ChatGPT vs. Tabnine: Why AI code assistants are so much more than LLMs 

AI is everywhere. Some form of AI-powered assistance is available for performing just about every life task you can think of: from planning vacations to designing a personalized workout regimen to composing a message for your mom’s birthday card. For these use cases, the chat interface to any generative AI model you utilize will typically return a reasonably decent result.

However, it turns out that many of us are also using AI to help us create, test, and deploy software — a situation where your choice of AI tools matters. A lot.

Why should you choose a dedicated AI code assistant like Tabnine over a large language model (LLM) like ChatGPT? Here are five important ways the two are different when it comes to writing (also debugging, updating, testing, and documenting) code.

Prompt engineering vs. intelligent agents

Any interactive AI chatbot (like ChatGPT) rests atop one or more LLMs. LLMs are the core of AI: programs that use deep learning to analyze and understand huge amounts of data. LLMs are trained on vast datasets, typically millions of gigabytes of text from across the internet, to recognize, generate, and interpret human language.

Because LLMs are not specifically trained or optimized for coding tasks, getting good results requires a lot of context and additional information for writing code that will do a task properly. Developers must master the art of prompt engineering to coax forth the code they need. The user types in what they think is the right prompt query into ChatGPT, and then continues reprompting the LLM to eventually arrive at an accurate answer (unless they give up and just manually correct the code themselves).

Code assistants like Tabnine, however, enhance whatever the user asks inside of the chat interface by providing additional prompt information and context to generate a better outcome. The benefit is that the user can simply focus on the task and the “ask” and allow the AI code assistant to close the gap.

In addition, AI code assistants have built-in AI agents, which are a series of complex prompts and contexts that are stitched together to solve more specific problems, like a testing agent for creating unit tests or an onboarding agent that helps a developer that is new to a project get up to speed very quickly.

Code context and personalization

The core difference between LLMs like ChatGPT and dedicated AI code assistants can be summed up in one word: context.

When you ask ChatGPT for a code suggestion, the answer it returns is drawn from an LLM containing every imaginable kind of data available on the internet. What it’s not drawn from is your code. Because ChatGPT has zero awareness of anything outside the browser box, it can only return generic results. The AI has no context to understand whatever you’re working on. As a result, GPT requires you to include your own context and a lot of additional information in your prompts before it can return something even close to what you need.

LLMs are great at generating boilerplate code because no context is required. This type of code is so common that any model will have abundantly trained on endless examples. LLMs can also be good at generating small code improvements because you’re providing the context when you feed it the chunk of code you want it to work from.

What LLMs are not good at is giving anything better than generic, textbook-appropriate responses — versus an answer that matches your company or your team’s approach to solving problems. This happens because the LLM lacks the context to make suggestions that are specific to your code and your logic.

An example of this would be asking ChatGPT how to write a function to solve a specific problem. Without codebase awareness, it’s likely to give you a generic answer. With codebase awareness, though, an AI code assistant will reference the specific APIs or methods used within your company.

Tabnine, for example, can return personalized real-time results that are specific to your current work and right in your IDE because it has local context: awareness of the code available locally from your machine. This includes data from your IDE for information like variable types, comments you’ve added, open files you’ve interacted with, any imported packages and libraries, other open projects, and more. Tabnine also uses global context to return highly specific answers because, unlike ChatGPT, the tool can be connected with organization-level sources of information (like code repositories, design documents, Jira tickets, and more) to generate recommendations more aligned with a team’s way of working.

The other major advantage of an AI code assistant over an LLM is that you can ask questions about your codebase — and not just general coding questions. Instead of “how to” questions, you can ask “how do we” and “where do we” questions.

Customized coding models

ChatGPT’s interface does make it very easy to ask for a function or code block, and you can then copy/paste whatever it returns into your IDE. The problem, though, is that ChatGPT’s answer is drawn from every imaginable kind of data available on the internet. Functions, funny limericks, food ideas for a birthday party — it’s all the same to ChatGPT because its underlying LLM, GPT, was designed for general-purpose processing tasks.

Tabnine, however, is designed specifically for programming tasks. Tabnine runs on its own proprietary models, one for code generation and one to power the code-oriented natural language chat agent. Tabnine’s LLMs are exclusively trained on code from credible open source repositories with permissive licensing. The baseline quality of the code it returns is substantially better because Tabnine’s AI was explicitly fine-tuned for code.

Public models simply cannot match the benefits of having models built around a discrete set of ultra–high-quality training data — including custom models trained on your specific codebases — the way a code assistant can. Furthermore, code assistants have deep and specific knowledge of languages and frameworks that are weak in public models due to the lack of public training data.

Also: Although ChatGPT is limited to a single LLM (it can only ever query GPT models), Tabnine lets users choose to add additional LLMs for the code assistant to draw from (including, actually, GPT, plus Claude, Mistral, Cohere, and others).

Integration and workflow

Probably the biggest advantage of working with ChatGPT is how easy it is to plug prompts into its browser interface. It gets more complicated after that, though, as you need to keep copying and pasting snippets back and forth between the site and your IDE. (There are ways to incorporate ChatGPT into your IDE, although these are cumbersome and highly manual to implement while also being nowhere near as powerful as running a code-specific AI assistant).

