Traditional development methods—even those that promote agile practices—will seldom deliver a new application rapidly. Development can get bogged down by internal politics and resourcing challenges, and sometimes even by emerging issues that need more immediate programmer attention.

The arrival of AI code generation might help the development process go a lot faster.

Using AI to write code is now possible, and is being adopted by many large corporations as part of their development pipeline. When it comes to quickly prototyping code, identifying potential bugs within existing code, and standardizing coding practices, AI code generation can offer some considerable benefits.

To help you evaluate if AI code generation tools are right for your organization, let’s look at what AI code generation is, how it works, and the benefits and challenges associated with adopting it.

What is AI code generation?

Code generation AI leverages specialized large language models (LLMs) to automate part of the development of software code. Developers use natural language descriptions, example code snippets, and other forms of input to describe the code they need the AI to write. The process helps to streamline the software development process, providing support to developers of all skill levels, and helping to modernize legacy code or translate code from one programming language to another.

The underlying LLM will have been trained on data such as public code repositories (e.g. GitHub), developer Q&A (e.g. Stack Overflow), and API documentation, as well as large volumes of existing codebases, using deep learning algorithms and large neural networks. The result is a system that software developers can use as a coding assistant to automatically generate code.

How AI code generation works

The key steps in using AI for code generation include the following.

1. Input prompt

The code generation process begins with the input prompt. This could be something simple, such as “create a web-based login page using React,” or a more complex prompt, such as example code snippets and a full explanation of the functionality of the expected code.

2. Tokenization

The LLM will then convert the prompt into tokens, which is the process of turning the prompt into mathematical representations that the model can understand. For example, for text-based prompts, individual words or groups of words will be parsed and converted into tokens in a process known as embedding.

3. Context analysis

Next, the model will consider the prompt's context. For example, if the organization uses a development environment with specific libraries available, then previous interactions and prompts, as well as prior code generations, can be “remembered” to deliver a tailored response.

4. Inference by the AI model

Based on both the prompt and the model’s corpus of knowledge, the AI can begin to predict probable tokens—generative AI is largely a statistical method of assigning probability, after all. The goal of this stage is to predict the tokens that will result in working code that fulfils the requirements outlined in the prompt.

5. Code generation

Following the inference step, the model takes the predicted tokens and turns them into the output code. Depending on the prompt that was provided, this could be anything from just a few lines of code to complete development solutions.

6. Post-processing and user review

The final stage is based on testing, debugging, and improving the code that the model has produced. AI code is generally syntactically well-formed, but could include logic errors or inefficiencies. There is a continuous need for human oversight to ensure that the code that has been produced works, is secure, and aligns with the development guidelines of the enterprise. This is not about replacing programmers; rather, it’s about helping development teams be more productive.

What is the difference between AI code generation and code completion?

Although AI code generation and AI code completion are similar in many ways — especially if the developer is using an AI visual code generator — they are subtly different concepts. Let’s compare them below.

Prompt

AI code completion: Context from a few lines of existing code.
AI code generation: Natural language, code snippets, technical documents, or detailed descriptions.

Output

AI code completion: A few words or lines to fully complete ongoing code.
AI code generation: Longer segments such as functions, modules, or full applications.

Use case

AI code completion: Assists in writing ongoing code (e.g. syntax suggestions).
AI code generation: Generates entirely fresh standalone code based on intent.

Core technology

AI code completion: Often simpler models, but modern tools use LLMs too.
AI code generation: LLMs trained on large code databases and documentation.

Essentially, code completion works much like predicting text when writing an email, suggesting likely completions. Following the same analogy, AI code generation would be like asking a virtual assistant to write an email for you.

Use cases of AI code generation

AI code generation has become a fully functional tool in recent years. AI code assistants are already being leveraged by major international organizations to augment and accelerate the development of in-house applications, as well as in creating commercial software. Let’s look at some of the use cases for AI as a software development tool.

Product design and inventory management

Manufacturers of many different kinds of goods are using AI to generate code in different ways across their ecosystems. AI-generated code has been used to help developers create applications to manage logistics and simulate designs. For example, one manufacturer is using generative AI as a code assistant to create automation scripts and machine visualizations as part of the production process.

Energy grid optimization and predictive maintenance

The software that runs a power grid is highly complex and needs to be completely robust. Predictive maintenance empowers energy providers to proactively ensure each part of grid infrastructure is kept in working order, whilst minimizing unexpected downtime.

AI has been used to generate code for processing the data used for grid optimization, as well as code to ensure equipment is maintained in an efficient manner. AI code generation’s inherent statistical modelling capabilities can also help energy providers to predict potential issues using historic weather or health and safety data.

Software development in citizen services

Governments and local authorities have moved away from paper-based forms for essential services such as filing tax information, applying for benefits, and renewing licenses. In many cases, these old paper-based systems have been replaced with online tools.

