enterprise-rag-template

A structured template for Retrieval Augmented Generation (RAG) projects designed for enterprise and regulated environments. It focuses on reliability, evaluation, governance and cost-awareness, with optional extensions for agentic workflows.

Scope of this template

1. Data ingestion and preparation

• Document loaders (PDF, DOCX, HTML, knowledge bases)
• Chunking strategies
• Metadata design
• PII-sensitive processing considerations

2. Embedding and indexing

• Vector store configuration
• Embedding model selection guidance
• Index update routines
• Storage and cost considerations

3. Retrieval patterns

• Retriever configurations for various query types
• Hybrid and re-ranking options
• Controls to reduce retrieval errors

4. Generation and grounding

• Prompt structuring
• Context window optimisation
• Techniques to reduce unsupported claims

5. Evaluation

• Accuracy, relevance and groundedness checks
• Risk evaluations: unsupported statements, leakage, sensitivity issues
• Cost modelling and usage tracking

6. Governance integration

• Logging and traceability
• Model versioning and audit support
• Approval points and human-in-the-loop design

7. Deployment guidance

• Implementation options (local, cloud, managed services)
• Latency and cost trade-offs
• Operational considerations

8. Agentic extensions

• Retrieval-aware agents
• Multi-step reasoning patterns
• Tool-use integration

Intended audience

• AI CoE teams
• Enterprise architects
• Data and ML teams working in regulated sectors
• Risk and governance functions reviewing GenAI projects

Roadmap

• Add a worked end-to-end example with evaluation
• Add retrieval-aware agent example
• Add cost-monitoring integration