ChatGPT has become a popular tool for coders. With its ability to understand and generate human-like text and code, it can assist in writing code snippets, debugging, and even explaining complex programming concepts.
For example, developers can use ChatGPT to generate code based on specific requirements or natural language descriptions. Beginners can ask for explanations of programming concepts or guidance on solving coding problems, making it an interactive learning tool.
However, as a general purpose tool, ChatGPT often creates code that doesn’t suit the specific requirements of a developer, or may not be in line with an organization’s coding best practices. It might generate code that isn’t secure and might raise compliance issues because it could be based on open source code that uses nonpermissive licenses.
In addition, ChatGPT is prone to hallucinations and might create code that doesn’t compile or uses nonexistent libraries or incorrect syntax.
Here are a few reasons many developers prefer using coding-specific AI assistants:
Specialized focus on programming tasks
Although ChatGPT offers broad assistance across many domains, other AI tools are designed with a focus on coding-specific tasks, providing a more tailored experience for developers. These tools understand the nuances of programming languages, making them adept at offering context-aware suggestions and solutions.
For instance, they can provide code completions that are syntactically and semantically accurate, understand coding patterns, and offer recommendations that align with software development best practices.
Integrated development environment (IDE) integration
Most coding-specific AI tools integrate with popular IDEs, streamlining the development process. This integration allows developers to access AI-powered insights and suggestions directly in their coding environment, eliminating the need to switch contexts. For example, these tools can replace the built-in autocomplete in the IDE or enable chat with AI that references code in the editor.
Real-time error checking and debugging
AI tools dedicated to coding can offer real-time error checking and suggestions for debugging, significantly reducing development time. By identifying syntax errors, logical mistakes, or inefficiencies as code is being written, they provide immediate feedback that would otherwise require manual testing.
Automated code reviews
AI tools can automate code reviews by analyzing code for readability, consistency, and adherence to best practices. They can identify complex code that may need refactoring, suggest improvements, and even flag potential performance issues. While ChatGPT can perform code reviews, specialized tools can take into account the context of an existing project or codebase and an organization’s existing coding best practices.
Tabnine is the AI code assistant that you control — helping development teams of every size use AI to accelerate and simplify the software development process without sacrificing privacy, security, or compliance. Tabnine boosts engineering velocity, code quality, and developer happiness by automating the coding workflow through AI tools customized to your team. Tabnine is trusted by more than 1 million developers across thousands of organizations.
Key features of Tabnine
Complete privacy over your code and data: Secure the integrity and confidentiality of your codebase and stay in command of how your teams use AI.
Total deployment flexibility: Tabnine is fully private and self-contained. Deploy your way: as SaaS, VPC, or on-premises. You also have the option to be fully air-gapped.
Air-gapped deployment: Engineering teams with stringent privacy and security requirements can deploy Tabnine on-premises air-gapped or VPC and reap the benefits of highly personalized AI coding performance with zero risk of code exposure, leaks, or security issues. *Enterprise only
Best-in-class AI code generation: Let Tabnine’s AI code assistant streamline AI code generation and automate mundane tasks so you can spend more time on the work you love. Get accurate and personalized code completions. Add comments and other natural language prompts in-line or via chat and Tabnine will automatically convert them into code.
Real-time code generation: As a developer writes code or comments, Tabnine makes recommendations tailored to the current coding context, previous inputs, improving productivity by up to 50% and reducing coding errors.
Block completion: Tabnine automatically completes code blocks including if/for/while/try statements based on the developer’s input and context from inside the IDE, connected code repositories, and customization/fine-tuning.
Function generation from comments: Tabnine gets context from comments inside the code. Developers can prompt Tabnine to generate full functions, unit tests, and more simply by writing a comment.
Unified interface: Unlike other code assistants, Tabnine’s AI agents all operate from a single interface within the IDE, simplifying and streamlining the developer workflow for coding using AI tools.
Work smarter with AI personalized to you: Tabnine’s AI code assistant is context-aware of your projects, requirements, codebase, and more, so it understands your applications — and the way you work. Tabnine uses progressive personalization to optimize how its AI code assistant works for your team. This happens in four ways: context, connection, constraints, and customization.
Context awareness in the IDE: Get accurate, relevant results with context awareness inside the IDE.
Connection to your code and knowledge bases: Connect your codebase, requirements, and docs to enhance performance. *Enterprise only
Coaching based on your standards: More mature and disciplined engineering teams can take this personalization even further by providing Tabnine with expert guidance which is applied in both recommendations and in code review. *Enterprise only
Customization of the underlying models: If you have a large pool of high-quality code, Tabnine can build on our existing models by incorporating your code as training data, achieving the maximum in personalization of your AI assistant. *Enterprise only
Switchable model selection: Access new state-of-the-art models in Tabnine Chat as soon as they become available. You can choose from Tabnine Protected, Tabnine + Mistral, Codestral, Command R, Claude 3, GPT-4o, GPT-4 Turbo, and GPT-3.5 Turbo. You’re not locked into any one of these models and can switch instantly between models for specific projects, use cases, or to meet the requirements of specific teams.
