What Are AI Code Generators? AI code generators are used by software developers to analyze, suggest, and write code. They interpret natural-language prompts, then generate blocks of code intended to answer those prompts. These tools aim to accelerate developer productivity by automating repetitive or tedious code tasks, generating first-draft code for iteration and refinement, and suggesting improvements and optimizations for existing code. AI code generators are typically based on large ...
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AI code generators are used by software developers to analyze, suggest, and write code. They interpret natural-language prompts, then generate blocks of code intended to answer those prompts. These tools aim to accelerate developer productivity by automating repetitive or tedious code tasks, generating first-draft code for iteration and refinement, and suggesting improvements and optimizations for existing code.
AI code generators are typically based on large language models (LLMs) that have been trained on large datasets of language and code. As a result, these tools can interpret and generate a combination of natural-language instructions and code. Most tools are general-purpose, but some AI code generation tools are tailored to specific use cases, such as game development or web development.
Some AI code generation platforms can be further trained on an existing codebase to increase their accuracy and usefulness. Other products can also analyze a codebase to enable natural-language search, highlight code with security vulnerabilities, or flag code that might require an open-source license.
Most products in the AI code generation tool category have the following features:
Before purchasing an AI code generation tool, consider these important factors:
Developer Input: Most AI code generation tools do not claim to generate 100% accurate, precise code on their own. Developers will still need to analyze, interpret, debug, and iterate on the code generated by these tools. Non-developer stakeholders should understand that while these tools can increase developer productivity, they are not a replacement for human developer expertise.
Specialized Use Cases: Some AI code generation tools are optimized for specific use cases. For example, Amazon CodeWhisperer is a general-usage product, but it’s also tuned specifically to generate API code for other AWS products. If your organization is heavily invested in a specific platform, you could get more value from an AI code generation tool optimized for that platform. Otherwise, a more platform-agnostic tool might be a better fit.
Customizability: While most AI code generation tools can match the basic style and conventions of your codebase, some tools facilitate more thorough customization. They might support training the model on your specific codebase, tuning the underlying model’s parameters, or embedding specific best practices or coding standards into the model. If your organization has a particularly extensive codebase or strict code standards, it might be a good idea to pick a highly customizable product. This customization might not be straightforward, though, so make sure your team is prepared to invest the necessary time and effort.
AI code generation tools are generally priced on a tiered per-user per-month subscription model. Pricing can typically range from $10 to $25 per user per month, with many vendors offering free versions of their product for individual users. Some platforms offer custom quotes for enterprise customers. Lower-priced tiers typically include core functionality like manual, prompt-based code generation and autocompletion. Features like model tuning and security scanning are often restricted to higher tiers and those targeted at enterprises.
AI code generation lowers the barrier to developing applications by applying AI algorithms to understand natural language requests and create source code to meet a prompt’s requirements. In other words, AI Code Generators can turn the following prompt input into source code that (should) function properly:
This makes coding more accessible to those more familiar with writing in natural language, and the AI can generate corresponding code in a chosen programming language.
Current AI coding assistants are typically powered by LLMs, which start by training on massive datasets of both natural language about code (such as documentation, Stack Overflow threads, etc.) and source code (usually open-source repositories like GitHub). This training phase helps AI models understand important aspects like structure, syntax, and patterns commonly found in specific programming languages.
When in use, these LLMs start by analyzing a user's prompt input, breaking it into tokens to “understand” each component. Similar to how broader generative AI works, AI Gode Generators then use probabilities to predict the next word, command, or punctuation based on context (both from the prompt and the previously-generated tokens), until the full code is generated. These models know what valid code should look like based on the huge amounts of training data, and also gravitate towards common function patterns to keep their code legible.
Besides lowering the barrier to entry because inputs can rely on natural language instead of deep technical knowledge of commands, structure, and syntax, AI code generation comes with several key advantages. Even for experienced programmers, these tools enable much faster prototyping by quickly generating boilerplate code or scaffolding code, reducing the time to create a working product. AI Code Generators also handle repetitive coding tasks (like getters/setters, API integrations, etc.) to reduce context switching from the human developer.
When used in larger teams or across complex projects, these systems can maintain a more consistent structure, as well as accelerate onboarding more junior developers. Lastly, AI coding tools can be incredibly valuable for translating from one language to another. For example, taking an existing system written in Python and converting the entire source code to Java without losing functionality.
