What Is Code Completion? 

Code completion, also known as autocompletion, is a feature offered by many integrated development environments (IDEs) and text editors. It’s a programming tool that suggests and completes code snippets as you type, based on a thorough understanding of the programming language’s syntax and semantics. The suggestions provided by code completion are contextually relevant, meaning they are based on the code you’ve already written and the current context of your project.

This tool is not just about filling in the blanks. Code completion can suggest variable names, functions, classes, methods, and even entire code blocks, depending on the sophistication of the tool. It’s like having a knowledgeable coding partner who can instantly provide you with a list of possible options at any point in your coding process. This feature can save you time, reduce errors, and increase your overall coding efficiency.

But code completion does more than just save you a few keystrokes. It also guides you through the syntax and intricacies of a programming language, acting as an on-the-fly reference and guide. This feature can be particularly helpful when you’re learning a new language or working with a large and complex codebase. By showing you the correct syntax and suggesting appropriate code elements, code completion can significantly speed up your learning curve and improve your coding accuracy.

Code completion is not new, but was recently supercharged by the introduction of generative AI. In this article, we’ll review the traditional capabilities of code completion, and how it is providing new and advanced functionality by leveraging large language models (LLMs).

This is part of a series of articles about AI tools for developers.

How Are Large Language Models (LLMs) Revolutionizing Code Completion?

Large language models (LLMs) are taking the concept of code completion to a whole new level. These models, developed using advanced machine learning techniques, are capable of understanding natural language inputs and predicting complex code patterns. Instead of merely suggesting the next word or statement in your code, LLMs can generate entire functions or classes based on a given prompt.

Benefits of LLMs for code completion

One of the main advantages of using LLMs in code completion is their ability to understand and generate human-like code. They’re trained on a vast corpus of code data from different programming languages, and therefore they’re familiar with a wide array of coding styles, idioms, and best practices. This wide-ranging knowledge allows them to generate suggestions that are not only syntactically correct but also semantically meaningful, thereby enhancing the readability and maintainability of your code.

LLMs also have the potential to revolutionize the way we learn to code. As they are capable of generating human-readable code from natural language prompts, they can be used as a teaching tool, helping beginners understand how a specific coding task is typically implemented. They can provide real-time, context-based feedback, making the learning process more interactive and engaging.

Moreover, LLMs can significantly improve the developer’s productivity by offering high-level code suggestions. Instead of typing out every single line of code, developers can provide a brief description of the function they want to implement, and the LLM can generate the corresponding code. This can be a massive time-saver, especially when working on large, complex projects.

Concerns with the use of LLMs for code completion

While LLMs are incredibly useful for coding tasks, they also introduce new risks. LLMs can generate code that is not of sufficiently high quality, contains security vulnerabilities, or does not meet the requirements of the organization or the specific project. They are also prone to ‘hallucinations’, meaning that they might create code that appears valid but contains errors or inaccuracies. 

This makes it important to carefully review the output of LLM-based code completions by knowledgeable developers who can identify quality or security flaws and remediate them.

Benefits of Code Completion in Software Development 

Increased Efficiency and Productivity

One of the most significant benefits of code completion is the increase in efficiency and productivity it can bring to your coding process. By suggesting accurate and contextually relevant code snippets as you type, it can significantly reduce the time you spend typing and thinking about the correct syntax.

Moreover, code completion can also improve your productivity by reducing the time you spend looking up syntax or navigating through your codebase. With a good code completion tool, you won’t need to interrupt your coding flow to look up a function name or variable type—the tool will provide these details for you.

Reduction in Coding Errors

Code completion can also significantly reduce the number of errors in your code. By providing accurate and contextually relevant suggestions, it can help you avoid common mistakes such as typos, incorrect syntax, or forgetting to close a code block. This reduction in errors can save you valuable time in debugging and testing, leading to faster and more reliable software development.

Ease of Learning New Programming Languages

If you’re learning a new programming language, code completion can be a valuable tool. By providing accurate and contextually relevant suggestions, it can guide you through the syntax and semantics of the new language, acting as an on-the-fly reference. This guidance can speed up your learning process and help you avoid common beginner mistakes.

Supporting Code Readability and Maintenance

Code completion can also support code readability and maintenance. By suggesting accurate and contextually relevant code snippets, it can help you write cleaner and more consistent code. This consistency can make your code easier to read and understand, making it easier to maintain and update in the future.

Types of Code Completion 

Basic Code Completion

Basic code completion provides suggestions based on the current context. This type of code completion typically suggests variable names, function names, and other code elements that match the current context. Basic code completion is a common feature in many IDEs and text editors and can be a great time-saver when coding.

Smart Code Completion

Smart code completion not only suggests code elements based on the current context but analyzes your code more deeply to provide more intelligent suggestions. For example, it might suggest the most likely methods for an object based on its type, or it might suggest the most commonly used variables in your code or an entire codebase. Smart code completion can significantly improve your coding efficiency and accuracy.

