ChatGPT Expert Interview Questions: by GPT-4 😜. An Experiment.

Introduction

ChatGPT has become a vital force in natural language processing (NLP) and the AI landscape in recent years. In this blog post, we’ll explore an exclusive interview with a ChatGPT expert, delving into topics such as deep learning, transformers, LLMs, and prompt engineering. Additionally, we’ll discuss practical questions for ChatGPT prompt engineer and expert consultant roles.

Create an image that represents the intersection of natural language processing, deep learning, and art. The image should feature elements such as a language model, transformers, and neural networks, and should convey the complexity and potential of these technologies. The image should be visually appealing and suitable for use as a featured image for an article on this topic. [Made with DreamStudio, Prompt Credit: ChatGPT]

Background: The ChatGPT Chronicles

Picture this: I was minding my own business, scrolling through the depths of the internet, when out of the blue, I received an email. An online AI-based chat interview from a mysterious company awaited me. The anticipation was palpable, but how could I prepare for this enigmatic encounter? No questions were available to practice, and the pressure was mounting.

Then it struck me like a bolt of lightning: Why not consult the big kahuna of AI, GPT-4, for guidance? After all, who better to generate questions about its own existence than the language model itself? So, I summoned the mighty GPT-4 and embarked on a whimsical journey into the world of ChatGPT, deep learning, transformers, and LLMs. And with that, dear reader, our tale of ChatGPT exploration began…

But first, cracking any online exams just got a lot easier with this chrome extension hack that I made entirely with GPT-4. Get Codes Here. I had 0 experience in creating chrome extensions till now.

It almost feels the same.

I. ChatGPT

1. Can you explain what ChatGPT is and how it works?
ChatGPT is an advanced language model that uses large and deep learning techniques to generate text based on input prompts. It leverages a vast amount of training data to understand and produce human-like responses.

2. What are some applications of ChatGPT?
ChatGPT has numerous applications, including chatbots, language translation, content generation, and even creative writing.

3. What are some limitations of ChatGPT?
Limitations include potential biases in training data, handling sensitive or offensive content, and the need for large amounts of training data.

4. How does ChatGPT differ from other language models?
Unlike other models such as BERT, ChatGPT is designed to generate text, making it more suitable for applications like content generation and chatbots.

II. Deep Learning and Transformers

5. What is the difference between supervised and unsupervised learning?
Supervised learning involves learning from labeled data, such as image classification, while unsupervised learning deals with unlabeled data, like clustering.

6. How do transformer-based architectures differ from traditional RNN-based architectures?
Transformers make use of self-attention mechanisms, providing better parallelization and handling long-term dependencies more effectively than RNNs.

This Guy Nailed it.

III. LLMs

7. How do LLMs such as GPT-3 and GPT-4 achieve high levels of performance in NLP tasks?
LLMs leverage vast amounts of data to learn complex patterns, capturing subtle nuances in human language. Limitations include potential biases and lack of interpretability.

IV. Prompt Engineering

8. What is prompt engineering and how does it work in NLP?
Prompt engineering involves crafting prompts or input text to improve a model’s performance. It plays a crucial role in guiding the output of a language model.

9. What are some best practices for prompt engineering?
Best practices include choosing relevant and specific prompts, avoiding bias, and evaluating the effectiveness of prompts.

Author Addition: Read my earlier post on A Guide to Prompt Writing for Large Language Models like GPT

RLHF is key when we craft the finetuning dataset.

V. Practical Questions for ChatGPT Prompt Engineer and Expert Consultant Roles

10. How would you approach the fine-tuning of ChatGPT for a specific domain?
Fine-tuning involves using domain-specific data to improve the model’s performance in that domain, leading to more accurate and relevant outputs.

Author Addition: Checkout my earlier posts on

1. Fine-tuning GPT-3 to Write in the Style of George R.R. Martin (Or Any Author)
2. Efficiently Save $$$ on OpenAI GPT API Calls: Tips and Techniques

11. Can you provide an example of a successful prompt engineering project you’ve worked on?
Yes, I’ve done a few at my earlier company, few Fiver Clients and also do check few examples on the articles below.

