SayPro GPT Topic Extraction Template

Project Name: [Insert Project Name]
Report/Document Name: [Insert Name of the Document]
Date of Extraction: [Insert Date]
Extracted By: [Your Name/Position]

1. Overview of Data

Provide a brief description of the data from which topics will be extracted. This can include the source, type of document, and any relevant context.

• Source of Data: [e.g., beneficiary surveys, field reports, project evaluations, etc.]
• Document Length: [e.g., number of words, pages, or size of data]
• Purpose of Extraction: [Why the data is being extracted and how it will be used (e.g., to identify recurring themes, inform decision-making, etc.)]

2. Pre-Extraction Process

List the steps taken before running the GPT model for topic extraction to ensure that the data is properly prepared.

• Data Cleaning:
  • [e.g., Removal of irrelevant data, correcting misspellings, removing non-informative content]
• Text Tokenization:
  • [e.g., Splitting the text into manageable chunks (sentences/paragraphs)]
• Text Normalization:
  • [e.g., Converting all text to lowercase, removing special characters, etc.]
• Data Filtering:
  • [e.g., Removing stop words, non-relevant sections of text]

3. GPT Model Setup

Define the specific setup used for the topic extraction process, including settings and configuration for GPT.

• GPT Version: [e.g., GPT-4, GPT-4-mini]
• Parameters Used:
  • Temperature: [e.g., 0.7 – Higher values for creativity, lower for precision]
  • Max Tokens: [e.g., 300 tokens – To control the output length]
  • Top P: [e.g., 0.9 for more focused output]
  • Frequency Penalty: [e.g., 0.2 to reduce repetitive outputs]
  • Prompt Used: [The exact text or prompt fed into GPT to generate the topics]

4. Topic Extraction Process

Describe how the topic extraction was performed using the GPT model, including any post-processing steps.

• Methodology:
  • Used GPT’s ability to identify major themes and topics from large text datasets.
  • Applied NLP techniques such as topic modeling, keyword extraction, or clustering.
• Text Segmentation:
  • The document was divided into smaller chunks (if necessary) for better processing.
• Topic Extraction:
  • GPT was used to generate a list of key topics or themes present in the document.
• Output Analysis:
  • The generated topics were manually reviewed to ensure relevance and accuracy.

5. Extracted Topics

Provide a list of the most relevant topics, themes, or keywords identified by the GPT model. Each topic can be followed by a brief description of why it was extracted.

Note: Extracted topics may also be presented as a frequency analysis or top keywords if desired.

6. Insights and Analysis

Based on the extracted topics, provide an analysis or insights that are relevant to the project. This section explains how the identified topics can be used to inform decisions, improve project strategies, or highlight areas for further exploration.

• Key Insights:
  • [Insight #1, e.g., “A common theme in beneficiary feedback is the need for more timely communication from field staff.”]
  • [Insight #2, e.g., “A recurring issue is the lack of community engagement in remote regions, which could affect project success.”]
  • [Insight #3, e.g., “Stakeholder support is high, but there is a growing concern about resource allocation.”]
• Implications for the Project:
  • [e.g., “Based on feedback about communication, project leaders may consider improving information-sharing protocols.”]
  • [e.g., “In areas with low community participation, additional efforts may be needed to involve local stakeholders in project activities.”]

7. Recommendations

Based on the extracted topics and insights, propose actionable recommendations for the project team or stakeholders to consider moving forward.

• Recommendation #1: [e.g., “Increase training for field staff on communication skills to address concerns raised by beneficiaries.”]
• Recommendation #2: [e.g., “Design a targeted engagement campaign in remote areas to boost participation and awareness.”]
• Recommendation #3: [e.g., “Monitor resource allocation more closely to ensure that high-priority areas receive adequate attention.”]

8. Conclusion

Summarize the key findings from the topic extraction process and their potential impact on the project. This section should emphasize the importance of continuous analysis and how extracted topics can help improve decision-making in future project phases.

• Conclusion:
  • [e.g., “The topic extraction process has provided valuable insights into stakeholder concerns and areas of opportunity. By addressing these topics, the project can better align with beneficiary needs and improve overall impact.”]

9. Attachments/References

Include any references, documents, or data that support the extraction process or that could help in further analysis.

• [List of attached documents or external references, if any]

Report Prepared By:
[Your Name]
[Your Position]
[Date of Submission]