Tabnine, on the other hand, works locally and inside your IDE, where it can act on your code directly. Because it has this tight integration with your IDE, Tabnine’s generative inline code suggestions, autocompletions, and responses to prompts as comments are real time and, more importantly, highly accurate and context appropriate.

Tabnine also has an AI chat agent that integrates into your IDE along with its generative and code completion functions. You can type into a chat box using natural language and ask the agent to, for example, explain some code, have a back-and-forth conversation to work through some logic, or help refine a more comprehensive prompt — any topic or issue you would search for on Stack Overflow or even chat about with a coworker.

Tabnine is far less disruptive to workflow because you never have to pop out of your IDE to a browser to query ChatGPT or any other source — everything you need is right there.

Privacy and security

When you use ChatGPT, OpenAI collects data from your interactions with ChatGPT and uses your data to continue training their LLMs:

We retain certain data from your interactions with us, but we take steps to reduce the amount of personal information in our training datasets before they are used to improve and train our models. This data helps us better understand user needs and preferences, allowing our model to become more efficient over time.

ChatGPT saves everything — not only your prompts and the AI’s responses, but also geolocation data, network activity, and what device you’re using. Oh yeah, and your email address and phone number. According to Open AI’s privacy policy, this data is used to train the LLM and improve its responses, but the terms also allow the sharing of your personal information with affiliates, vendors, service providers, and law enforcement.

It’s not just ChatGPT — many (if not most) generative AI tools, LLMs, and chat assistants alike, retain your data for their model to train on. Buried deep within their terms of use is language specifying that they can use your code, data, and behaviors to feed their platform’s models, making your information available to anyone (and everyone) using that platform.

Even if any code you share with ChatGPT (to provide it with context, as above) does not contain sensitive information, it can still contain some knowledge that you may not want to share with others. Many developers follow the guideline, “If you’d not be comfortable posting something on Stack Overflow, don’t paste it into an unsecured LLM.” Unfortunately, that advice also applies to some AI code assistants currently in the market. It’s also worth noting that sharing code with any LLM may be a violation of your corporate policies or your employment agreements (or contracts, if doing work for others).

ChatGPT does offer the ability to opt out of their default opt-in data sharing and retention settings, but doing this comes at a cost. Limiting the LLM’s data collection reduces ChatGPT’s functionality because it will not “remember” anything from your previous chats — so the code suggestions it returns will devoid of context. Any results will now come from a limited set of generic algorithms, resulting in even less accuracy and relevance than the already limited code personalization ChatGPT offers when you completely share all your data.

Tabnine, on the other hand, is built by people who understand all the reasons why developers aren’t comfortable sharing their data externally. Tabnine’s code assistant never retains any code: requests are only ephemerally processed to provide coding suggestions, and then immediately discarded. Any data transmitted between the user’s machine and Tabnine servers is encrypted, securing against eavesdropping or attacks.

Finally, LLMs (and some other code assistance tools) use third-party APIs or models to deliver their services. Tabnine, however, uses proprietary models (constructed with knowledge gained over more than a decade of working in generative AI) so there’s no risk of your code being shared.

Another privacy and security feature not available from LLMs is the ability to control deployment location. If your company policy prefers or requires private deployments, Tabnine can be deployed on-premises for you to maximize control; deployed as single-tenant SaaS for convenience; or deployed on a VPC as a balance of the two. If it’s truly critical for your data to never leave your perimeter, ever, the assistant also supports fully air-gapped deployments where there’s no network path outside your environment.

Why AI code assistants are so much more than LLMs

For software engineers, choosing a dedicated AI code assistant like Tabnine over a general-purpose LLM such as ChatGPT is the difference between writing code in a text editor and pushing it to a live prod environment, versus using an IDE and a VCS. Both allow you to get the job completed, but only one approach is tailored to how you work. AI code assistants like Tabnine are enterprise-grade tools designed exclusively for software development tasks, while ChatGPT is a general-purpose chat bot designed to answer a broad variety of questions. Because Tabnine leverages fine-tuned models and can tap into the deep context of your code and requirements, the AI is able to return higher quality and more relevant code suggestions.

While ChatGPT can provide code snippets based on broad knowledge, the LLM doesn’t know anything about your code. ChatGPT operates without awareness of the specific project or codebase a developer is working on, leading to more generic suggestions that require significant user input and can require major correcting.

Tabnine specifically uses sophisticated and nuanced context based on both local and global awareness of your code and company standards to return highly personalized code recommendations that are the opposite of generic. And Tabnine’s tight integration also ensures a smoother workflow, since developers can receive real-time assistance with their coding questions without leaving their coding environment.

Privacy also differs radically between the two. Coding with an LLM like ChatGPT requires feeding in your own, potentially proprietary, code for it to work from — prompts and associated code it can then retain and use for training its models. Tabnine, on the other hand, puts you in total control. Your code is never stored, never used as training data, and never shared with external applications or services.

Specialization. Personalization. Integration. Privacy. These are the five pillars of Tabnine’s AI coding assistant — and they’re also five things that ChatGPT and all other LLMs simply do not, and cannot, give to developers.