With overstretched public sector services seeking ways to more efficiently serve the community, they are turning to AI code generation to help produce these applications—AI can streamline and speed up the end-to-end process of producing and monitoring compliant online services.

Algorithmic financial trading strategy generation

Financial institutions such as investment firms and stock trading teams use trading strategies that rely on algorithms to analyze past and current trading trends. These have traditionally been developed by hand, and tweaked over many years. With AI code generation, these same organizations can quickly prototype and test new algorithms to capitalize on emerging market conditions—with, of course, humans in the loop of the process.

Automated bioinformatics pipelines

AI code generation can help biotechnologists create and implement data pipelines to assist in analyzing genomes, and for simulating clinical trials of new drug compounds.

For example, AI can be used to quickly produce and test new code for normalizing data and performing statistical modeling. Traditionally, these kinds of applications have taken significant resources to develop, as they require a human programmer to have a deep understanding of the use case for them. AI code generation can help reduce the need for deep domain expertise by generating code patterns based on generalized programming knowledge. In more specialized settings, models fine-tuned on bioinformatics-related code and data can help further with more focused code generation.

Main benefits of AI code generation

Over time, the complexity and cost of software development have increased, whether for internal applications or for commercial software. AI code generation provides an opportunity to greatly reduce development times and lower the cost of implementing new systems or maintaining existing ones. Below, we outline some benefits of using AI to help develop code.

Speeds up prototyping: Manually prototyping code concepts can be time-consuming. By bringing AI code solutions into the development pipeline, software can be prototyped more quickly, helping to prove key concepts before investing in a full development project.
Supports learning: Teams, especially those with a number of junior developers, can use AI code generation as a learning tool. AI can provide code examples, highlight syntax errors or bugs in handwritten code, and offer advice on code optimization. This helps to bring new team members up to speed faster.
Encourages collaboration: Non-technical staff can use AI code generation tools to describe software in real-world terms, using plain language, and produce draft code that they can test. Positive results can be fed to the in-house development team as the basis for code updates or new features.
Enhances productivity: By using AI to handle often-repeated tasks, developers are freed up to work on more advanced tasks. For example, AI can be used to generate boilerplate code, update configuration files, and test scripts.
Improves accessibility: Non-technical users can leverage AI code generation to develop applications. This is especially useful in cases where an individual has specialized domain-specific knowledge, but lacks the technical skills to either produce a software specification or develop code themselves.

Challenges of AI code generation

Of course, as with any technology, there are also risks and challenges that need to be taken into consideration, including the following.

Bugs and errors

AI rarely generates production-ready code. It may create code that appears to function correctly, but does not completely fulfill the prompt requirements, or is faulty in context. AI-generated code should always be analyzed and evaluated by a professional programmer to guarantee accuracy and proper integration into the working codebase.

Lack of contextual understanding

In some cases, AI may produce code that does not adhere to the organization's needs or align with project requirements as it lacks contextual understanding. To help mitigate this, provide a more detailed prompt including working code examples, known dependencies, and required coding standards.

Over-reliance and skill atrophy

Developers may risk becoming too dependent on AI code generation tools, which could lead to a decline in skills and a failure to keep up with emerging technology trends. Consider undertaking regular learning reviews, highlighting emerging skill deficits, and encouraging programmers to review the code that AI has produced.

AI code generation best practices

Ready to get started with AI code generation in your organization? Here are a few tips for best practices.

Target the most valuable use cases and use AI code generation when it can provide the most benefit in the shortest time.
Provide detailed, rich prompts. Describe the desired design architecture, and provide examples of the expected quality of output.
Implement manual code review processes. Make sure that AI is used as an assistant, and that all AI code is checked by a capable programmer.
Document as you go; ask AI tools to generate fully documented and commented code. Additionally, set up a logical version control framework for submitting AI code to the codebase.
Limit how developers can use AI to help them in their jobs. Define which functions can use AI code and which must be hand-coded.

AI code generation has the potential to change how software is ideated, designed, and developed. Used sensibly, and by maintaining a human in the loop at all times, it can become a powerful vehicle to help development teams deliver projects more efficiently, and to be generally more productive in their work.

FAQs

Can I generate code using generative AI models?

Yes, there are already specialized generative AI large language models that have been trained to generate code in a range of languages using various development frameworks.

How to check if code is AI-generated?

Identifying AI-generated code can be challenging, as it often resembles human-written code. However, AI-generated code may exhibit patterns like unusually consistent formatting or a lack of creative problem-solving typical of human developers. These clues can help indicate that the code was produced by an AI.

Building enterprise AI agents

A comprehensive blueprint for developing scalable AI agents in regulated industries.

Download the eBook

The future of coding: AI code generation explained