Update dataset README and metadata for drift detection frameworks

Updates the dataset README for AI drift detection frameworks.

Clarifies:

drift detection across data, performance, behavioral, semantic, and system layers
application to LLMs, agents, and production AI systems
focus on alignment, usefulness, and real-world performance over time

README.md

@@ -1,3 +1,113 @@
----
-license: cc-by-4.0
----
+---
+license: cc-by-4.0
+task_categories:
+- text-generation
+- question-answering
+- text-retrieval
+language:
+- en
+tags:
+- llm
+- ai
+- model-drift
+- drift-detection
+- evaluation
+- ai-alignment
+- monitoring
+- mlops
+- agents
+- ai-governance
+- ai-risk
+- model-evaluation
+- semantic-drift
+- system-analysis
+- reliability
+pretty_name: AI Drift Detection Frameworks
+---
+# AI Drift Detection Frameworks
+
+A structured collection of frameworks, checklists, and evaluation methods for detecting drift in AI systems, including large language models (LLMs), agent workflows, and production machine learning systems.
+
+---
+
+## Overview
+
+Most AI systems do not fail abruptly. Outputs remain fluent, structured, and internally consistent.
+
+But systems can degrade while still appearing to work.
+
+This dataset documents a recurring pattern:
+
+> Systems preserve coherence while gradually losing alignment with intent, context, and real-world conditions.
+
+Each document focuses on a different layer of drift detection and reframes model degradation as a structural issue rather than a visible failure.
+
+---
+
+## Contents
+
+- [LLM Drift Detection — Why AI Outputs Degrade Without Errors](./llm-drift-detection-why-ai-outputs-degrade-without-errors.pdf)  
+
+- [AI Model Audit Checklist — Drift Detection in Production Systems](./ai-model-audit-checklist-drift-detection.pdf)  
+
+- [Model Drift Detection Framework — Machine Learning Systems](./model-drift-detection-framework-machine-learning.pdf)  
+
+- [Institutional Drift Detection Framework](./institutional-drift-detection-framework.pdf)
+
+---
+
+## Drift Types Covered
+
+- Data drift (input distribution changes)  
+- Performance drift (metric-level degradation)  
+- Behavioral drift (changes in system outputs)  
+- Semantic drift (loss of meaning or intent alignment)  
+- System drift (compounding misalignment across workflows)  
+
+---
+
+## Core Idea
+
+Standard evaluation focuses on accuracy and correctness.
+
+This framework focuses on whether systems remain:
+
+- aligned with user intent  
+- grounded in real-world conditions  
+- useful over time  
+
+Drift often emerges without triggering metrics, making it difficult to detect using traditional monitoring approaches.
+
+---
+
+## Intended Use
+
+This dataset is useful for:
+
+- monitoring LLMs and production AI systems  
+- designing evaluation frameworks beyond accuracy  
+- analyzing agent and multi-step system behavior  
+- implementing AI governance and risk frameworks  
+- detecting alignment failures in real-world deployments  
+
+---
+
+## Not Intended For
+
+This is not a benchmark dataset or training dataset.  
+It is a conceptual and diagnostic resource for understanding system behavior and detecting drift in deployed AI systems.
+
+---
+
+## Core framework and sources
+
+- Research Library (GitHub): [Semantic Fidelity Lab Repository](https://github.com/therealitydrift/semantic-fidelity-lab)
+- Articles & Essays (Substack): [Reality Drift](https://therealitydrift.substack.com)
+- Primary DOI Record: [Figshare Collection](https://figshare.com/)
+- Concept Glossary: [Semantic Fidelity Glossary](https://offbrandguy.com/semantic-fidelity-glossary/)
+
+---
+
+## License
+
+CC BY 4.0