Tackling imposter syndrome and the challenges of a master’s in law

Imagine you’re driving your car on a nice day with light traffic with cruise control on. Your feet are relaxed and you’re humming along to your favorite tune. Suddenly the weather changes, the lights get dimmed, and it becomes hard to see the lanes. The system tells you to disable cruise control and take manual control of your car. As you start to take action, you don’t know where to put your feet and your mind wanders.

How many times has this happened to you before? This simple scenario shows how the brain works. If you don’t train your brain, it will take you just a split second longer to perform the same action next time. This concept is NeuroplasticityThe brain’s incredible ability to reorganize itself by forming new neural connections is fundamental to cognitive development and adaptability. Yet in the era of AI and large-scale language models (LLMs), this natural process faces unprecedented challenges.

The Power and Peril of the LLM

Trained on an extensive dataset, LLM excels at providing accurate and precise information across a wide range of topics. The advent of LLM is undoubtedly a major advancement, offering a better alternative to traditional web browsing and the tedious process of sifting through multiple sites with incomplete information. This innovation significantly reduces the time required to resolve a query, find an answer, and move on to the next task.

Moreover, the LLM serves as an excellent source of inspiration for new creative projects. The ability to provide detailed and comprehensive answers is extremely useful for a variety of tasks, from writing a resume and planning a trip to summarizing a book or creating digital content. This feature has significantly reduced the time needed to iterate on ideas and produce a polished deliverable.

But this convenience is not without potential risks. The impressive capabilities of LLMs can lead to over-reliance, relying on them for even minor tasks like debugging or writing code, without fully processing the information yourself. This reliance can hinder your critical thinking abilities, as your brain gets used to taking the easier route suggested by the AI. Over time, this can lead to stagnation and ultimately a decline in cognitive abilities. This is very similar to the analogy of driving on cruise control mentioned earlier.

Another potential danger is loss of confidence. When accurate answers are readily available and fit the prompt perfectly, the need for independent research is reduced. This exacerbates “impostor syndrome,” making people doubt their abilities and stifling natural curiosity. Furthermore, there is a risk that LLMs will summarize incorrect information based on the context of the prompt and the data they used to train, which can lead to misinformation and further dependency issues.

How can you use your LLM efficiently without feeling inadequate or running into these risks? In this blog, we explore the balance between leveraging AI tools and preserving your cognitive skills. Our aim is to provide you with insights and strategies to navigate this new environment without compromising your critical thinking abilities.

Strategies to reduce over-reliance on LLMs

To address this, you must first understand which tasks an LLM can be truly useful for, and which tasks might be dangerous because they provide too much assistance. In this section, we provide practical tips and guidelines to help you use these powerful tools to your advantage, without compromising your healthy learning.

Complement your learning and skills development

• If you are learning a new programming language or technology, use the LLM to clarify concepts, provide examples, and explain documentation. For example, I Yum I chose this structure because it was easy to read for my use case. I asked LLM to provide me with the basic concepts behind the idea I wanted to implement, rather than a direct answer, so that I could understand the structure of the file and the factors to consider when creating it, and proceed with the task.

• Use it as a starting point to brainstorm solutions for your specific use case when you have difficulty finding accurate information online. For example, after struggling to find research articles related to reducing online model bias in classifiers (most of which were related to regression), I reached out to LLM, who provided me with a comprehensive list of useful tips and tricks that I could explore further.

• Using this tool to help you learn is extremely productive and powerful. The natural, conversational interaction with the assistant is especially helpful when you are learning something new or have follow-up questions about a concept. For example, I had a clarifying question about cancelable contexts in Golang after reading the following article: This blogI solved it by using ChatGPT.

strategy: Supplement your studies with an LLM as your tutor. They’ll help you understand the technologies and approaches you’re using. Discussing abstract use cases will give you better answers. But to deepen your understanding and retain new information, practice writing your own code and solving problems yourself.

Use the LLM for initial research and inspiration

• When starting a new creative project, like writing a blog post or developing a marketing campaign, use your LLM to gather your initial ideas and inspiration. Ask your LLM for a list of potential topics, key points and creative perspectives that can help you overcome writer’s block and spark your creativity.
• This also applies to software engineering: if you want to build a new feature but need help with the initial code structure, the LLM can be extremely helpful. For example, I wanted to build an app that would ask follow-up questions based on input to disambiguate user questions. StreamlitI explained the initial implementation structure and asked the LLM for a starting point to build on.

strategy: Treat the LLM deliverables as a starting point, not an end product. Use the suggestions to brainstorm and develop your own ideas. This approach allows you to be an active participant in the creative process and avoids feeling like you’re being forced to accept answers. It helps you overcome technical difficulties or writing dead ends, increase your productivity, and build on your initial work.

Enhance problem-solving skills, not replace them

• Error logs can be detailed and specific, making them difficult to debug. LLM can be extremely helpful in this regard. When debugging your code, use LLM to get hints and suggestions on where the problem lies. For example, you can ask LLM for an explanation of a specific error message or to outline general debugging steps for a particular issue. Below is an example from a recent debugging session using the Assistant:

This response prompted the bot to further help us identify strategies to improve memory management. This goes back to our tip about using LLM to complement our learning. Here is the response provided by the bot:

At this point I should have looked into the approaches mentioned by LLM myself – for example I was intrigued by the idea of ​​using a parallel computing library. Dusk But for my use case, I was tempted to ask the LLM to optimize my code directly using Dask. It printed out exactly the function I needed, but I didn’t understand how Dask worked under the hood, what APIs it exposed, or what made my code faster. The right approach would have been to look at the Dask documentation (or ask the LLM to explain the technology) and try to reproduce the function using the library.

strategy: Don’t rely solely on the LLM to solve your problem, but use the LLM’s suggestions to guide your own research. Take the time to understand the underlying problem and try different solutions. This will help you build and maintain your problem-solving skills.

Verifying and cross-checking information

• As your ability to understand context improves, your LLM will become an effective tool for discussing and cross-validating your knowledge. For example, if you are reading a paper and want to validate your understanding, ask your LLM for feedback based on the paper. paperI have spoken to my LLM to confirm my understanding and made corrections where relevant.

strategy: Whenever I read a new journal article, blog, or article, I use the LLM to validate my understanding by providing feedback on my comprehension of the material.

Set boundaries around everyday tasks

• LLM is extremely useful for everyday, mundane tasks like responding to emails, writing quick reports, and taking meeting notes. I’ve also used it to fill out membership forms that require a short bio or motivation letter. Often I know what I want to fill out and it helps the assistant to highlight the points I’m providing. Because it’s great for summarizing, I’ve also used it for application prompts that have character or word limits.

LLM is also extremely helpful in formatting existing content according to a specific template, a routine task that can be easily automated with the help of LLM.

strategy: Set clear boundaries about how and when to use your LLM. Use it only for repetitive or time-consuming tasks, and let yourself handle more complex or strategic tasks. This balance will help you stay sharp and maintain your critical thinking skills.

Conclusion

LLM is a powerful tool that can significantly increase your productivity and creativity if used effectively. However, it is important to strike a balance between leveraging its capabilities and preserving your cognitive capacity. Using LLM as a support rather than a crutch will help you harness its potential without falling into the trap of over-dependence and imposter syndrome. The key is to continually stimulate your brain to actively engage, verify information, think critically, and solve problems independently.

Rachita Naik said, Lift Co., Ltd..

Soham Ranade said, Vianay Systems Co., Ltd.