README.md

---
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.