The ultimate guide to AI code generation for businesses

With AI, anyone can now be a software developer.

However, you still need to know how to use AI to write code. It’s not enough to write a random prompt and hope for the best – this can yield nothing but useless results.

In this article, we share Artificial Intelligence code generation dos and don'ts, present the best AI code generation tools currently available on the market for various use cases, and offer 3 scenarios where you might want to opt for a custom tool for automated code generation.

Let’s get into it.

What is AI coding?

Artificial intelligence code generation refers to the use of artificial intelligence to automatically write, complete, or suggest code. Instead of manually typing out every line, developers can rely on AI-powered tools to generate code snippets, functions, or even entire programs based on natural language descriptions.

These tools are trained on vast amounts of existing code and use machine learning models to predict what you need next, making coding faster and more intuitive.

But AI-generated code isn’t just about speed. It helps reduce human error, improves code consistency, and even assists developers in learning new programming languages - needless to say, AI is reshaping software development in ways that were unimaginable just a few years ago and the tools we review below are a testament to this evolution.

How do AI code generation tools work?

AI-powered code generators aren’t magic. They are trained on massive datasets that include millions of lines of code from publicly available sources, such as open-source projects, documentation, and programming tutorials. Most of this training data comes from publicly available open-source projects. This means AI models learn a wide range of coding patterns, best practices, and syntax across different programming languages.

At its core, AI code generation works by interpreting natural language prompts provided by programmers. Developers describe the functionality they need in plain text, and AI-powered tools generate relevant code snippets, functions, or even complete scripts.

AI code generation might seem like magic, but it’s actually powered by some of the most advanced technologies in artificial intelligence. At its core, it relies on large language models (LLMs) and natural language processing (NLP) – the same technologies that enable AI chatbots and virtual assistants to understand and generate human-like text. But instead of answering questions or holding conversations, these AI models are trained to write, complete, and even improve code.

What tasks can AI code generation tools assist with?

AI assistants can help suggest chunks of code that may sometimes be perfect, but other times garbage. This depends on the tools in use as well as the quality of prompts. Therefore, at this stage, we wouldn’t advise using AI code generation tools to write entire programs. They can, however, be used for a range of programming tasks, including: 

• Code translation: converting code from one programming language to another, which is particularly useful for updating legacy systems, e.g. transforming old COBOL programs into modern Java-based applications.
• Code completion: this feature suggests the next lines of code as developers type. It also helps minimize typos and syntax errors.
• Code refactoring: analyzing existing code and identifying redundancies, inefficient loops, or outdated syntax - all to improve readability.
• Debugging: detecting errors, security vulnerabilities, and potential bugs in code before they cause issues in production. Some tools go even further and suggest fixes or make corrections automatically.
• Generating unit tests: some AI code generation tools can automatically create unit tests based on the existing codebase, which is great, because it ensures that new features and updates don’t break existing functionality.
• Automating repetitive coding tasks: generating boilerplate code, setting up configurations, or creating standardized API endpoints and more.

However, while AI-generated code continues to improve in accuracy, it isn’t flawless. The output should always be reviewed, tested, and refined by developers to ensure quality and security.

Business problems that can be solved with Artificial Intelligence coding

AI code generation is not just about writing code faster – it’s about coding smarter. If used wisely, it can speed up development, cut costs, improve software quality and future-proof it. Here’s what Artificial Intelligence code generation can help you with: 

AI code generation is not just about writing code faster – it’s about coding smarter.

Increasing productivity 

Software development involves a lot of repetitive, time-consuming tasks – writing boilerplate code, configuring settings, creating data models, and writing test cases. AI code generation tools handle these tasks, allowing developers to focus on problem-solving, software architecture, and innovation rather than tedious coding.

Reducing mental load

AI reduces cognitive overload by automating routine processes like writing and deleting debug statements or suggesting best practices for naming conventions and code commenting. This means developers can work longer with better focus, leading to more efficient coding sessions and higher-quality output.

Limiting context switching

Developers frequently switch between tasks – looking up documentation, searching for code snippets, debugging, and planning how to implement abstract ideas in code. This constant context switching slows down development and reduces efficiency.

AI-powered code assistants help consolidate these tasks into one workflow. Instead of manually searching for solutions, developers can have conversational interactions with AI, asking it to look up documentation, suggest relevant code snippets, or even test their code. Thus, AI allows developers to stay focused on higher-level problem-solving and strategic thinking, rather than getting distracted by low-level syntax details or documentation searches.

