Prompt Engineering exercise (optional)

This part of the lab is marked as optional. It can therefore be skipped without effect on the other parts of the lab. You can always come back to it later at the end of the lab if you have time to spare.
When you interact with ChatGPT or other commercial services, a lot of guardrails are in place to prevent you from getting unwanted or not suitable for work results. In our Lab exercises, we are using a model that is not protected by those guardrails, and we will be modifying its settings. Therefore, it is your responsibility to do it in a safe way, and to make sure that the results you get are suitable for you and/or your audience. The authors of this Lab cannot be held responsible for any inappropriate results you may get.

As you have seen in the previous sections, there are different factors that will influence the results you can get from your model: the model parameters, the prompt, the query, and of course the data itself…​

Let’s see how we can adjust those different factors to get the best results.

Modifying the settings

Go back to the notebook 03-01-nb-llm-example.ipynb and try changing:

  • The llm parameters, like the temperature, to make the model more creative

  • The prompt template, to make the model behave differently, like giving the answer in the form of a poem. Or more useful in an enterprise context, give answers like it was addressing different types of audience: for example 5 years old children, or people without technical knowledge, or the opposite…​

  • The query itself which you can use to override certain aspects of the prompt. This will show you why it’s important to have guardrails in place against "prompt injection", like pre-filtering user queries

Modifying the data

Go back to the notebook 03-02-summarization.ipynb and try changing:

  • Edit or create new claims, making them harder to understand. (Note: to edit the json file in Jupyter, right click on it and select "Open With > Editor")

  • Experiment with different languages

Modifying the prompt

Go back to the notebook 03-03-information-extraction.ipynb and try changing:

  • Edit or create new claims, making them harder to understand

  • Try to have the model more precise and concise in date and time extraction, like making it respect a specific format