What Are AI Coding Assistants? 

AI coding assistants are a type of software development tool intended to make coding processes faster and easier. These assistants rely on artificial intelligence to help developers generate, fix, or adjust code. They usually integrate with popular IDEs (development environments) and work in real time. 

A major function of AI coding assistants is to offer suggestions for code completion, which is possible given their ability to understand the context of code. These assistants typically use large language models (LLMs) to analyze large code repositories and identify relevant patterns, and solutions for a given code snippet or function. 

AI coding assistants are especially useful for speeding up the process of writing code through automation. Developers often use them to carry out repetitive or simple but time-consuming tasks, or to highlight issues. However, they cannot reliably replace the human input and still require some level of expertise to create accurate and safe code.

3 Advanced Tips for the Top 3 AI Coding Assistants 

Here’s a look at three of the most advanced AI coding assistants, along with some tips for how to best use these tools.

Tabnine

Tabnine is an AI code assistant that simplifies code generation and automates various development tasks. It combines code completion capabilities with a chat interface, allowing developers to build or fix code faster.

Tabnine is available through two different deployment options:

• Tabnine SaaS: In this default option, Tabnine’s server is hosted in a secure cloud, available with any Tabnine plan, including Starter, Pro, or Enterprise.

• Private installation: Here, the customer privately hosts the Tabnine server on-premises or in a virtual private network (VPC). This option is only available with Tabnine’s Enterprise plan.

Tip 1: Make sure variables and function names are descriptive 

In Tabnine, any information available to the code editor is used for context, regardless of whether it is in code format or plain text. By providing clear, meaningful names to variables and functions, developers can improve the predictive accuracy of Tabine’s AI code assistant. 

For example, having a written-out function signature makes it easier for Tabnine to understand its purpose. To name a function, simply type in the descriptive name and press Enter:

Tip 2: Makes sure you comments are in the same form they would be in the real world 

Tabnine is useful for generating code segments from comments. When writing a comment, press Enter to initiate the code generation. For example, a comment might be: “// connect to Database 1”. 

It’s not recommended to ask questions, such as: “// Q: How can I link to Mongodb?” Write simple comments as instructions and keep it in the same format or wording you would use in real-world code.

Leverage documentation lookup

Tabnine can look up documentation for libraries and functions. Developers can click on a given suggestion and press the relevant combination of keys to access the documentation. It should appear in the code editor.

GitHub Copilot

GitHub Copilot is another popular AI coding assistant, which helps developers write code with minimal effort. It encompassess a range of coding features and can be used to retrieve code suggestions directly to the IDE. 

The Copilot chat feature is useful for asking for assistance with code issues. Copilot also supports coding functions through the command line or via pull requests. For example, Developers can use pull requests to generate descriptions of code changes.

Tip1: Use top-level comments

A top-level comment is like a short, high-level introduction to a code file, which helps Copilot get the overarching context of a task or project. This is especially useful for creating boilerplate code, providing the required background for the AI assistant to generate relevant snippets.

It’s important for this comment to include details about the code requirements, as well as a clear description to ensure sufficient information is available. The top-level comment guides GitHub Copilot to provide more relevant or accurate suggestions, establishing a goal for the project. A good comment should include examples to provide further guidance, especially for tasks like string manipulation or data processing data.

Tip 2: Include code samples

Feeding a sample code to Copilot helps it understand what outcome the developer wants. Samples help to orient the model with additional context. They also allows GitHub Copilot to generate code suggestions that better match the programming language, style, or tasks. For example Copliot can offer suggestions based on existing coding practices and standards.

One type of sample code that is useful at the level of an individual method or function is a unit test. A more complex option is to include an end-to-end code example showing what to do throughout the entire project. Over the long term, GitHub Copilot encourages developers to implement coding best practices.

Tip 3: Keep requests relevant in inline chat

Inline chat is a built-in feature in Copilot, which allows developers to communicate with GitHub Copilot outside of the code itself. This mode is accessible by pressing CMD + I (on Linux, iOS) or CTRL + I (on Windows), allowing users to ask Copilot questions almost as if it were a human coding expert. It can be more convenient for small fixes than accessing the Copilot Chat side panel.

When using inline chat, it’s important to delete unnecessary requests. For example, previously asked questions can be deleted from the chat interface or indexed conversations. This helps keep Copilot focus and reduces unnecessary noise on the screen. It also helps improve the conversation flow, ensuring the coding assistance produces the most relevant output.

Claude

Claude is a series of next-generation AI coding assistants from Anthropic. It is the result of extensive research into training AI systems with an emphasis on helpfulness and safety. Claude models can be accessed through an API or chat interface in the developer console, ready to perform various text processing and conversational tasks. 

Claude coding assistants are useful for search, summarization, creative writing, collaboration, Q&A, and coding. Users have reported that Claude is reliable, with a low risk of generating harmful outputs. It aims to be easy to chat with and direct towards the desired output. Claude models can be personalized and adopt a specified behavior or tone.

Tip 1: Structure Claude prompts with XML tags 

Coding prompts should contain multiple components, such as context, examples, and instructions. XML tags are useful for ensuring that Claude parses these prompts accurately, providing more precise guidelines for the AI model.

Using XML tags improves prompt clarity by clearly separating different parts of each prompt. They help prevent Claude from misinterpreting any part of a prompt and improve flexibility, for example, by making it easier for the model to identify, modify, add, or remove segments parts of a prompt without having to rewrite the whole prompt.

To ensure consistency, reuse the same tag names for all prompts, and refer to these tag names in any discussions about the content. For hierarchical content, nest tags like <outer>, <inner>, </inner>, and </outer> to provide a clear structure.

Tip 2: Mange prompts with long context 

When using long-context prompts in Claude, it is easy for the model to get confused. Keep longform inputs at the top of the prompt. Any longer documents or data (containing 20,000 or more tokens) should be at the start of the prompt, before elements such as queries, examples, and instructions. This helps improve the performance of Claude models.

Another approach is to use quotes to ground responses. For tasks requiring long documents, add a request for Claude to quote the relevant parts of the documents before performing the rest of the task. This distinguishes the quoted information from the rest of the contents in a document, avoiding confusion and reducing noise.

Tip 3: Use chain-of-thought prompts

Allowing Clause to “think” about tasks helps improve its performance, especially in complex use cases like problem-solving, research, and analysis. Chain-of-thought (CoT) prompting is a technique that helps Claude break down problems into more manageable steps, resulting in higher accuracy and nuance. 

CoT techniques vary in terms of complexity, with the most complex methods typically taking up more space within the model’s context window. XML tags such as <thinking> or <answer> can help distinguish Claude’s reasoning from its ultimate output. 

For example, to write a set of emails to members of a charity organization using CoT prompts, users might input the following:

User Write personalized emails to each member asking for donations to the 2024 Canine Charity program.

Program information:

<program>{{ENTER_PROGRAM_INFORMATION}}

</program>

Member information:

<member>{{MEMBER_INFORMATION}}

</member>

Think before writing each email within <thinking> tags. Start by thinking through the messages that may appeal to the particular member based on their involvement in the charity, donation history, and the campaigns they have contributed to. Next, think through the aspects of the 2024 Canine Charity program that are most likely to appeal to them, based on this history. Lastly, write the final personalized donor email within <email> tags, based on the analysis.

Conclusion 

AI coding assistants are powerful tools for speeding up various processes involved in writing and adjusting code. However the AI-based capabilities of these tools still require human expertise to ensure accurate and useful outputs. Developers must be able to understand how their coding assistant works and interprets prompts, and use their coding experience to tweak their prompts with care and finesse.