Improving code quality

AI code generation tools don’t just write code – they improve it. They analyze patterns and best practices to provide real-time suggestions for optimizing code structure, improving security, and refactoring inefficient code.

With built-in checks for common mistakes and vulnerabilities, AI-generated code is often cleaner, more efficient, and less prone to bugs. Developers can ensure their code meets industry standards while reducing the time spent on debugging and maintenance. AI also enhances readability by enforcing best practices for naming conventions and formatting, making it easier for teams to collaborate on shared codebases.

Bridging skills gaps

Learning to code – or transitioning to a new language – can be overwhelming. AI tools act as mentors, providing real-time guidance, code explanations, and step-by-step instructions to help junior developers ramp up quickly.

For new programmers, AI can:

• Generate code from natural language prompts, simplifying complex tasks.
• Provide explanations for generated code, making learning more intuitive.
• Suggest improvements for existing code, reinforcing best practices.
• Guide users through debugging and troubleshooting, reducing frustration.

Even non-technical team members can leverage AI to bridge the gap between business requirements and development. Describing features or issues in natural language allows AI-generated code to communicate with developers more effectively.

Managing code environments efficiently

Software development isn’t just about writing code – it’s about managing the environments where code runs. Developers must ensure the correct configurations, dependencies, and libraries are in place for testing and production.

Advanced AI code generation tools go beyond writing source code; they assist with:

• Automating environment setup to ensure seamless deployment.
• Managing dependencies to avoid compatibility issues.
• Streamlining the software development lifecycle, from coding to AI integration and deployment.

AI-powered tools help development teams work more efficiently while minimizing errors in production by reducing the manual effort required to maintain different environments.

Best AI code generation tools and their use cases

With so many AI-powered coding tools available, it can be tough to figure out which one fits your needs best. Some are great for autocompleting code, while others help with debugging, refactoring, or even full-function generation. Below, we’ll break down the top Artificial Intelligence code generation tools, comparing their strengths, weaknesses, and ideal use cases.

1. GitHub Copilot – best for general-purpose AI coding assistance

GitHub Copilot, powered by OpenAI’s Codex, is one of the most widely used AI coding assistants. It integrates directly into popular IDEs like VS Code and suggests code completions as you type. It’s ideal for everyday coding and significantly reduces boilerplate work.

Pros:

• Excellent autocompletion and inline code suggestions
• Works well with multiple programming languages
• Seamlessly integrates into VS Code, JetBrains, and other IDEs

Cons:

• Can generate inaccurate or insecure code without developer oversight
• Requires an active subscription

Best for:

• General-purpose AI coding assistance
• Speeding up coding with inline suggestions
• Writing functions in multiple programming languages

2. Tabnine – best for privacy-focused AI code completion

Tabnine is an AI-powered autocomplete tool that prioritizes privacy. Unlike GitHub Copilot, it allows users to run AI models locally instead of relying on cloud-based processing. It’s a great alternative for teams working with proprietary codebases.

Pros:

• Strong privacy and security controls
• Local AI models for offline coding assistance
• Fast and lightweight, improving real-time productivity

Cons:

• Limited compared to GitHub Copilot for complex code generation
• Works best for autocompletion rather than generating full functions

Best for:

• Developers concerned about data privacy
• Teams working with sensitive or proprietary code
• Lightweight AI-powered autocomplete in local environments

3. OpenAI Codex – best for generating full code blocks from natural language

Codex is the AI model behind GitHub Copilot but is also available separately for developers who want more flexibility. It can generate entire code blocks from natural language prompts and supports a wide range of programming languages.

Pros:

• Highly advanced AI capable of full function and script generation
• Works with natural language queries for intuitive coding
• Strong support for multiple languages

Cons:

• Requires API access and may involve additional costs
• Can generate inefficient or unnecessary code without guidance

Best for:

• Developers looking for AI-driven code generation beyond autocomplete
• Creating entire functions from natural language prompts
• Experimenting with AI-powered software development

4. AskCodi – best for learning and quick code snippets

Designed for developers who need quick, AI-generated code snippets rather than full projects. AskCodi works well for learning new languages, looking up syntax, and getting small pieces of code on demand.

