An expert from DeepMind has meticulously written an 80,000-word article detailing how they use AI.
Over the past year, the author has devoted a significant amount of energy to interacting with Large Language Models (LLMs). At least several hours each week are spent deeply engaging with these models, exploring their capabilities and applications. In this process, the author has been profoundly impressed by the powerful capabilities of LLMs in handling increasingly complex tasks.
The author lists numerous practical examples of using LLMs in the article, showcasing their wide range of applications in different fields. This includes using LLMs to build complete applications. From initial concept design to planning functional modules, and then to generating specific code, LLMs can provide effective help and guidance. When learning to use various frameworks, LLMs also play an important role. Whether it's a newly emerging popular framework or a more complex and specialized one, the author can quickly understand its basic principles, core concepts, and usage methods through communication with LLMs, thus mastering these frameworks more efficiently.
In terms of code optimization, LLMs also perform exceptionally well. The author uses LLMs to analyze existing code, identify potential performance bottlenecks, and then optimize the code according to the suggestions provided by LLMs to improve its performance. For the management and simplification of large codebases, LLMs also provide strong support. They can help the author sort out the structure of the codebase, identify redundant parts, and propose simplification and optimization solutions, making the codebase clearer and easier to maintain.
In the field of academic research, LLMs also demonstrate their unique value. When writing preliminary experimental code for research papers, the author refers to the suggestions of LLMs. LLMs can provide some possible experimental ideas and corresponding code frameworks based on the research topic and objectives, saving the author a lot of time and effort. In addition, LLMs can also be used to automate some monotonous tasks and the writing of one-off scripts. For example, repetitive data processing tasks, file format conversion tasks, etc., can be completed by automatically generating corresponding script code through LLMs, greatly improving work efficiency.
LLMs can also be used as API references and search engines. When the author needs to understand the specific usage method of an API, LLMs can quickly provide detailed explanations and example code. When looking for relevant technical materials and information, LLMs can also act like a search engine, providing related literature, blog articles, technical forum discussions, and other content based on the author's questions, helping the author obtain the knowledge needed.
Finally, LLMs also have significant effects in solving already solved problems and fixing errors. When encountering some common problems or errors in the code, the author can describe the symptoms of the problem and related background information to LLMs. LLMs will provide possible solutions and repair suggestions based on their accumulated knowledge and experience, helping the author quickly solve problems and restore normal progress in work.