GPT-4 Prompt Engineering: Why This Is a BIG Deal!

Understanding the Context Window in GPT-4

In this section, the speaker explains the context window of GPT-4 and how it has doubled from 4000 tokens to 8000 or even 32000 tokens. The speaker also clarifies some confusion around the context length and shows how it works.

Context Window and Length

• A thousand tokens is about 750 words.

• The context window for chat GPT has increased from 4000 to 8000 tokens in one version of GPT-4, with a maximum of 32000 tokens in the biggest version.

• The model can still understand information within the token window but cannot access information outside of it.

• If we add more than 4000 tokens, some parts will end up outside the token window, making it impossible for the model to access that information.

Use Cases for Big Context Window

Turning YouTube Video into Blog Post

• Using a big context window in GPT-4, we can turn a YouTube video into a blog post or article by transcribing the audio using a Python script and copying relevant information from previous articles.

• After transcribing the audio into text form, we copy relevant information from previous articles and paste them into a Word document.

• We then copy additional information from Mid Journey's homepage to provide context for our article or blog post.

Saving Tokens with Big Answers

• When GPT-4 gives us a big answer back from everything we put in, we lose some tokens. However, if we get back only "red" as an answer after pasting an entire article into GPT-4, we save tokens.

Using GPT-4 to Generate In-Depth Articles

The speaker discusses how he used GPT-4 to generate an in-depth article about the future of photography. He explains how he primed GPT-4 for optimal prompt generation and added his own context to create a unique article.

Creating the Prompt

• Fed information into GPT-4 to generate an in-depth article about the future of photography.

• Changed the title to "GPT-4 Plus Mid Journey V5: The Future of Photography" and included details from the information provided.

Section One: GPT-4 and Mid Joining V5

• Discussed how GPT-4 and Mid joined V5, showcasing their power in the industry.

Priming GPT-4 for Optimal Prompt Generation

• Elaborated on section one by discussing gpt4 priming, building contextual understanding, crafting detailed prompts, and iterative refinement.

Conclusion

• Requested more elaboration on conclusion with a first-person view. Received a four-five paragraph conclusion that was very in-depth.

Advantages of Using Context Window

• Explained that using all available context information allows for more personalized content creation without relying heavily on foundation models.

AI Detection Tool Results

• Ran generated article through OpenAI's text classifier which considered it "very unlikely" to be AI-generated.

Overall, the speaker spent 20-30 minutes generating an in-depth article using GPT-4. By adding his own context and priming techniques, he was able to create unique content that did not rely heavily on foundation models.

Creating a Quiz Test Based on GPT-4 Technical Report

In this section, the speaker discusses how they created a quiz test based on the technical report of GPT-4. They first split the text into different sequences and then created a quiz test with 15 questions for students in machine learning. Later, they simplified the questions to make them suitable for fourth-graders.

Creating a Quiz Test for Machine Learning Students

• The speaker created a quiz test with 15 questions about the content of GPT-4 technical report.

• Some of the questions were about biases and limitations that GPT-4 may have in its outputs, how OpenAI works to improve safety and alignment compared to previous models, and explaining pre-training and reinforcement learning with human feedback.

Creating a Quiz Test for Fourth Graders

• The speaker simplified the quiz test to five questions suitable for fourth-graders.

• The questions included asking about the name of AI language model (GPT), what GPT-4 does (help people cook, drive cars, play video games, answer questions, give information), whether GPT is perfect (no), and what it should not help with (making friends).

Conclusion

In this section, the speaker concludes by saying that this was just scratching the surface of what can be done with large content windows like GPT-4. They plan to explore more ideas later and invite viewers to check out their membership link in the description.

Final Thoughts

• The speaker hopes that this video gave inspiration on how to take advantage of new context window length.

• Viewers are invited to check out the link in the description for more videos on this topic.