Pros:

• Great for quickly generating and testing code snippets
• Supports multiple languages
• Helps developers learn new syntax and best practices

Cons:

• Not ideal for complex or large-scale projects
• Works best as a supplementary tool rather than a primary coding assistant

Best for:

• Learning new programming languages
• Quickly retrieving syntax and code snippets
• Developers looking for a lightweight AI coding companion

5. CodeT5 – best for NLP-based AI code generation

CodeT5 is an AI model designed specifically for code generation, completion, and summarization. It’s useful for translating natural language descriptions into actual code, making it a powerful tool for automating repetitive tasks.

Pros:

• Strong NLP capabilities for turning descriptions into code
• Works well for automated code documentation and summarization
• Can be fine-tuned for specific business use cases

Cons:

• Requires some technical expertise to integrate effectively
• Not as user-friendly as GitHub Copilot for everyday development

Best for:

• AI-powered code summarization and documentation
• Automating repetitive coding tasks
• Developers who need NLP-driven code generation

6. WPCode – best for WordPress code generation

WPCode is a specialized AI code generator built for WordPress development. It helps create custom functions, modify themes, and manage WordPress snippets without requiring deep PHP expertise.

Pros:

• Designed specifically for WordPress development
• Helps automate WordPress customization and plugin development
• Simplifies PHP, CSS, and JavaScript modifications for non-experts

Cons:

• Limited to WordPress-related coding
• Not suitable for general-purpose programming

Best for:

• WordPress developers looking to streamline coding
• Non-technical users managing custom WordPress functionality
• Automating common WordPress tasks

7. Polycoder – best open-source AI code generator

Polycoder is an open-source AI coding tool that offers greater transparency and flexibility than proprietary AI models like Codex. It’s ideal for developers who prefer a customizable AI-powered coding solution.

Pros:

• Fully open-source, offering more control and customization
• Strong performance for code completion and generation
• Ideal for research and custom AI coding projects

Cons:

• Not as refined as commercial AI coding tools
• Requires more setup and technical knowledge to use effectively

Best for:

• Developers looking for an open-source alternative to commercial AI coding tools
• AI research and experimentation
• Custom AI-powered development workflows

Best AI for code generation at a glance

For general-purpose AI coding assistance: GitHub Copilot

For privacy-focused AI coding: Tabnine

For full-function generation from text prompts: OpenAI Codex

For quick code snippets and learning: AskCodi

For NLP-driven code automation: CodeT5

For WordPress developers: WPCode

For open-source AI coding solutions: Polycoder

Each tool has its strengths and weaknesses, so the best choice depends on your specific development needs. Whether you need fast autocompletion, AI-driven debugging, or full-function code generation, there’s an AI tool that can help streamline your workflow and improve productivity.

Do AI code generation tools replace developers?

AI code generation is a game-changer for software development, but it’s not a replacement for human programmers. AI lacks creativity, deep problem-solving skills, and contextual awareness – all of which are crucial for building high-quality software.

Let’s break down why AI-generated code is a tool for developers, not a replacement.

AI lacks true problem-solving skills

AI is excellent at recognizing patterns and generating code based on past examples, but it doesn’t actually understand software architecture, user needs, or business logic the way humans do.

Developers don’t just write code – they design solutions, make strategic decisions, and troubleshoot complex issues that require critical thinking. AI can suggest code, but it can’t:

• Decide which features a product needs.
• Architect a scalable software solution.
• Understand end-user behavior and design accordingly.

Artificial intelligence coding enhances a developer’s workflow, but human judgment is required to ensure software meets business goals, user needs, and long-term scalability.

AI-generated code isn’t always reliable

AI for writing code relies on large public datasets, which means it may inherit flaws, inefficiencies, or even security risks. Without human oversight, AI-generated code can:

• Contain logical errors that don’t align with the intended functionality.
• Introduce security vulnerabilities, especially when handling sensitive data.
• Be inefficient or unnecessary, generating extra code instead of optimizing for performance.

Developers must review and refine AI-generated code to ensure it meets quality and security standards. AI is a tool that speeds up development, but it doesn’t guarantee correctness.

AI still needs human creativity

Artificial intelligence code follows existing patterns – it doesn’t create new ones. Software development isn’t just about writing functional code; it involves:

• Innovation: designing unique features and user experiences
• Optimization: finding ways to improve efficiency and performance
• Creative problem-solving: addressing unique business challenges.

AI can assist with execution, but developers drive originality and innovation in software projects.

