Artificial Intelligence (AI) has fundamentally changed the way we programming. AI agents can generate, optimize and even help with debugging. Yet there are some limitations that programmers have to keep in mind when working with AI.
It seems easy, but complexity brings problems
At first glance it seems as if AI can effortlessly write code. Simple functions Show page Insights And scripts are often generated without problems. But as soon as a project consists of multiple files and folders, problems arise. AI has difficulty maintaining consistency and structure in a larger code base. This can lead to problems such as missing or incorrect couplings between files and inconsistency in the implementation of functions.
Problems with order and duplication
AI agents have difficulty with the correct order of code. For example, they can place initializations at the end of a file, which causes runtime errors. In addition, AI can define multiple versions of the same class or function within a project without hesitation, which leads to conflicts and confusion.
A code platform with memory and project structure helps (but is not always reliable)
A solution for this is the use of AI code platforms that can manage memory and project structures. This helps to save consistency in complex projects. Unfortunately, these functions are not always applied consistently. This may happen that the AI loses the coherence of a project and introduces unwanted duplications or incorrect dependencies during programming.
IDE extensions are indispensable
To better manage AI-generated code, developers can use IDE extensions that monitor code correctness. Tools such as Linters, Type Checkers and Advanced Code analysis helps tools to detect and correct errors early. They form an essential addition to AI-generated code to guarantee quality and stability.
The cause of repeating errors: context and role in APIs
One of the main reasons why AI agents continue to repeat errors lies in the way AI APIs interpret. AI models need context and a clear role description to generate effective code. This means that prompts must be very complete: they must not only contain the functional requirements, but also make the expected result and the preconditions explicit.
Helping tools such as Faiss and Langchain
Products such as Faiss and Longchain Offer solutions to get AI better with context. For example, Faiss helps with the efficient search and collecting relevant code features, while Langchain helps to structure AI-generated code and maintain context within a larger project.
Conclusion: useful, but not yet independent
AI is a powerful tool for programmers and can help accelerate development processes. Yet it is not yet able to independently design and build a complete codebase without human control. Programmers must consider AI as an assistant who can automate tasks and generate ideas, but who still needs guidance and correction to achieve a good result.
Take contact On to help set up the development environment to help teams get the most out of the development environment and to be more concerned with requirements engineering and design than with debugs and writing code.
