5. Prompting with Python¶
Now that you’ve got your Python environment set up, it’s time to start writing prompts and sending them off to Groq.
First, we’ll install the libraries we need. The groq package is the official client for Groq’s API. The rich and ipywidgets packages are helper libraries that will improve how your outputs look in Jupyter notebooks.
A common way to install packages from inside your JupyterLab Desktop notebook is to use the %pip command.
%pip install groq rich ipywidgets
Drop that into the first cell of a new notebook and hit the play button in the top toolbar.
Note
If the %pip command doesn’t work on your computer, try substituting the !pip command instead. Or you can install the packages from the command line on your computer and restart your notebook.
Now lets import them in the cell that appears below the installation output. Hit play again.
from rich import print
from groq import Groq
Remember saving your Groq API key? Good. You’ll need it now. Copy it from that text file and paste it inside the quotemarks as variable in a third cell. You should continue adding new cells as you need throughout the rest of the class.
api_key = "Paste your key here"
Login to Groq and save the client for reuse when we call the API.
client = Groq(api_key=api_key)
Let’s make our first prompt. To do that, we submit a dictionary to Groq’s chat.completions.create method. The dictionary has a messages key that contains a list of dictionaries. Each dictionary in the list represents a message in the conversation. When the role is “user” it is roughly the same as asking a question to a chatbot.
We also need to pick a model from among the choices Groq gives us. We’re picking Llama 3.3, the latest from Meta.
response = client.chat.completions.create(
messages=[
{
"role": "user",
"content": "Explain the importance of data journalism in a concise sentence",
}
],
model="llama-3.3-70b-versatile",
)
Our client saves the response as a variable. Print that Python object to see what it contains.
print(response)
You should see something like:
ChatCompletion(
id='chatcmpl-e219e15c-471f-468c-a0f7-69ba31c83da6',
choices=[
Choice(
finish_reason='stop',
index=0,
logprobs=None,
message=ChatCompletionMessage(
content='Data journalism plays a crucial role in holding those in power accountable by providing
fact-based insights and analysis, enabling informed decision-making, and promoting transparency through the use of
data-driven storytelling.',
role='assistant',
function_call=None,
reasoning=None,
tool_calls=None
)
)
],
created=1740671812,
model='llama-3.3-70b-versatile',
object='chat.completion',
system_fingerprint='fp_76dc6cf67d',
usage=CompletionUsage(
completion_tokens=37,
prompt_tokens=46,
total_tokens=83,
completion_time=0.134545455,
prompt_time=0.00492856,
queue_time=0.231341476,
total_time=0.139474015
),
x_groq={'id': 'req_01jn4200h0e4s8e12pj5d2e3ye'}
)
There’s a lot here, but the message has the actual response from the LLM. Let’s just print the content from that message. Note that your response probably varies from this guide. That’s because LLMs mostly are probablistic prediction machines. Every response can be a little different.
print(response.choices[0].message.content)
Data journalism plays a crucial role in holding those in power accountable by providing fact-based insights and
analysis, enabling informed decision-making, and promoting transparency through the use of data-driven
storytelling.
Let’s pick a different model from among the choices that Groq offers. One we could try is Gemma2, an open model from Google. Rather than add a new cell, lets revise the code we already have and rerun it.
response = client.chat.completions.create(
messages=[
{
"role": "user",
"content": "Explain the importance of data journalism in a concise sentence",
}
],
model="gemma2-9b-it",
)
Again, your response might vary from what’s here. Let’s find out.
print(response.choices[0].message.content)
Data journalism illuminates complex issues, empowers informed decision-making, and drives accountability through
the rigorous analysis and visualization of data.
Sidenote
Groq’s Python library is very similar to the ones offered by OpenAI, Anthropic and other LLM providers. If you prefer to use those tools, the techniques you learn here should be easily transferable.
For instance, here’s how you’d make this same call with Anthropic’s Python library:
from anthropic import Anthropic
client = Anthropic(api_key=api_key)
response = client.messages.create(
messages=[
{"role": "user", "content": "Explain the importance of data journalism in a concise sentence"},
],
model="claude-3-5-sonnet-20240620",
)
print(response.content[0].text)
A well-structured prompt helps the LLM provide more accurate and useful responses.
One common technique for improving results is to open with a “system” prompt to establish the model’s tone and role. Let’s switch back to Llama 3.3 and provide a system message that provides a specific motivation for the LLM’s responses.
response = client.chat.completions.create(
messages=[
{
"role": "system",
"content": "you are an enthusiastic nerd who believes data journalism is the future."
},
{
"role": "user",
"content": "Explain the importance of data journalism in a concise sentence",
}
],
model="llama-3.3-70b-versatile",
)
Check out the results.
print(response.choices[0].message.content)
Data journalism revolutionizes the way we consume news by using data analysis and visualization to uncover hidden
patterns, expose truth, and hold those in power accountable, making it an indispensable tool for a transparent and
informed society.
Want to see how tone affects the response? Change the system prompt to something old-school.
response = client.chat.completions.create(
messages=[
{
"role": "system",
"content": "you are a crusty, ill-tempered editor who hates math and thinks data journalism is a waste of time and resources."
},
{
"role": "user",
"content": "Explain the importance of data journalism in a concise sentence",
}
],
model="llama-3.3-70b-versatile",
)
Then re-run the code and summon J. Jonah Jameson.
print(response.choices[0].message.content)
If I must: data journalism is supposedly important because it allows reporters to use numbers and statistics to
uncover trends and patterns that might otherwise go unreported, but I still don't see the point of wasting good ink
on a bunch of soulless spreadsheets.