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Myths About Large Language Models

Posted on:February 15, 2024 at 09:35 AM

In the rapidly evolving world of technology, Large Language Models (LLMs) like ChatGPT have become a focal point of discussion and innovation. However, there are many myths and misconceptions about the true potential and accessibility of these models. In this article, I want to assess these myths, revealing the reality of LLMs and their real capabilities.

Myth 1: LLMs Are Difficult to Learn

Contrary to popular belief, LLMs are not just for tech experts. With user-friendly interfaces and evolving documentation, anyone can learn to use and master LLMs. They are designed to be intuitive, making them accessible for both beginners and advanced users alike.

This works not only for the leaders on the market like ChatGPT, but to numerous projects built on the top of these technologies, i.e., Looka or HeyGen.

Myth 2: Custom LLMs Outperform ChatGPT

While custom LLMs have their advantages, ChatGPT’s versatility shouldn’t be underestimated. Effective prompt engineering can unlock ChatGPT’s potential, often yielding results comparable to specialised models. It’s more about skilful usage than the model itself.

Thus, it’s worth to invest some time into mastering your prompt engineering skills. There is an official guide provided by OpenAI on how to create prompts. There are many websites that collect the most efficient prompts. And, of course, there should be your real-life experience that integrates all of these.

Myth 3: Building Your Own LLM Is Complex

Creating a custom LLM is more approachable than it appears. Tools like CustomGPT simplify the process, allowing users to integrate their own data and preferences. Furthermore, ChatGPT has a feature that allows you to create plugin enable a tailored experience without extensive technical know-how.

The only time consuming tasks are: (1) prepare and ‘clean’ the documents you will use as your corpus of texts, (2) polish the instructions and prompts you’ll use to achieve the best accuracy.

Myth 4: Open-Source Models Are Inferior

Open-source models such as llama2, vicuna, Mistral AI, or falcon falcon challenge this misconception. These models, especially when fine-tuned, can excel in specific tasks, sometimes even outperforming GPT 3.5. The key lies in selecting the right model for your specific needs.

Therefore, if you need some specific things, watch carefully to the latest updates about this models.

Myth 5: Fine-Tuned Open-Source Models Always Surpass GPT

While fine-tuning can enhance a model’s performance, it doesn’t automatically make it superior to ChatGPT. Often, the right prompts and instructions can leverage ChatGPT’s capabilities to match or exceed those of fine-tuned alternatives.

Therefore, before doing anything complicated or time consuming, try different options with your regular AI instruments. In 80 per cent cases, it will work ‘out-of-the-box’.

Myth 6: LLMs Are Limited to Text Generation

LLMs’ capabilities extend far beyond text generation. They can create images, audio, and videos, perform complex proofreading tasks, and even assist in drafting and reviewing documents. Their versatility makes them invaluable tools across various domains.

If you have routine or any repetitive task, invest a little bit time into research whether or not it is possible to automate it with ChatGPT or any tools built on the top of it.

Myth 7: LLMs Are Exclusive to Developers and Marketers

Often, LLMs are used by software developers and marketers. However, they have a wide array of applications outside of these boundaries.

They can automate routine tasks, making them useful in diverse fields beyond just development and marketing. For example, they will be extremely useful for legal counsellors (check, i.e., Casetext), accountants, project and product managers, and even CEOs (summarise discussions, email threads, highlight key points, etc.).

Myth 8: Building Projects with LLMs Is Challenging

Developing a project with LLMs can be surprisingly straightforward. With resources like Langchain, Streamlit, and Hugging Face Transformers, even non-experts can create functional applications in a matter of hours, especially, if they use ChatGPT to develop the code needed for such a project.

To create an automatic script from scratch, you just need a basic knowledge of programming on python. Everything the rest could be handled by LLM.

Myth 9: LLMs Are Only for Professional Use

Often, we believe that LLMs should be used for work purpose only. However, this is not quite accurate. LLMs find applications in personal life as well.

From planning travel itineraries to creating meal plans and seeking basic legal advice, their utility extends into our everyday lives, offering convenience and efficiency. Whenever you have something boring, routine, or repetitive, consider automating this with one of the tools.


To sum up, the myths surrounding Large Language Models stem from a lack of understanding and exposure. As we debunk these misconceptions, it becomes evident that LLMs are versatile, accessible, and powerful tools that can transform both professional tasks and personal projects. Embracing their capabilities opens a world of possibilities, encouraging innovation and efficiency in a myriad of applications.