Developing Apps with GPT-4 and ChatGPT, 2nd Edition

Chapter 6. Putting It All Together

This book has equipped you with the necessary knowledge to harness the power of LLMs and implement them in real-world applications. We covered everything from foundational principles and API integrations to advanced prompt engineering and fine-tuning, leading you toward practical use cases with OpenAI’s models. We ended the book with a detailed look at how frameworks and other tools can enable you to unleash the power of LLMs and build truly innovative applications.

Key Takeaways

As a reminder of what you have learned, let’s go through the key takeaways for each chapter:

State-of-the-art models

OpenAI’s GPT-4 and GPT-3.5 Turbo models are built on a massive amount of data from the internet, fine-tuned for interactive use, and aligned through human feedback to avoid unreliable or dangerous generations. LLMs including OpenAI models are prone to hallucinations because of their underlying design; they are also prone to biases (including sexism and racism) because they tend to reinforce stereotypes derived from their training data. This is due to how they are designed and work: they essentially generate text based on their prediction of the next most probable word, which they base on what they have learned from the internet.

The OpenAI API

OpenAI provides simple solutions to use its models as a service: an HTTP API or libraries. In this book, we have used the official Python library. The prerequisites are simply having an OpenAI account and a means ...