Prompt Engineering - FIND Prompt

The FIND prompt allows us to extract specific information or perform searches within the generated responses of ChatGPT. By utilizing the FIND directive, we can instruct the language model to find and present relevant details based on specific criteria, enhancing the precision and usefulness of the generated output.

Understanding the FIND Directive

The FIND directive enables us to specify a search pattern or criteria to locate specific information within the response generated by ChatGPT. It provides a way to programmatically search for and extract relevant details from the model's output.

The basic syntax for the FIND directive is as follows −

User: Can you provide a summary of the novel "Pride and Prejudice"?
ChatGPT: "Pride and Prejudice" is a classic novel written by Jane Austen. It explores themes of love, class, and societal expectations. [FIND: themes]

In this example, the user asks for a summary of the novel "Pride and Prejudice," and the response from ChatGPT includes the content specified within the FIND directive, which is the information related to "themes" in this case.

Best Practices for Using the FIND Directive

To make the most of the FIND directive, consider the following best practices −

  • Be Specific − Clearly define the search pattern or criteria within the FIND directive. This helps ensure that the model locates the desired information accurately.

  • Contextual Prompts − Incorporate the FIND directive within a contextually rich prompt. By providing relevant context along with the directive, we can guide the model's understanding and improve the accuracy of the search.

  • Iterate and Refine − Experiment with different search patterns and criteria to find the most effective way to extract the desired information. Iterate and refine our prompts based on the results obtained.

  • Combine with Other Techniques − The FIND directive can be used in conjunction with other prompt engineering techniques, such as the INCLUDE directive or COLUMN directive, to further enhance the generated output. Consider combining multiple techniques to achieve our desired results.

Example Application − Python Implementation

Let's explore a practical example of using the FIND directive with a Python script that interacts with ChatGPT.

In this example, we define a function generate_chat_response() that takes a prompt and uses the OpenAI API to generate a response using ChatGPT.

The chat_prompt variable contains the user's prompt and the ChatGPT response, including the FIND directive to search for information related to "themes."

import openai
# Set your API key here
openai.api_key = 'YOUR_API_KEY'

def generate_chat_response(prompt):
   response = openai.Completion.create(
      engine="text-davinci-003",
      prompt=prompt,
      max_tokens=100,
      temperature=0.7,
      n=1,
      stop=None
   )
   return response
   
user_prompt = "User: Can you provide a summary of the novel 'Pride and Prejudice'?\n"
chat_prompt = user_prompt + "ChatGPT: 'Pride and Prejudice' is a classic novel written by Jane Austen. It explores themes of love, class, and societal expectations. [FIND: themes]"

response = generate_chat_response(chat_prompt)
print(response)

Output

When we run the script, we will receive the generated response from ChatGPT, including the extracted details based on the specified search pattern.

The novel follows the five Bennet sisters, Elizabeth, Jane, Lydia, Mary, and Kitty, who are all looking for love and marriage. Elizabeth and her older sister Jane both fall in love with different men, but are faced with obstacles as they must battle society's expectations, their own pride, and the prejudice of others. The novel ultimately ends with the two sisters finding true love and happiness.

Conclusion

In this chapter, we explored the power of the FIND directive in prompt engineering for ChatGPT. By using the FIND directive, we can extract specific information or perform searches within the generated responses.

We discussed the syntax of the FIND directive and provided best practices for its usage, including being specific, using contextual prompts, iterating and refining, and combining with other prompt engineering techniques.

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