Future-proofed AI investment: Tabnine supports model switching and continually adds new LLMs to power AI agents in Tabnine Chat. Developers get access to multiple state-of-the-art models soon within days of them being available and all models are included for free with your subscription.
Real-time model switching: Tabnine Pro users can switch between LLMs at the click of a button to select the best model for their project or task. Tabnine Enterprise Admins can control model availability to users based on the needs of the organization, project, and user for privacy and protection.
Model selection aligned to privacy needs: Tabnine Protected offers complete data privacy and protection making it safe to use on IP-sensitive projects and codebases. It can be deployed behind your firewall on-premises air-gapped or VPC, and also has a single-tenant SaaS deployment offering.
Tabnine Protected: Tabnine’s original proprietary model delivers high performance without the risks of intellectual property violations or exposing your code and data to others.
Tabnine + Mistral: Delivers high-quality performance while still maintaining complete privacy over your code and data.
Cohere’s Command R: This model is ideal for large-scale production workloads and balances high efficiency with strong accuracy. It’s optimized for long context tasks such as retrieval augmented generation (RAG) and using external APIs and tools.
Anthropic’s Claude 3 Sonnet: The benchmarks conducted by Anthropic demonstrate that the entire Claude 3 family of models delivers increased capability in data analysis, nuanced content creation, and code generation.
Codestral by Mistral: This model is fluent in 80+ programming languages and outperformed CodeLlama, DeepSeek Coder, and Llama 3 on multiple benchmarking tests for Python, SQL, C++, Bash, Java, PHP, Typescript, and C#.
GPT-4o: The most powerful model from OpenAI is significantly faster than previous GPT models and offers a 2x speed improvement over its predecessor, GPT-4 Turbo.
Use AI-powered chat across the entire SDLC: Analyze, create, and improve your code with AI agents that support every stage of your software development life cycle:
AI project onboarding: The Code Explorer agent for Tabnine helps developers ramp up on a new project faster. For developers who are new to an organization or existing developers who are new to a project, the onboarding agent provides a comprehensive overview of key project elements, including runnable scripts, dependencies, and overall structure to help them get up to speed effortlessly.
AI workspace search: Ask Tabnine general coding questions, learn how things work in your specific project, and get solutions and references relevant to your workspace.
AI code creation: Generate new code using natural language. As you continue coding, Tabnine provides in-line code completions, offering real-time, context-aware suggestions that seamlessly blend with your coding style.
AI unit test generation: Ask Tabnine to create tests for a specific function or code in your project, and get back the actual test cases, implementation, and assertion. Tabnine can also use existing tests in your project and suggest tests that align with your project’s testing framework.
AI error resolution and bug fixing: Select code with an error and Tabnine will recommend fixes. You can use Tabnine to fix any errors identified by your tools.
AI automated documentation generation: Generate documentation for specific sections of your code to enhance readability and make it easy for other team members to understand. Tabnine gathers context from your IDE, codebase, coaching, and fine-tuning to generate documentation. For example, if you’re using a particular Java docs format, Tabnine will be automatically aware of that and generate documentation in that format.Learn more in our detailed guide about AI code documentation.
AI code explanations: Tabnine Chat can provide you with an explanation for a block of existing code, which is especially useful when reading a new codebase or reading legacy code in languages you don’t know as well. You can also use this feature to understand APIs, get help with resolving an error, or get guidance on how to best approach a task. For example, if you’re creating your first Next.js application and don’t know how to start, you can ask an AI chat agent to provide step-by-step instructions right in your IDE for setting up a new Next.js project. Learn more about using AI code explanations with Tabnine.
AI code maintenance, refactoring, and modification: In addition to writing new code, Tabnine can help you change existing code by adding functionality, refactoring, or fixing specific code. Tabnine will pull context from the model’s training data, code from other engineers in your organization’s repos, and form fine-tuning of the AI model to significantly simplify and accelerate coding tasks for existing projects.
GitHub Copilot is an AI coding assistant used by developers to enhance software development processes. It’s designed to increase productivity by offering suggestions and automating parts of the coding process.
Read our article Github Copilot vs. Tabnine to get a comprehensive look at the capabilities and features of Github Copilot and how it stacks up against Tabnine.
Key features
AI coding assistant: Functions as an AI assistant that provides real-time coding suggestions and converts natural language prompts into code based on the project’s context.
Workflow acceleration: Identifies bugs and can assist with new features by facilitating conversations about the codebase.
