RAG System Audit Template
Comprehensive Evaluation Framework for RAG Pipelines
RAG systems fail silently. This audit template helps you systematically evaluate retrieval quality, chunking strategies, embedding models, and end-to-end accuracy.
Key Features:
End-to-end RAG pipeline evaluation framework
Retrieval quality metrics (precision, recall, MRR, nDCG)
Chunking strategy assessment methodology
Embedding model comparison templates
Hallucination detection for RAG outputs
Knowledge base coverage analysis
Audit Components:
- •📚 **Document Ingestion** - Parsing quality, metadata extraction, deduplication
- •✂️ **Chunking Strategy** - Size optimization, semantic coherence, overlap analysis
- •🧮 **Embedding Quality** - Model selection, dimension analysis, semantic similarity
- •🔍 **Vector Search** - Index performance, query optimization, ranking quality
- •🎯 **Retrieval Accuracy** - Precision@K, recall, MRR, nDCG metrics
- •🤖 **Generation Quality** - Groundedness, citation accuracy, hallucination rate
- •⚡ **Performance** - Latency breakdown, caching effectiveness, cost per query
- •🔒 **Security** - Access control, PII leakage, prompt injection via documents
- •📊 **Monitoring** - Drift detection, failure mode analysis, quality degradation
- •✅ **Test Cases** - 50+ evaluation prompts across difficulty levels
Perfect For:
RAG EngineersML Platform TeamsData EngineersAI Product TeamsSearch EngineersKnowledge Management
"Our RAG system had a 31% hallucination rate and we didn't know it. This audit caught it immediately. After implementing their recommendations, we're down to 4%."
James Liu
Senior ML Engineer, Legal Tech AI Company
Download Your Free Resource
Enter your email to get instant access
5,000+
Downloads
4.9/5
Rating
100%
Free
Why BeaconShield Labs?
Trusted by Fortune 500 & defense contractors
Battle-tested methodologies from real engagements
Used by AI safety teams worldwide