Statement Completion

Statement completion is a more advanced form of code completion. It goes beyond suggesting individual code elements and can suggest entire code statements or blocks. For example, if you’re writing a for loop, a statement completion tool might suggest the entire loop syntax, saving you valuable time and reducing the risk of errors.

Code Completion Based on Natural-Language Prompts

This approach leverages the advancements in natural language processing (NLP) and large language models (LLMs) to bridge the gap between human language and code. This type of code completion can be particularly useful for beginners who may not yet be familiar with the syntax and semantics of a programming language. By describing what they want in natural language, they can still generate valid and functional code.

Moreover, code completion based on natural language prompts can also be beneficial for experienced developers. It can save them time, as they can generate boilerplate code and routine functions with a simple description. This type of code completion can also help developers explore different ways of implementing a functionality.

Code Completion Technologies 

Syntax-Based Code Completion

Syntax-based code completion is the most basic type of code completion technology. It works by suggesting code snippets that match the syntax of the programming language you are using. 

However, syntax-based code completion has its limitations. It can only suggest code snippets based on the syntax of the language and cannot understand the context of the code. This is where the other types of code completion technologies come in.

Context-Based Code Completion

Context-based code completion provides code suggestions based on the context of your code. This means that it takes into account the variables, functions, and classes that you have already defined in your code.

Context-based code completion can provide more accurate suggestions, yet it cannot learn from your coding habits or suggest code snippets based on patterns in your code.

Predictive Code Completion

Predictive code completion uses machine learning algorithms to learn from your coding habits and suggest code snippets based on patterns in your code. This type of code completion can understand the context of your code, learn from your coding habits, and even predict what you are likely to type next.

Code Completion Based on Large Language Models (LLM)

LLM-based code completion tools are trained on a vast amount of code, which enables them to generate code snippets that are not only syntactically correct but also contextually appropriate. These tools can understand the broader context of your code, taking into account factors such as variable types, function calls, and even coding style.

One of the key benefits of LLM-based code completion is its ability to generate whole blocks of code from a brief description. By using natural language processing, these tools can translate a description into a fully functional code snippet.

3 Notable AI-Powered Code Completion Tools 

Now that we have a better understanding of code completion technologies, let’s take a look at 3 AI-powered code completion tools that are worth evaluating.

GitHub Copilot

GitHub Copilot, powered by OpenAI’s Codex model, is designed to work alongside you as you code, making intelligent suggestions as you type.

Whether you’re writing in Python, JavaScript, TypeScript, Ruby, or any other popular programming language, Copilot is designed to understand your context and suggest relevant, syntactically correct code.

Moreover, GitHub Copilot can also generate entire functions based on your comments, making it an invaluable tool for rapid prototyping and exploratory programming.

Visual Studio IntelliCode

Visual Studio IntelliCode is a code completion tool developed by Microsoft. Like GitHub Copilot, IntelliCode uses AI to predict and complete code as you type. However, IntelliCode takes a slightly different approach, leveraging the collective knowledge of the developer community to make its predictions.

Visual Studio IntelliCode stands out in its ability to learn from your code. It uses machine learning algorithms to analyze patterns in your code and make personalized suggestions. This means that the more you use IntelliCode, the better it gets at understanding your coding style and making relevant suggestions.

IntelliCode can also help improve code quality. The tool not only suggests completions but also flags potential issues, helping you to write cleaner, more efficient code.


Tabnine is a code completion tool powered by GPT-3, OpenAI’s previous-generation large language model. Like the other tools on this list, Tabnine uses AI to predict and complete code snippets as you type.

What sets Tabnine apart is its deep contextual understanding. Whether you’re working in Python, JavaScript, C++, or any other popular programming language, Tabnine is designed to understand your code and make contextually relevant suggestions.

In addition to its code completion capabilities, Tabnine also offers a range of other features, including code generation, code translation, and error detection.

Automating Code Documentation with Swimm

While AI-powered code completion tools are incredibly potent, their suggestions still require scrutiny from experienced developers to ensure quality and security. This is where products like Swimm come into play. Swimm’s code-coupled documentation feature helps teams ensure that the code generated through these advanced tools aligns with organizational standards and best practices.

 Whether you’re a distinguished engineer, an entry-level developer, or a VP focused on team efficiency, Swimm provides you with the documentation and learning modules needed to understand, review, and integrate AI-generated code effectively into your projects. By acting as a knowledge-sharing layer that complements advanced code completion technologies, Swimm elevates the coding experience, making it more efficient, secure, and educational.

In a landscape where AI is setting new rules for software development, a tool like Swimm serves as the bridge between cutting-edge technology and the human expertise that remains crucial for crafting high-quality software.

Learn more about Swimm