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

Chapter 4. Advanced LLM Integration Strategies with OpenAI

Now that you are familiar with the basics of LLMs and the OpenAI API, it’s time to take your skills to the next level. This chapter covers powerful strategies that will enable you to harness the true potential of OpenAI’s models. From prompt engineering and fine-tuning models for specific tasks to implementing RAG design on your proprietary data, this chapter will give you all the knowledge you need to go a step further in using LLMs with OpenAI. We will also cover some strategies to overcome the specific pitfalls that you will come across while working on integrating LLMs into your projects.

Prompt Engineering

Before we dive into prompt engineering, let’s briefly review the chat model’s completion function, as this section will use it extensively. To make the code more compact, we define the function as follows:

client = OpenAI()

def chat_completion(prompt, model="gpt-4", temperature=0, response_format=None):
    res = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": prompt}],
        temperature=temperature,
        response_format=response_format 
    )
    return res.choices[0].message.content

This function receives a prompt and displays the completion result in the terminal. The model and the temperature are two optional features set by default, respectively, to GPT-4 and 0. The response_format can be used if a JSON output format is needed.

To demonstrate prompt engineering, we will return to the example prompt ...