AI lacks context awareness

AI-generated code does not inherently understand project-specific requirements, industry regulations, or long-term business goals. While artificial intelligence coding tools can suggest generic solutions, they don’t:

• Understand long-term project vision.
• Consider company-specific coding standards.
• Adapt to unique business logic without extensive human input.

This means developers still need to guide AI, provide context, and refine its output to align with business needs.

AI works best as a developer’s assistant

Rather than replacing developers, AI code generation tools enhance productivity by automating tedious coding tasks. Developers can use AI to:

• Speed up development by generating boilerplate code.
• Reduce debugging time with AI-powered error detection.
• Improve efficiency by getting real-time code suggestions.
• Learn new programming languages with AI-driven guidance.

In short, developers can focus on high-value tasks like designing, innovating, and solving complex problems when they offload repetitive work to AI.

AI code generation: dos and don’ts

How to use AI to write code the right way? Applying AI code generation tools definitely requires following several best practices. While they can accelerate development and improve efficiency, they also come with limitations that developers need to keep in mind. To get the most out of AI for writing code, here are some essential dos and don’ts.

Dos: best practices for using AI code generation

Always validate AI-generated code

AI-generated code isn’t always perfect. It can contain inefficiencies, security risks, or logic errors. Always test, debug, and refine the AI’s output before using it in production.

Use AI as a productivity booster, not a crutch

AI code generation should complement your skills, not replace critical thinking. Leverage AI to automate repetitive tasks, generate boilerplate code, and suggest solutions – but rely on your expertise for problem-solving.

Customize AI output to match your coding standards

Most AI tools generate generic code. Make sure you adapt and refine AI-generated code to align with your project’s style guide, best practices, and performance requirements.

Consider security and compliance

AI code generation tools learn from public datasets, which may contain security vulnerabilities. Always review AI-generated code for potential security risks and ensure it meets industry compliance standards.

Don’ts: Common Mistakes to Avoid

Don’t blindly trust AI-generated code

AI makes mistakes, and its suggestions aren’t always optimal. Never copy-paste AI-generated code into production without manual review and testing.

Don’t rely on AI for complex problem-solving

AI can generate code, but it doesn’t understand the bigger picture. For tasks requiring deep logic, architecture design, or critical security considerations, human oversight is essential.

Don’t ignore ethical and legal implications

Some AI-generated code may be influenced by copyrighted material from open-source projects. Always verify licensing terms and ensure compliance when using AI-generated code in proprietary software.

Don’t assume AI will always follow best practices

AI models are trained on vast datasets, but they don’t always produce optimized, efficient, or scalable code. Developers should still refactor, optimize, and document AI-generated code where necessary.

Don’t overuse AI without understanding the code

Relying too much on AI without understanding the generated code can lead to technical debt. Developers should use AI as a tool to enhance learning and efficiency, not replace the fundamental skills required for software development.

When to build a custom AI code generation tool?

While off-the-shelf AI code generation tools we described earlier work well for general coding assistance, they may not be the best fit for every business. If your organization has specific coding requirements, security concerns, or workflow challenges, building a custom AI-powered coding assistant could be a smarter investment.

Here are key scenarios where a custom AI code generation tool makes the most sense.

You have strict security and compliance requirements

If your business deals with sensitive data, proprietary code, or industry regulations, using a third-party AI coding tool could pose security risks. A custom-built AI model ensures:

• Your codebase remains private and isn’t shared with external AI providers.
• Compliance with industry regulations like GDPR, HIPAA, or SOC 2.
• AI-generated code doesn’t introduce security vulnerabilities from public datasets.

When to consider a custom tool:

• You work in finance, healthcare, government, or cybersecurity with strict data policies.
• Your organization can’t use cloud-based AI tools due to compliance restrictions.

You need AI to follow your specific coding standards

Generic AI code generation tools are trained on publicly available code, which means their output may not align with your company’s coding guidelines, frameworks, or best practices. A custom AI model allows you to:

• Train the AI on your internal codebase to ensure consistency.
• Adapt AI-generated code to your team’s preferred style, naming conventions, and architecture.
• Reduce time spent on refactoring AI-generated code to fit company standards.

When to consider a custom tool:

• Your team follows strict internal development guidelines.
• You work with specialized frameworks or unique architectures that off-the-shelf AI tools don’t understand.

You want deeper integration with your development workflow

Many AI-powered coding tools function as standalone assistants, but a custom-built solution can be integrated directly into your existing development environment, CI/CD pipeline, or internal tools. This ensures a seamless and automated coding process tailored to your needs.

