Creating a prompt library or template for generating narratives from cricket data in JSON format requires identifying the specific insights or narratives you want to extract. Below is a step-by-step approach to designing prompts and building a reusable enterprise library:

1. Analyze Cricket Data and Desired Narratives

Typical cricket data might include: - Match details: Teams, date, location, format (e.g., ODI, Test, T20). - Player stats: Runs, wickets, strike rate, economy, etc. - Match summary: Scores, result, top performers, partnerships. - Innings breakdown: Over-by-over performance, key moments. - Comparison: Head-to-head stats, player rankings, etc.

2. Build Prompt Templates for Common Use Cases

Design modular prompts to handle different scenarios in cricket narratives:

A. Match Summary

Prompt Template: ```plaintext Generate a match summary based on the following data: - Match Format: {match_format} - Teams: {team_1} vs {team_2} - Location: {location} - Result: {result} - Top Performers: {top_performers} - Key Highlights: {highlights}

Match Data: {json_data} ```

B. Player Performance

Prompt Template: ```plaintext Analyze the player performance for {player_name} in the match: - Runs: {runs} - Balls Faced: {balls_faced} - Strike Rate: {strike_rate} - Wickets Taken: {wickets} - Overs Bowled: {overs} - Economy Rate: {economy}

Provide a detailed narrative on their performance. Data: {json_data} ```

C. Team Comparison

Prompt Template: ```plaintext Compare the performance of {team_1} and {team_2} based on the following metrics: - Total Runs - Wickets Lost - Top Scorer - Bowling Economy

Provide insights into which team performed better and why. Match Data: {json_data} ```

D. Innings Breakdown

Prompt Template: ```plaintext Create a detailed breakdown of {team}'s innings: - Powerplay Performance: {powerplay_stats} - Key Partnerships: {key_partnerships} - Fall of Wickets: {fall_of_wickets} - Death Overs Performance: {death_overs_stats}

Data: {json_data} ```

E. Key Moments

Prompt Template: ```plaintext Highlight the key moments of the match between {team_1} and {team_2}: - Game-Changing Partnerships - Critical Wickets - Top Performances - Turning Points

Data: {json_data} ```

3. Build an Enterprise Prompt Library

Develop a system to manage and reuse these templates.

Structure of the Library

• Modular Design: Each narrative type (summary, player analysis, comparison, etc.) is a separate module.
• Dynamic Variables: Use placeholders like {team}, {player_name}, {match_format} for flexibility.
• JSON Parser: Include a preprocessing layer to extract and format relevant fields from the raw JSON.

Example Structure:

plaintext enterprise_prompts/ ├── match_summary_prompt.txt ├── player_performance_prompt.txt ├── team_comparison_prompt.txt ├── innings_breakdown_prompt.txt └── key_moments_prompt.txt

4. Automate Data Integration

Use a script or tool (e.g., Python with libraries like json, Jinja2, or LangChain) to populate templates dynamically:

Example Code:

```python import json from jinja2 import Template

Load JSON data

with open('cricket_data.json', 'r') as file: cricket_data = json.load(file)

Load template

with open('match_summary_prompt.txt', 'r') as file: template_text = file.read()

Populate template

template = Template(template_text) prompt = template.render( match_format=cricket_data['match_format'], team_1=cricket_data['teams'][0], team_2=cricket_data['teams'][1], location=cricket_data['location'], result=cricket_data['result'], top_performers=cricket_data['top_performers'], highlights=cricket_data['highlights'] )

Send prompt to LLM

print(prompt) ```

5. Enhance with AI Features

a. Pre-Built Libraries

• Use tools like LangChain or PromptLayer to manage, version, and optimize prompts.

b. Fine-Tuning

• Fine-tune an LLM on cricket-specific data to improve narrative generation.

c. Adaptive Prompts

• Build dynamic prompts that adjust based on input data size or type, e.g., generating concise narratives for T20 and detailed ones for Test matches.

6. Consider Domain-Specific Enhancements

• Add cricket jargon (e.g., "all-rounder performance," "death overs heroics").
• Tailor tones for different audiences (casual fans, analysts, commentators).

By combining these elements, you can create a robust enterprise-grade system for generating cricket narratives from structured data.