Limitations and Improvements of File Tools in Coding

The Cursor IDE extends VS Code, indexing code for efficient retrieval, enabling semantic queries and context-based code generation for optimal results.

### Overview of AI Code Generation Tools and Techniques
* 🧰 "File Tool" is a basic tool used for AI code generation, but it has limitations such as **requiring manual orchestration of tasks**, **inability to interpret large tasks or code bases**, and **using a context window that restricts its capabilities**.
* 🔄 Compared to GPT-4, current tools like "Copilot Chat" and "File Tool" have **limitations**. To overcome these limitations, technologies like "Leg" for **increasing memory context**, and "Plan" and "Solve" for **enhancing capabilities** have been introduced.
* 🔒 When internal codes are exposed to external platforms like OpenAI, there is a risk of **intellectual property leakage**. Therefore, closed-source Large Language Model (LLM) technologies need to be considered.
* 🔍 "Cursor IDE" is an AI code generation tool similar to "Filer" that extends VS Code and **indexes the entire code base in a vector store**. It automatically **extracts related code snippets** based on requests and presents them in a **limited context window**.
* 💬 When querying with "Cursor IDE," semantic queries are performed to find all the files where a specific function or message is used in the codebase. The tool acts like a "**librarian**" by scraping and presenting relevant code snippets.
* By using custom IDs in "Cursor IDE," it becomes possible to **accurately find and fix bugs in related code**, as well as **generate code that requires modifications in multiple places**.
* 🚀 "Cursor IDE" has its own model that is fine-tuned to identify **how to modify specific parts** of which files. This enables the tool to **automatically find and make necessary edits**, thereby **increasing productivity**.
* 📊 There are various AI code generation tools like "Filer," "Eraser," and "Add," and it is recommended to **explore and experiment with them** to find the most suitable tool for specific use cases.

Well, not everyone can do this well or continue to research, so we need to rely on tools. Among them, the most basic tool we can use is the file tool. Have you tried using the file tool? The limitations of the file tool are that I am the controller. I need to orchestrate tasks like generating and bringing in code, testing, and so on. Therefore, integration needs to be done by me. Also, due to the limitations of the context window, it cannot interpret large tasks or large code bases. Furthermore, since it does not use the GPT-4 model as it is, there are current limitations in what Copilot Chat or File Tool can do compared to what was possible with GPT-4. It is constantly evolving, so I am talking about the current limitations as of February 2024, at the end of January.

I am telling a story. So, in order to improve those things, we have applied technologies like Leg that increase memory context, and then technologies like Plan and Solve have been included. Also, when the internal codes are exposed to places like OpenAI, our intellectual property leaks externally, so there are many cases where we cannot use them. Therefore, we also need to consider utilizing closed-source LLM technologies. In the case of Filer, they are all just used publicly, so various tools that I will talk about later can use closed-source LLM like codes. So, in the last session today, there is also content on how to apply closed-source LLM. Among various IDE tools, there is something similar to Filer called Cursor IDE.

Cursor IDE is extending the VS Code, so what it does is, it indexes the entire code base in a vector store, which is a technology of vector databases. So, in order to facilitate extensible generation, it automatically extracts various related codes when I request various codes. For example, if I want to fix a bug in the code, it automatically extracts various related codes related to that bug from the indexed whole code base. It then presents only the source code parts that need to be reviewed within a limited context window through LML. This tool is designed to lead us to the optimal results we desire. It provides explanations in the overall context. Once I install Cursor IDE here and actually use it, for example, I will show you one thing. If I look here, there is a function called load. I will try asking where this function is being used. Let me do it here like this, and then in the chat or.

I will ask where this function is used. When you just send it, it only understands the text in the file because it costs money. The default option is to send only a small token amount. So, it won't be able to explain well where it's used. It will say it can't confirm. But when you mention it and ask to check in the entire codebase, different results come out. First, I'm querying. This thing is doing semantic queries. So, in our entire codebase here, all the files where this load message is used are first found. Then, from what is found, we extract only the areas related to chunks, meaning areas of sentences, and put them in the context window.

You can submit it in one place and ask questions together. It's like a librarian. If I'm a lawyer, I go to the library and find all the things related to this patent, and bring them back, that's what scraping is. When the lawyer sees what has been scraped, the lawyer decides if this patent is possible or not. So, the message here is being used, and additional messages about where it is used will come out. These things will now be expanded, so you can use things like custom IDs. And if you use custom IDs, you can do a lot more work. For example, if there is a bug in the related code, you can find that bug as accurately as possible, and because you understand the entire context and when you generate the code, what is generated is GPT-4.

Through this, it will go, so GPT 44 just talks about this and that. This needs to be edited here and there. With these words, you need to touch multiple codes, so you need to edit the configuration file and modify parts of a specific source code in several places. Understanding the context, it has its own model, which is a fine-tuned model that identifies how to modify which part of which file. Therefore, it automatically finds and makes the necessary edits to that part. By using this kind of console, you can increase productivity. So, there are running comparisons, and since there is a comparison on Medium, there are tools like file eraser and add, etc., so it would be good to try using all of them once.

3. Copilot의 한계와 Cursor IDE

최고의 전문 컨텐츠로 가장 쉽게 시작하는 AI와 마이크로서비스 아키텍쳐 채널을 만들어가는 유엔진입니다. 

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Limitations and Evolving Technologies in File Tool Usage

The file tool is limited by the user's control and the context window, making it challenging to interpret large tasks or code bases.

Its capabilities are currently not as advanced as GPT-4, but evolving technologies like Leg, Plan, and Solve are being implemented to enhance its performance.

The use of closed-source LLM technologies, such as Filer and Cursor IDE, is also being considered to address the limitations and improve the file tool's functionality.

Cursor IDE extends VS Code by indexing the entire code base in a vector store and automatically extracting related code when requested, facilitating the review process.

It uses Limited Memory LML to present the relevant parts of the source code within a limited context window and is designed to provide optimal results and explanations in the overall context.

The tool allows for semantic queries in the entire code base to find all files where a specific message is used and extracts the related sentences, providing a comprehensive overview of the code's usage.

The Impact of Scraping on Legal Decision-Making

Scraping is used to gather information for legal decision-making, including patent viability.

Custom IDs can enhance the scraping process, allowing for more precise bug detection in related code.

The ultimate result of code generation with a comprehensive understanding of the context is GPT-4.

Using GPT-44 to Automatically Modify Source Code for Increased Productivity

GPT-44 is a fine-tuned model for identifying and making edits to specific parts of source code, increasing productivity.

It allows for running comparisons and provides tools like file eraser and add for efficient code editing.