To manage prompts effectively for your Business Analyst Agent that generates different narratives based on conditions like the day of the week or the start of the month, you can use a modular and dynamic prompt design. Here's how you can approach it:

1. Define Modular Templates

Break down your prompt into reusable templates based on narrative types. For example: - Weekly Narrative (Monday): Focus on weekly trends, goals, and priorities. - Daily Narrative (Other Days): Focus on daily insights and highlights. - Monthly Narrative (Start of the Month): Focus on monthly summaries, goals, and forecasts.

Example Templates:

python prompts = { "monday": "Analyze the data and generate a narrative focusing on weekly trends, goals, and key areas for improvement.", "other_day": "Analyze today's data and generate a concise narrative highlighting key daily insights and trends.", "start_of_month": "Analyze the data and generate a narrative summarizing the past month's performance and outlining goals for the new month." }

2. Use Dynamic Conditions to Choose Prompts

Use logic in your code to determine which prompt to use based on the date.

Example in Python:

```python from datetime import datetime

def get_prompt(date, data): # Determine the day and whether it's the start of the month day_of_week = date.strftime("%A").lower() # e.g., "monday" is_start_of_month = date.day == 1

# Choose the appropriate template
if is_start_of_month:
    prompt = prompts["start_of_month"]
elif day_of_week == "monday":
    prompt = prompts["monday"]
else:
    prompt = prompts["other_day"]

# Add data and context dynamically
final_prompt = f"{prompt}/n/nData: {data}/n/nGenerate the narrative."
return final_prompt

```

3. Use Context-Specific Prompts

Each narrative type may require additional instructions, examples, or focus areas.

Example for Start of Month:

```plaintext Instruction: Summarize the performance data for the past month and outline key objectives for the new month. Focus on trends, growth opportunities, and risks.

Data: {data}

Task: Generate a detailed narrative highlighting the past month's insights and the outlook for the upcoming month. ```

Example for Monday:

```plaintext Instruction: Based on the data, generate a narrative that highlights the previous week's performance and priorities for the upcoming week.

Data: {data}

Task: Provide a structured report with key trends and recommendations. ```

Example for Other Days:

```plaintext Instruction: Use today's data to create a short narrative focusing on daily trends, challenges, and highlights.

Data: {data}

Task: Summarize the key insights in a concise format. ```

4. Manage Prompts in a Configurable Manner

Use a configuration file or database to store and update templates, making it easier to manage and update without modifying the code.

Example JSON File for Prompts:

json { "monday": "Analyze the data and focus on weekly trends, goals, and areas of improvement.", "other_day": "Analyze today's data and focus on daily highlights and insights.", "start_of_month": "Summarize the past month's performance and provide goals for the new month." }

Loading Prompts in Python:

```python import json

Load prompts from a JSON file

with open("prompts.json", "r") as file: prompts = json.load(file) ```

5. Add Context Dynamically

Depending on the day and the data provided, dynamically append context or examples to guide the agent.

Example:

```python def get_contextualized_prompt(date, data): prompt = get_prompt(date, data)

# Add additional context if needed
context = ""
if date.day == 1:  # Start of month
    context = "Include a breakdown of revenue, cost, and customer growth."
elif date.strftime("%A").lower() == "monday":  # Monday
    context = "Focus on team goals and weekly priorities."
else:  # Other days
    context = "Highlight the most significant daily metrics."

return f"{prompt}/n/nAdditional Context: {context}"

```

6. Consider Using Prompt Variables

Instead of hardcoding prompts, use placeholders to keep templates flexible.

Example:

```python template = "{narrative_type} Analyze the data: {data}. Generate a narrative focusing on {focus_area}."

def generate_prompt(date, data): narrative_type = "Weekly Report" if date.strftime("%A").lower() == "monday" else "Daily Report" focus_area = ( "weekly trends and goals" if date.strftime("%A").lower() == "monday" else "key insights and highlights" if date.day != 1 else "monthly performance and objectives" ) return template.format(narrative_type=narrative_type, data=data, focus_area=focus_area) ```

7. Test and Iterate

Test the narratives generated for different days and refine the prompts based on feedback or observed gaps in the generated text.

8. Consider Fine-Tuning or Using APIs

If your use case involves repetitive narratives, fine-tune a model or use retrieval-augmented generation (RAG) to include historical context dynamically.

This approach allows your Business Analyst Agent to: 1. Handle day-specific and month-specific narratives seamlessly. 2. Be modular and easily extendable for additional scenarios. 3. Dynamically adapt to different dates and contexts while maintaining consistent quality.