To pass variables such as date and events dynamically into a prompt, you can use placeholders in your prompt template and substitute them with actual values in your code. Here's how to manage this effectively:

1. Define a Prompt with Placeholders

Design your prompt with placeholders like {date} and {events} to make it flexible.

Example Template:

```plaintext Instruction: Generate a detailed narrative for the given date and events.

Date: {date} Events: {events}

Task: Write a structured narrative summarizing the events for {date}, highlighting key trends and insights. ```

2. Replace Placeholders with Variables

Use string formatting or template substitution in your programming language to insert actual values into the placeholders.

Python Example:

```python

Template with placeholders

prompt_template = """ Instruction: Generate a detailed narrative for the given date and events.

Date: {date} Events: {events}

Task: Write a structured narrative summarizing the events for {date}, highlighting key trends and insights. """

Variables to pass

date = "2024-11-22" events = [ "Black Friday sales reached $5M", "Customer traffic increased by 20%", "New product launches gained 15% market share" ]

Convert events to a string

events_str = "/n".join([f"- {event}" for event in events])

Populate the template

final_prompt = prompt_template.format(date=date, events=events_str)

Print the final prompt

print(final_prompt) ```

3. Example Output

The dynamically populated prompt will look like this: ```plaintext Instruction: Generate a detailed narrative for the given date and events.

Date: 2024-11-22 Events: - Black Friday sales reached $5M - Customer traffic increased by 20% - New product launches gained 15% market share

Task: Write a structured narrative summarizing the events for 2024-11-22, highlighting key trends and insights. ```

4. Advanced Example with Multiple Variables

If there are multiple types of variables (e.g., stats, key highlights, goals), you can expand the template accordingly.

Template:

```plaintext Instruction: Generate a narrative for the following details.

Date: {date} Key Events: {events} Statistics: - Sales: {sales} - Customer Traffic: {traffic}

Task: Write a report summarizing the events and statistics, and suggest key focus areas for improvement. ```

Python Code:

```python

Template with placeholders

prompt_template = """ Instruction: Generate a narrative for the following details.

Date: {date} Key Events: {events} Statistics: - Sales: {sales} - Customer Traffic: {traffic}

Task: Write a report summarizing the events and statistics, and suggest key focus areas for improvement. """

Variables

date = "2024-11-22" events = ["Record Black Friday sales", "Customer complaints reduced by 15%", "New products launched"] sales = "$5M" traffic = "20% increase"

Convert events to a string

events_str = "/n".join([f"- {event}" for event in events])

Populate the template

final_prompt = prompt_template.format(date=date, events=events_str, sales=sales, traffic=traffic)

Print the final prompt

print(final_prompt) ```

5. Example Output

```plaintext Instruction: Generate a narrative for the following details.

Date: 2024-11-22 Key Events: - Record Black Friday sales - Customer complaints reduced by 15% - New products launched Statistics: - Sales: $5M - Customer Traffic: 20% increase

Task: Write a report summarizing the events and statistics, and suggest key focus areas for improvement. ```

6. Dynamic Integration in API Calls

When using OpenAI’s API, pass the dynamically created final_prompt as part of your request payload.

Example:

```python import openai

response = openai.ChatCompletion.create( model="gpt-4", messages=[ {"role": "system", "content": "You are a data analyst and report writer."}, {"role": "user", "content": final_prompt} ] )

print(response['choices'][0]['message']['content']) ```

Key Benefits of This Approach

1. Reusability: The same prompt template can be reused with different variables.
2. Flexibility: Easily adapt to new types of inputs (e.g., adding new variables like goals or risks).
3. Clarity: Clearly separates the prompt structure from the data, making debugging and updates easier.
4. Dynamic Adaptation: Generate context-specific prompts based on the inputs and conditions.

By leveraging placeholders and dynamic substitution, you can create highly flexible and modular prompts for your Business Analyst Agent.