Collaboration tool: Serves as a collaborative tool within development teams by offering fast answers to programming queries and suggestions for code improvement.
Personalized documentation: Delivers personalized documentation answers, leveraging the organization’s knowledge base to provide specific insights.
Limitations of GitHub Copilot
Understanding and relevance: May occasionally misinterpret the developer’s intent or the context of the code, leading to irrelevant or incorrect code suggestions.
Suggestion accuracy: The accuracy of suggestions varies, and there may be instances where the generated code does not match the intended output, requiring manual correction.
Reliance and creativity: There’s a potential for developers to become overly reliant on the tool, which could impact their problem-solving skills and creativity.
Security and code quality: The tool might suggest code that introduces vulnerabilities or does not adhere to best practices, emphasizing the need for careful review of its suggestions.
Code generation relevance: In certain cases, GitHub Copilot may produce code that is not directly applicable to the current task, necessitating additional developer intervention.
3. Google Gemini
Google Gemini is a general-purpose large language model (LLM), similar in capabilities to OpenAI GPT-4, which can also be used for software development, offering code generation, debugging, and documentation capabilities.
Key features
Smart code generation: Gemini can generate code snippets and entire functions based on natural language prompts.
Context-aware debugging: Offers real-time debugging assistance by identifying syntax errors, logical issues, and inefficiencies within the code.
Automated documentation: Can generate documentation or explanations based on snippets of code, making it easier for developers to understand and maintain projects.
Integration with Google Cloud: Seamlessly integrates with Google Cloud services, making it easier to deploy and manage applications.
Limitations of Gemini
Limited language support: Gemini currently supports a narrower range of programming languages compared to other AI coding assistants.
Performance variability: The accuracy and relevance of generated code can vary, requiring manual adjustments by developers.
Dependency on Google ecosystem: Its full potential is realized when used within the Google Cloud ecosystem, which might limit its appeal to developers using other cloud services.
4. Codeium
Codeium is an AI-powered code completion tool that focuses on high-speed code generation. It’s compatible with a range of IDEs.
Key features
Speed: Codeium offers autocomplete functionality that operates at low latencies, allowing developers to maintain their flow without interruption.
Wide language support: Supports more than 70 programming languages.
Codeium chat: An AI-powered coding assistant within Codeium provides the ability to generate functions, explain code, refactor existing code, and translate code between languages.
AI suggestions: Codeium allows users to accept, reject, or modify AI-generated code.
Limitations of Codeium
Limited IDE integration: Codeium integrates with Neovim and VS Code, but does not offer a smooth experience with other popular IDEs, with users experiencing conflicts between Codeium’s suggestions and the IDE’s native language server protocol (LSP).
Learning curve for beginners: The large number of suggestions offered by Codeium can be overwhelming and difficult for new developers to understand.
Incorrect suggestions: Like many AI-based tools, Codeium is not infallible and may sometimes offer incorrect suggestions.
5. Amazon Q Developer
Amazon Q Developer is Amazon Web Service’s offering for AI-driven code generation, which provides real-time code recommendations as developers work.
Key features
Real-time code recommendations: As developers type code or comments, Amazon Q Developer offers suggestions tailored to the current coding context and previous inputs, improving productivity and reducing coding errors.
Personalized suggestions: Amazon Q Developer’s suggestions range from single-line comments to entire functions, adapting to the developer’s style and project needs.
Block completion: This feature supports the automatic completion of code blocks, such as if/for/while/try statements, based on the initial signature provided by the developer, streamlining the coding process.
Function generation from comments: By interpreting comments within the code, Amazon Q Developer can suggest the signature of a function and its complete body.
Limitations of Amazon Q Developer
Limited language support: Amazon Q Developer supports a narrower range of programming languages compared to its competitors.
Restricted IDE compatibility: Its integration is limited to VS Code, JetBrains, and select AWS IDEs, excluding widely used editors like Vim, Neovim, Emacs, and various web notebooks and IDEs.
Inconsistent code generation: The tool sometimes fails to generate useful code at expected points, produces errors like additional spaces, and suggests nonexistent methods, indicating a lack of understanding of the coding context.
Latency issues: The variability in latency, even for short suggestions, introduces uncertainty about whether a suggestion is being generated, impacting the coding workflow.
6. AskCodi
AskCodi is an AI-driven coding tool that aims to improve coding workflows. It acts as an AI coding assistant and provides a user-friendly chat interface.
Key features
Codi Chat: An AI-powered chat feature that enables developers to engage in code-related conversations.
Workbooks: Jupyter-style notebooks that offer a flexible platform for coding, testing, and documentation.
Codi integrations: Extensions for major IDEs, including Visual Studio Code, JetBrains, and Sublime Text.
Translate: An app that allows effortless translation between programming languages, facilitating cross-language development projects.
