‑day work, the broader development community has learned additional lessons in 2026. Among the most important trends are:

• Treat AI like a teammate: Modern assistants understand project structure and context. Teams that involve AI in architectural decisions, dependency mapping, and legacy analysis achieve better results than those that ask for one‑off snippets.

• Build repeatable workflows: The “perfect prompt” is overrated. Successful teams define a consistent pattern: set intent, list constraints, ask the model to reason, and repeat.

• Review the AI’s reasoning: New tools expose the model’s decision process. By reviewing these traces, we catch logic errors long before code reaches production.

• Security first: AI can introduce vulnerabilities as quickly as it writes code. Security‑aware reviews, guided by OWASP and MITRE updates, are now mandatory.

• Test generation: AI generates unit, integration, and regression tests before we write a feature. Starting from a testing blueprint improves coverage and stability.

• Refactor incrementally: Rather than asking AI to rewrite entire modules, we let it summarize unfamiliar code and then refactor the highest‑risk parts one at a time.

• Pair program with AI: Treating the model as a conversation partner helps validate ideas. The value comes from discussion, not from blindly accepting the output.

• Embed AI in CI pipelines: Many teams are also embedding AI assistants directly into CI pipelines and DevOps workflows to improve reliability and automate quality checks.

• Speed up onboarding: AI can generate dependency graphs, architectural maps, and module explanations, cutting onboarding time from weeks to days.

• Keep humans in charge: AI accelerates refactoring and documentation, but human engineers still define architecture and system behaviour. Many critical tech roles still depend on human judgment, creativity, and system-level thinking, which is why discussions around AI-proof careers continue to highlight the importance of experienced developers in modern engineering teams.

By combining these emerging practices with the fundamentals of scanning and testing, we’ve built a workflow that harnesses AI’s strengths while guarding against its weaknesses.

Example: Fixing a Buggy AI‑Generated Function

To illustrate, here’s a simple bug we encountered during a client project at APIDOTS. We asked our AI assistant to write a function that computes the average of a list. The generated code looked reasonable:

At first glance, this seemed fine, but when we called compute_average([]) it threw a ZeroDivisionError because the list is empty. This is an example of a subtle edge case that the AI overlooked—a common issue when tools generalise typical patterns but miss corner cases.

Our fix was straightforward:

Below is a screenshot showing both the AI‑generated code, the error, and our fix. Creating this image helped us document the issue for our team and client:

Capturing errors visually like this makes it easy to share context and remember what went wrong. In larger codebases, we take similar screenshots for trickier bugs and include them in pull requests for clarity.

Final Thoughts

AI tools accelerate development, but they still require oversight. By setting clear system prompts, integrating with workflow servers, scanning for vulnerabilities, testing performance, working iteratively, and embracing modern AI‑development practices, we can reap the benefits of AI while avoiding its pitfalls. 

The human element remains critical; our judgment, intuition, and contextual knowledge steer the AI toward useful outcomes.

We hope this team journey through our AI‑assisted workflow at API DOTS helps you refine yours. Feel free to adapt these patterns and share your own experiences. Happy debugging!

For more insights on modern development, AI tools, and software engineering practices, explore the latest articles on our engineering blog.

Aminah Rafaqat

Hi! I’m Aminah Rafaqat, a technical writer, content designer, and editor with an academic background in English Language and Literature. Thanks for taking a moment to get to know me. My work focuses on making complex information clear and accessible for B2B audiences. I’ve written extensively across several industries, including AI, SaaS, e-commerce, digital marketing, fintech, and health & fitness , with AI as the area I explore most deeply. With a foundation in linguistic precision and analytical reading, I bring a blend of technical understanding and strong language skills to every project. Over the years, I’ve collaborated with organizations across different regions, including teams here in the UAE, to create documentation that’s structured, accurate, and genuinely useful. I specialize in technical writing, content design, editing, and producing clear communication across digital and print platforms. At the core of my approach is a simple belief: when information is easy to understand, everything else becomes easier.