README.md

metadata

title: Knowledge Value Lab
emoji: 🔬
colorFrom: blue
colorTo: indigo
sdk: docker
app_port: 7860
pinned: false
license: apache-2.0

Knowledge Value Lab (KVL)

Measuring the Marginal Value of Knowledge Assets for AI Systems

KVL quantifies how much a knowledge document contributes to AI systems across five dimensions, producing a single weighted Knowledge Value Score (KVS).

How to Use

1. Upload a Markdown (.md) document
2. Click Evaluate Knowledge Value
3. Review the scored report and download it

Dimensions

Dimension	Weight	What it measures
Knowledge Novelty	30%	How much of the document is unknown to the base model
Retrieval Utility	20%	How well the document surfaces in RAG search
Generation Utility	25%	How much RAG answers improve over baseline
Attribution & Grounding	15%	How faithfully answers are grounded in the document
Demand Utility	10%	How frequently this knowledge is needed by users

Score Classifications

Score	Classification
81–100	Transformational Value
61–80	High Value
41–60	Moderate Value
21–40	Incremental Value
0–20	Minimal Value

Important Note

Knowledge Novelty and Generation Utility scores are model-relative — they measure value against specific AI models and will change when models are updated. Always report scores alongside the model names and evaluation date shown in each report.