1. Incorporating Andrew Huberman’s Expertise into an AI-Powered Chatbot Interface using GPT-3
2. How to Use GPT-3 to Automate Replacing Words with Synonyms

12. How do you stay up-to-date with the latest advancements in NLP and deep learning?
I consume a lot of YouTubers, LinkedIn Posts, Medium Articles, Twitter Influencers, and engaging with the AI community in HackerNews and Reddit. It’s a fast changing space and already feels like the singularity moment.

Conclusion

In this blog post, we’ve explored an in-depth interview with a ChatGPT expert, discussing deep learning, transformers, LLMs, and prompt engineering. We’ve also touched on practical questions for ChatGPT prompt engineer and expert consultant roles. Understanding these concepts and staying informed about the latest advancements in NLP and AI is crucial for anyone working in this rapidly-evolving field.

Admission: A Playful Plot Twist

And now, for the pièce de résistance, the grand reveal that will leave you chuckling and shaking your head in disbelief: this entire whimsical masterpiece was, in fact, crafted by the mighty GPT-4 itself! Haha! The very subject of our tale has been the mastermind behind its creation, showcasing its prowess and tickling our funny bones along the way. What a delightful twist, indeed!

Epilogue: A Comical Caveat

Dear adventurous reader, as we conclude our whimsical journey into the enigmatic world of AI interviews, let us pause for a moment of reflective mirth. You see, the questions and answers we’ve explored thus far, while entertaining and informative, are by no means a definitive guide for facing the unpredictable nature of real-life interviews.

The truth is, these questions were but an experimental exercise of my own curiosity, fueled by the desire to test the limits of the mighty GPT-4. So, as I valiantly march into the battlefield of my upcoming interview, I shall return to share my experience and the actual questions I encountered.

Let us not forget that the vast realm of AI is ever-evolving, and the questions above may not be sufficient to quench your thirst for knowledge. Therefore, I encourage you, fellow AI enthusiasts, to delve deeper, research further, and forge your own path in the limitless world of artificial intelligence!

And with that, we bid adieu to our tale, eagerly awaiting the next chapter in the ChatGPT Chronicles…

Behind the Scenes: The Collection of Prompts

Behold, dear reader, the list of prompts that have guided our journey through the enchanting world of ChatGPT, deep learning, and beyond. Each question, like a breadcrumb, led us down the delightful path of discovery and amusement.

1. Write an outline for a blog post based on the interview questions and answers.
- [A list of potential questions, prepared earlier with GPT-4]

2. Add a fifth column for practical questions for ChatGPT prompt engineer and expert consultant roles.

3. Write a Medium article post about it.

4. Add a humorous background section using the points below:
- I was called for an online chat interview from a certain company.
- Preparation was necessary.
- No questions were around because obviously.
- So, asked the GPT-4, the big boy itself.
- Here are some questions it generated about it’s own positions.
- Why? Why the hell not?

5. Write a humorous ending note and mention that the interview questions might differ. This is an experimental exercise of my own and I will update my experience and the question types after I finish the interview. Also mention that these questions above might not be sufficient enough and people need to research more into it.

6. Admit that this whole thing was written with GPT-4 and a haha.

7. List the prompts used in a behind-the-scenes section.

Through these creative inquiries, we have traversed the enchanting AI landscape, showcasing the power of GPT-4 and unearthing a treasure trove of knowledge and entertainment.

Author’s Take:

GPT-4 is amazing and I am dying to try out the model with image question answering and the 32K Token window to generate even longer form articles and code pieces. One observation in this experiment was that GPT-4 plays very strangely with temperatures than the earlier models. The above article was smuggled out at temperature setting 0.7. At temperature 1, with same prompts, it only gave the outline of the article. In my opinion, text generations at every temperate setting is very usable unlike the previous versions. Playing with multiple settings and prompts is almost unnecessary with GPT-4 and the higher prompt pricing has also made it uncomfortable. And of course, the author addition parts are not as good as the early AGI (Read the Paper / Watch Sparks of AGI). Now that you’ve came this far, and assuming that you liked the post, follow me on Twitter, gimme some claps.

What a time to be alive!