

The third part includes a set of curated examples that show the agent exactly what code it should produce
for what kind of user request. The large language models empowering the agent are extremely good at 
recognizing patterns in a prompt and repeating the pattern with new data. Therefore, it is very important
that the examples are written in a way that maximizes the likelihood of the agent to generating correct,
executable code in practice. 
Let's have a look at one example:
```text
Task: "Identify the oldest person in thedocument` and create an image showcasing the result as a banner."
I will use the following tools: document_qa to find the oldest person in the document, then image_generator to generate an image according to the answer.
Answer:
py
answer = document_qa(document, question="What is the oldest person?")
print(f"The answer is {answer}.")
image = image_generator("A banner showing " + answer)
`
The pattern the model is prompted to repeat has three parts: The task statement, the agent's explanation of 
what it intends to do, and finally the generated code. Every example that is part of the prompt has this exact 
pattern, thus making sure that the agent will reproduce exactly the same pattern when generating new tokens.
The prompt examples are curated by the Transformers team and rigorously evaluated on a set of 
problem statements
to ensure that the agent's prompt is as good as possible to solve real use cases of the agent.
The final part of the prompt corresponds to:
```text
Task: "Draw me a picture of rivers and lakes"
I will use the following