AI-Generated Code Reliability and Challenges in API Adoption

Artificial intelligence (AI) has brought transformative changes across industries, and one of the areas where AI is showing significant promise is in API development and adoption. But with that promise come challenges, especially when it comes to the reliability of AI-generated code and its role in streamlining developer workflows. In this article, we’ll explore how AI-generated code affects API adoption, the limitations developers face, and the potential solutions to these challenges.

Determinism in AI-Generated Code

A significant issue in AI-generated code is determinism, which refers to the ability to predict the same outcome from identical inputs consistently. For developers, determinism is critical because a non-deterministic system can introduce unpredictable bugs or subtle issues that are hard to track down.

Imagine you’re working with AI that generates thousands of lines of code, but one line is unpredictably incorrect. That small error can break an entire system, and the AI might not have the context to fix it. This challenge of determinism in AI-generated code is a major obstacle. AI often generates what seems like a plausible answer, but without sufficient context, it may not be the right answer.

This unpredictability can manifest in unexpected ways. For instance, if you asked an AI, “Why is the Golden Gate Bridge white?” it might respond as if the bridge were actually white (it’s red), simply because it tries to give a satisfying answer based on the patterns it has learned. In more serious contexts, this same “hallucination” issue—where the AI fabricates information—can lead to incorrect or insecure code being generated.

The Hallucination Problem in AI-Generated Code

Hallucinations in AI-generated code represent another significant challenge. When AI generates information or code without a reliable basis, it can lead to serious issues, especially in software development. For instance, AI might fabricate libraries or methods that don’t exist, which could mislead developers into thinking that their code will function when, in reality, it won’t.

A study conducted at Stanford revealed that AI-assisted coding tools generated insecure code significantly more often than human-written code. Another study showed that 52% of the AI-generated answers to Stack Overflow questions were incorrect. This data underlines a major problem with AI-generated code: it cannot always be trusted, and developers need to manually review and verify the code before deploying it in production.

In essence, AI-generated code can introduce vulnerabilities or errors, which developers might not catch until it’s too late. Therefore, the current best practice is to treat AI-generated code with skepticism and thoroughly review it, especially when it involves critical systems or security-sensitive applications.

Why AI Matters for API Adoption

Despite these challenges, AI plays a critical role in API adoption, particularly because it can make the API development and usage experience much smoother for developers. The growth of APIs has led to an increased need for better developer experiences, and many API-first companies recognize that a strong developer experience is crucial for their success. Companies like Stripe, Twilio, and others have set the bar high, offering robust SDKs, clear documentation, and a developer-friendly approach to API consumption.

There are three critical steps developers take when consuming APIs:

1. Learning API concepts: Developers need to understand how an API works, which endpoints to use, and how to authenticate requests. This information is typically found in API documentation.
2. Writing access code: Once developers understand the API, they need to write code that handles authentication, headers, and HTTP methods.
3. Writing use case code: Finally, developers write the code that implements their specific use case, whether it’s creating a user playlist, processing payments, or sending messages.

Take a concrete example—using the Spotify API to create a user playlist. A developer could start by reading the API documentation, downloading an SDK to handle authentication, and then writing the use case code to create the playlist. In this scenario, AI can assist with the first and third steps: learning API concepts and writing use case code.

AI-Assisted Code Generation for APIs

Let’s look at how AI can simplify API usage. Imagine a developer who wants to create a playlist on Spotify. Normally, they would have to:

1. Authenticate using OAuth.
2. Write code to call the relevant Spotify API endpoints.
3. Handle errors, responses, and pagination if necessary.

In an ideal scenario, an AI system can assist by generating the necessary code. It could help the developer learn which endpoints they need and even write the code for creating a playlist with specific parameters, such as adding the top 10 songs from a specific artist.

For example, an AI prompt could look like this: “Generate code to create a Spotify playlist and add the 10 most popular songs by Taylor Swift.” The AI would produce the code for handling authentication, calling the correct API endpoints, and managing the playlist creation process. This reduces the amount of manual coding a developer has to do, speeding up the workflow.

When AI Falls Short

Interestingly, in this example, AI is most useful for the dynamic steps, such as learning the API concepts and writing specific use case code. But when it comes to writing the access code—authentication, headers, and HTTP methods—AI’s assistance is less necessary. This is because these steps are fairly static. Once you’ve set up OAuth authentication, the process is generally the same across different use cases. As such, SDKs and templates already handle most of the repetitive work.

The reason AI is less effective here is that these static components don’t vary much between use cases. Whether you’re building a playlist or adding songs, the authentication flow remains the same. This is where traditional SDKs excel—they provide a stable, static way to handle repetitive tasks.

Solving AI Hallucinations with Determinism

One of the biggest challenges in AI-generated code is hallucinations, but this problem can be mitigated through a process called deterministic training. By training AI models on a specific, controlled machine or dataset, we can reduce the likelihood of hallucinations. Instead of relying on potentially unreliable sources of information, the AI can be trained to say, “I don’t know” or “I couldn’t find this information,” rather than fabricating a plausible-sounding but incorrect answer.

For API-related tasks, this means that AI could be trained on a specific set of API documentation, producing more accurate code. If the AI doesn’t find an appropriate method or library, it won’t try to generate something that doesn’t exist.

This deterministic approach is a critical step forward in improving the reliability of AI-generated code. While it won’t solve all the issues, it can reduce the most egregious problems, like hallucinations.

Conclusion

AI-generated code presents both incredible opportunities and serious challenges for developers. On one hand, it can drastically improve the API adoption process by automating repetitive tasks, guiding developers through complex workflows, and reducing the time spent writing use case code. On the other hand, issues like hallucinations, security vulnerabilities, and determinism need to be carefully managed.

By combining AI tools with traditional coding practices, developers can leverage the best of both worlds—using AI to handle dynamic, complex code generation while relying on SDKs and pre-built libraries for the static, repetitive tasks. With continued advancements in AI training techniques, such as deterministic generation, we can look forward to more reliable AI-generated code that developers can trust.