Quick suggestions: AI-driven code suggestions that can save time for repetitive tasks.
Limitations of AskCodi
Limited IDE features: AskCodi’s integration into IDEs could be expanded beyond simple chat interactions. Including more interactive and integrated IDE features could significantly enhance the developer experience.
Code quality variability: The quality of code generated by AskCodi’s AI can vary, with some outputs not meeting the high standards expected by developers.
Lack of real-time, context-aware recommendations: The tool currently does not provide real-time suggestions that are aware of the current coding context.
Separate interface for unit tests and documentation: Users have noted the lack of a dedicated interface within the IDE for creating unit tests and documentation.
7. Replit AI
Replit AI embeds an AI code generator directly within its popular cloud-based IDE. It aims to automate repetitive coding tasks, allowing developers to focus more on the creative aspects of their projects.
Key features
Integrated AI chat: Replit AI incorporates a chat-based code generator within the IDE, enabling developers to interact with the AI without the need to switch between tabs.
Autocomplete code suggestions: The tool is designed to offer quick and unobtrusive code suggestions in-line.
Generative coding: With the ability to understand plain language prompts, Replit AI can generate and improve code examples, facilitating rapid development and iteration.
Code explanation: It offers clear summaries of code, making it easier to understand complex codebases or to learn new programming concepts.
Limitations of Replit AI
Limited context awareness in some tools: The “generate,” “transform,” and “explain” functionalities seem to lack a comprehensive understanding of the project’s context, often providing generic solutions unrelated to the specific needs of the project.
Over-reliance on chat: Some users find themselves relying almost exclusively on the chat feature for its better context awareness and cross-cutting suggestions, which requires cumbersome copying and pasting of code.
Lack of integrated change review: The absence of a feature to review and accept changes through a side-by-side diff makes it harder to evaluate and incorporate AI suggestions.
Absence of a refactoring feature: The AI’s development process lacks a specific refactoring capability, which limits the ability to improve existing code with the tool.
8. Sourcegraph Cody
Cody is an AI coding assistant that provides autocomplete features, intended to significantly speed up the coding process.
AI-assisted autocomplete: Offers autocomplete suggestions for single lines or entire functions across any programming language, configuration file, or documentation.
Cody chat: An AI-powered chat feature that assists developers in navigating new projects, understanding legacy code, and tackling complex coding problems.
Integration with IDEs: Cody is available for VS Code, JetBrains IDEs, and NeoVim.
Limitations of Cody
User experience challenges: Some users have found the user interface not as intuitive as desired, noting difficulties with tasks like excluding individual repositories.
Limited by interaction depth: Cody sometimes provides general advice instead of specific code examples, requiring further prompts from the user to obtain actionable code snippets.
IDE support maturity: While Cody supports major IDEs, in many cases the integration is labeled as experimental or in beta for some environments.
Dependency on Sourcegraph: Cody’s performance and capabilities are heavily reliant on integration with Sourcegraph’s tools, which might limit its use in environments where Sourcegraph is not deployed or available.
Conclusion
While ChatGPT is a versatile and powerful tool for many coding tasks, specialized AI code assistants can offer significant advantages in terms of accuracy, integration with IDEs, and adherence to best practices. These tools are designed with a focus on coding-specific tasks, providing real-time error checking, automated code reviews, and personalized recommendations that can enhance the productivity and quality of a developer’s work. By leveraging these specialized tools, developers can streamline their workflows, reduce errors, and maintain higher standards of code quality and security.
If you’re interested in a more detailed guide to help choose the right AI software development tools for your company, we’ve got just the thing: download our new white paper, “AI Code Assistant Buyer’s Guide.” You’ll learn what to look for in an AI code assistant, what outcomes to expect, 7 evaluation criteria to consider, and much more — all backed by real-world examples and expert insights.
Unlike generic coding assistants, Tabnine is:
Private: You choose where and how to deploy Tabnine, either as a secure SaaS offering or self-hosted on-premises or in a VPC. When using Tabnine’s proprietary models, we don’t store your data, don’t share it with any third party, and don’t use your data to train our models. This ensures complete privacy and maximizes control over your intellectual property.
Personalized: Tabnine delivers an optimized experience for each developer and engineering team. You can increase Tabnine’s contextual awareness by making it aware of your environment — from a developer’s local IDE to the entire codebase — and receive highly personalized results for code completions, explanations, and documentation. Tabnine enterprise customers can further enrich the capability and quality of the output by creating a bespoke model that’s trained on their codebase.
Protected: Tabnine is built with enterprise-grade security and compliance at its core. It’s trained exclusively on open source code with permissive licenses, ensuring that you’re never exposed to legal liability. We are transparent about the data that was used to train our proprietary model and share it with customers under NDA. Additionally, we offer an IP indemnification to enterprise users for peace of mind.
