Automated Intelligence Reporting (LLM) documentation

This project documents a reporting-assistant workflow for programme, donor, and M&E teams. It is designed around evidence-based narrative generation, not unsupported free-form text.

Architecture

The demo follows a structured flow from data input to detection/analysis, human review, and reporting output.

Validated field data → Prompt template → LLM-style narrative generation → Human review → Donor brief / executive report

Demo data

The project uses demo sample data stored in data/synthetic/llm_reporting_field_data_sample.json. The fields are created for demonstration only and do not represent any real client, site, donor, beneficiary, or operational file.

Quality controls

• Uses structured demo indicators and risks.
• Narratives are tied to target-vs-actual values.
• Human review is required before external submission.
• Designed to reduce hallucination by keeping responses grounded in supplied data.

Limitations and production upgrade path

The sandbox is a browser demo. A production version would require secure backend processing, role-based access, approved prompt templates, review workflows, source citations, and audit logs for each generated report.