RAG Eligibility Fit Scoring (0-100)
RAG Fit Scoring is an automated 0-100 eligibility index evaluating an NGO's technical solvency against official tender clauses with zero AI hallucinations.
Retrieval-Augmented Generation (RAG) Fit Scoring is an algorithmic evaluation process developed by FundingQuarry to predict grant application success probability before writing.
Unlike generic Search AI engines, the RAG architecture matches non-profit solvency profiles (bylaws, historical reports, budgets) against exact tender specification clauses.
The system produces a 0-100 Fit Score along with an audit breakdown of compliance strengths, missing prerequisites, and required compliance documents.
Key Regulatory Takeaways
- Instant eligibility evaluation in under 60 seconds.
- Zero AI hallucinations via strict PDF tender clause retrieval.
- Automated audit of technical and financial solvency thresholds.
Official References & Sources
- RAG (Retrieval-Augmented Generation) Architecture Standards
Frequently Asked Questions
Why is RAG Fit Scoring superior to standard ChatGPT queries?
Standard LLMs hallucinate rules and miss PDF tender clauses. RAG parses exact official clauses directly against your organizational repository.
What Fit Score threshold recommends submission?
We recommend focusing proposal resources on opportunities scoring 75/100 or higher.
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