Large Language Models (LLMs) are a family of generative models that can understand natural language and produce human-like responses. Modern iterations of LLMs have been trained to accomplish various tasks from writing an email, proposing an outline of a paper, and writing code. Naturally, it gave rise to LLM-based tools, especially for developers as early adopters of the copilot-style of doing things. Now, developers can save time with more advanced autocompletion, spend less time hunting through Stack Overflow to debug errors, and stop struggling to remember the right terminal commands — all thanks to LLM-based tools like Copilot, AI-enhanced IDEs, and AI-enhanced Terminal.
Retrieval Augmented Generation (RAG) has played a major role in the growth and adoption of LLM-based applications. It allows GenAI applications to gain access to new, up-to-date information not seen during model training, allowing them to counter hallucinations and compose an information-rich response.
For coding applications, RAG is used to retrieve additional context for a software application and generate relevant responses. It links the LLM to the user codebase, technical documentation, and online repositories to retrieve updated information regarding the input query. The added information allows the LLM to stay updated with code updates, design patterns, and development standards and prevents it from hallucinating.
This is an honest review of 5 AI tools that truly make developers' lives easier, helping them feel more confident and satisfied in their work.
Long story short:
So let’s dive in and discuss these popular LLM apps for developers (based purely on experience and without any bias) 🧐
Copilot also utilizes RAG to access code in your application codebase and use it as context to improve suggestions. While earlier versions of Copilot only considered code in the currently active file, later iterations use neighboring tabs and
85% of developers felt more confident in their code quality when authoring code with GitHub Copilot
The tool was developed to improve the productivity of coding-related tasks and has gained great popularity among developers. A
GitHub offers Copilot integrations with popular IDEs like VS Code and JetBrains. The copilot extensions allow users to generate code by specifying prompts or receive real-time suggestions based on their existing codebase. Users can also train a custom LLM, tailored to their writing style and adopt all best practices, by specifying a few personal repositories and fine-tuning the model. The easy integration and amazing benefits have made copilot the preference of
Dont expect its going to replace developers or any stuff like this, but it definitely will save you some time 😉
The
Each feature improves developers' productivity and enables robust and quality development.
Cursor also includes a chatbot that understands the entire codebase as context. Developers can query the bot by referencing the codebase and ask questions about the implementations and suggestions for improvements. The chatbot is integrated with
Moreover, Cursor IDE is a fork of the popular VS Code IDE and has a similar intuitive UI and configurations. It also allows VS Code users to
There is a constant twitter (X) battle which one Copilot or Cursor is better, and both sides have strong opinion. For example, Andrej Karpathy recently became a fan of the second one.
Moreover, Tabnine boasts unique features that distinguish it from Github Copilot. One of its primary features is the focus on data privacy and IP protection. Tabnine's protected model is trained exclusively on
Tabnine’s productivity features and focus on data security have gained massive popularity and more than
Warp is a terminal for CLI (Command Line Interface) tasks. It provides an IDE-like interface with flexible cursor movement and multi-line edit support. It also has smart command completion that recognizes what the developer is typing and offers suggestions for time-saving.
The most interesting feature of the Warp is
In terms of data privacy, Warp has a no-retention policy. Any commands or chats generated by the user are not used for training and are not retained either by Warp or OpenAI. Overall, the Warp is a massive upgrade over the conventional terminal and provides various intuitive features and benefits.
The agent is designed to work with the Replit platform and is built directly into the
It is strongly integrated with the Replit platform and suggests deployment options, including reserved VMs, autoscaling, and static sites. Replit agent is currently only available via a limited early access program and is proclaimed as an experimental product so must be used with caution. But even though it is prone to some limitations, like handling more complex or backend-heavy applications, it’s a big step towards an AI-driven development trend.
LLM applications aim to improve productivity by automating several corporate and development-related tasks. They improve the developer's work experience and productivity and allow them to deliver quality products in a short time.