A custom AI code generator can:

• Automatically generate unit tests, documentation, or security scans alongside code suggestions.
• Work within your proprietary dev environment, IDE, or in-house software.
• Assist with code reviews and debugging based on your internal processes.

When to consider a custom tool:

• Your team relies on highly customized development workflows.
• You want AI-generated code to integrate directly into your CI/CD and deployment pipelines.

Your business needs AI for a niche use case

Most AI coding tools are built for general-purpose programming, but specialized industries and applications often require custom AI models. Here are some niche applications of AI for writing code: 

• AI code generation for embedded systems and IoT, e.g. a company designing smart home devices could use AI to auto-generate firmware for new sensors, ensuring faster deployment without manually writing thousands of lines of C code.
• Programming for scientific computing and data analysis, e.g. a pharmaceutical company could use AI to auto-generate bioinformatics scripts for analyzing genetic data, saving researchers hours of manual coding.
• AI-powered code generation for smart contracts and blockchain, e.g. a fintech startup could use AI to automate token contract creation for new cryptocurrency projects while ensuring security best practices.
• Game development, e.g. a game studio could use AI to generate procedural level layouts or NPC behaviors, allowing developers to focus on gameplay mechanics.
• AI code generation for legacy system modernization, e.g. a bank still running COBOL applications could use AI to automatically convert critical financial processing scripts into Python, reducing modernization costs.
• AI-assisted code generation for legal and compliance automation, e.g. a law firm could use AI to generate standard legal contract templates and auto-fill them based on client-specific data.
• AI coding industrial automation and robotics, e.g. a manufacturing company could use AI to generate real-time robotic arm control scripts, improving production line efficiency.

You want to future-proof your development process

AI-powered development is evolving rapidly, and businesses investing in custom AI solutions now will have a competitive advantage. Instead of relying on third-party AI tools that may not scale with your company’s growth, a custom AI model ensures:

• Long-term adaptability as your codebase, technologies, and team evolve.
• Continuous improvement by fine-tuning AI models on internal projects.
• Competitive differentiation by leveraging AI in ways competitors can’t.

When to consider a custom tool:

• You want an AI-powered coding assistant that grows with your business.
• You need a scalable AI model that can be continuously trained and refined over time.

Why work with us to build your custom AI code generation tool?

AI-powered code generation is transforming software development, but off-the-shelf solutions don’t always fit every business’s needs. That’s where we come in. Here’s what we can bring to the table: 

Expertise in AI and custom software development

Developing a powerful AI code generation tool requires more than just AI models – it demands deep expertise in software engineering, machine learning, and workflow automation. Our team has:

• Experience in AI-driven software solutions tailored to business needs.
• Strong knowledge of large language models (LLMs) and natural language processing (NLP). We even conduct our own experiments with AI, like our AI Freight Forwarder project, which helped us determine the criteria for choosing the best LLM model for various business use cases. 
• Proven success in integrating AI into real-world development workflows.

We don’t just build AI – we create scalable, efficient, and business-ready AI coding solutions that deliver real results.

Full focus on security & compliance

We develop AI solutions with:

• On-premise or private cloud deployment to protect sensitive code.
• AI models trained on your proprietary data, ensuring security and relevance.
• Compliance with industry regulations like GDPR, HIPAA, SOC 2, and more.

Seamless integration with your development workflow

One of the biggest challenges with AI coding tools is making them fit into existing development processes. Our solutions are:

• Fully customizable, working within your existing IDEs, repositories, and DevOps pipelines.
• Designed to automate coding, testing, and deployment to improve efficiency.
• Built to support real-time collaboration between developers and AI.

Ongoing support and continuous improvement

AI models improve over time, and we ensure your AI code generation tool stays ahead of the curve. We provide:

• Continuous AI training and fine-tuning based on your evolving needs.
• Ongoing support and updates to enhance performance.
• Custom feature development, so your AI tool grows with your business.

Instead of a one-time solution, you gain a long-term partnership that keeps your tool up to date at all times.

Let’s build the future of AI-driven development together

If you’re looking for an AI code generation tool that is secure, customized, and built specifically for your business, we can help. Our team specializes in AI-powered software solutions that enhance productivity, improve code quality, and seamlessly integrate into your existing workflows.

Get in touch today to explore how we can build a custom AI code generation tool tailored to your needs!