SayPro Guide: Using GPT-Based Prompt Engineering for Thematic Extraction

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1.SayPro Objective

To leverage GPT’s natural language processing capabilities to analyze qualitative data and generate thematic summaries, keyword lists, insights, and recommendations, saving time and improving accuracy in stakeholder reporting and program evaluations.

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2.SayPro Use Cases

• Extracting common themes from focus group transcripts
• Summarizing open-ended survey responses
• Analyzing social media sentiment
• Highlighting key issues and recommendations from feedback
• Categorizing responses by stakeholder group or priority level

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3.SayPro Prompt Engineering Framework

A. Basic Prompt Template (For Thematic Extraction)

pythonCopyEditAnalyze the following stakeholder feedback and provide:
1. A list of key themes mentioned
2. The number of times each theme appears
3. A short summary of the overall sentiment

Feedback:
"""
[Insert all feedback here]
"""

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B. Advanced Prompt Template (Categorization + Insights)

pythonCopyEditFrom the following text, extract:
- The top 5 recurring themes
- Direct quotes that support each theme
- Suggested actions based on the issues raised
- Sentiment classification (Positive, Neutral, Negative) per theme

Text:
"""
[Insert collected responses or focus group transcript here]
"""

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C. Prompt for Stakeholder-Specific Analysis

pythonCopyEditRead the input and group feedback by stakeholder type (Beneficiaries, Partners, Staff). For each group, list:
1. Key concerns
2. Positive feedback
3. Suggested improvements

Input:
"""
[Paste raw qualitative data here]
"""

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4.SayPro Sample Output (from GPT)

Themes:

1. Delayed Service Delivery – mentioned 8 times
2. High Program Satisfaction – mentioned 6 times
3. Need for Better Communication – mentioned 5 times
4. Digital Access Issues – mentioned 4 times

Sentiment Summary:

• 50% Positive
• 30% Neutral
• 20% Negative

Suggested Actions:

• Improve training for field teams on digital platforms
• Provide more frequent program updates to beneficiaries
• Address service delays through staff reallocation

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5.SayPro Best Practices for SayPro

• Use structured templates when collecting feedback to make GPT processing more efficient.
• Always pre-clean your data (remove unrelated content, profanity, or duplicates).
• Run multiple prompt variations to cross-check insights and consistency.
• Store GPT outputs with versioning for audit trail and learning.
• Use human reviewers to validate critical insights before making decisions.

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6.SayPro Tools You Can Use

Tool	Function
ChatGPT / GPT-4	Prompt analysis and text summarization
Excel / Google Sheets	Organize raw input and GPT output
Airtable / Notion	Create structured databases for responses
NVivo / Dedoose	For traditional thematic coding (to cross-reference GPT results)
Power BI / Google Data Studio	Visualize themes and frequencies

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7.SayPro Next Step: Implementation Plan for SayPro

1. Create a Prompt Library: Store and version reusable prompts for different types of feedback.
2. Develop a Data Flow: From collection > cleaning > GPT processing > validation > reporting.
3. Train Internal Teams: Short workshops on how to use prompts effectively.
4. Automate Where Possible: Use GPT API integration for bulk feedback analysis.
5. Document Insights: Use SayPro’s feedback database and reporting template to track changes over time.