AI Unveiled: The Power of Prompt Engineering in the Age of LLMs

The Importance of Context

Customizing an LLM application often involves giving it detailed instructions about how to behave. This might mean defining a personality type, a specific situation, or even emulating a historical figure. For instance, when asking for help with a moral dilemma, you can ask the model to answer in the style of someone specific, which will very much influence the type of answer you get.

Effective prompt engineering also involves creating a specific, customized data context. This means providing the model with relevant facts, like personal user data, real-time information or specialized knowledge, which it wouldn’t have access to otherwise. This approach allows the AI to produce output far more relevant to the user’s specific situation than would be possible for an uninformed generic model.

Efficient Data Management with Prompt Templating

Data can be loaded in manually, just by typing it into ChatGPT. If you ask for advice about how to install some software, you have to tell it about your hardware. If you ask for help crafting the perfect resume, you have to tell it your skills and work history first. However, while this is ok for personal use, it does not work for development. Even for personal use, manually inputting data for each interaction can be tedious and error-prone.

This is where prompt templating comes into play. Prompt Poet uses YAML and Jinja2 to create flexible and dynamic prompts, significantly enhancing LLM interactions.

A Powerful Example: Daily Planner

To illustrate the power of Prompt Poet, let’s work through a simple example: a daily planning assistant that will remind the user of upcoming events and provide contextual information to help prepare for their day, based on real-time data.

For example, you might want output like this:

Good morning! It looks like you have virtual meetings in the morning and an afternoon hike planned. Don’t forget water and sunscreen for your hike since it’s sunny outside.

Here are your schedule and current conditions for today:

• 09:00 AM: Virtual meeting with the marketing team

• 11:00 AM: One-on-one with the project manager

• 07:00 PM: Afternoon hike at Discovery Park with friends

It’s currently 65°F and sunny. Expect good conditions for your hike. Be aware of a bridge closure on I-90, which might cause delays.

To do that, we’ll need to provide at least two different pieces of context to the model, 1) customized instructions about the task, and 2) the required data to define the factual context of the user interaction.

Prompt Poet gives us some powerful tools for handling this context. We’ll start by creating a template to hold the general form of the instructions, and filling it in with specific data at the time when we want to run the query.

For the above example, we might use the following Python code to create a raw_template and the template_data to fill it, which are the components of a Prompt Poet Prompt object.

raw_template = """
- name: system instructions
  role: system
  content: |
    You are a helpful daily planning assistant. Use the following information about the user’s schedule and conditions in their area to provide a detailed summary of the day. Remind them of upcoming events and bring any warnings or unusual conditions to their attention, including weather, traffic, or air quality warnings. Ask if they have any follow-up questions.

- name: realtime data
  role: system
  content: |
    Weather in {{ user_city }}, {{ user_country }}:

    - Temperature: {{ user_temperature }}°C

    - Description: {{ user_description }}

    Traffic in {{ user_city }}:

    - Status: {{ traffic_status }}

    Air Quality in {{ user_city }}:

    - AQI: {{ aqi }}

    - Main Pollutant: {{ main_pollutant }}

    Upcoming Events:

    {% for event in events %}

    - {{ event.start }}: {{ event.summary }}

    {% endfor %}

"""

The code below uses Prompt Poet’s Prompt class to populate data from multiple data sources into a template to form a single, coherent prompt. This allows us to invoke a daily planning assistant to provide personalized, context-aware responses. By pulling in weather data, traffic updates, AQI information, and calendar events, the model can offer detailed summaries and reminders, enhancing the user experience.

User data

user_weather_info = get_weather_info(user_city)

traffic_info = get_traffic_info(user_city)

aqi_info = get_aqi_info(user_city)

events_info = get_events_info(calendar_events)

template_data = {

"user_city": user_city,

"user_country": user_country,

"user_temperature": user_weather_info["temperature"],

"user_description": user_weather_info["description"],

"traffic_status": traffic_info,

"aqi": aqi_info["aqi"],

"main_pollutant": aqi_info["main_pollutant"],

"events": events_info

}

Create the prompt using Prompt Poet

prompt = Prompt(

raw_template=raw_template_yaml,

template_data=template_data

)

Get response from OpenAI

model_response = openai.ChatCompletion.create(

model="gpt-4" ,

messages=prompt.messages

)

Conclusion

Mastering the fundamentals of prompt engineering, particularly the roles of instructions and data, is crucial for maximizing the potential of LLMs. Prompt Poet stands out as a powerful tool in this field, offering a streamlined approach to creating dynamic, data-rich prompts.

Prompt Poet’s low-code, flexible template system makes prompt design accessible and efficient. By integrating external data sources that would not be available to an LLM’s training, data-filled prompt templates can better ensure AI responses are accurate and relevant to the user.

By using tools like Prompt Poet, you can elevate your prompt engineering skills and develop innovative AI applications that meet diverse user needs with precision. As AI continues to evolve, staying proficient in the latest prompt engineering techniques will be essential.