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.
- Document loaders (PDF, DOCX, HTML, knowledge bases)
- Chunking strategies
- Metadata design
- PII-sensitive processing considerations
- Vector store configuration
- Embedding model selection guidance
- Index update routines
- Storage and cost considerations
- Retriever configurations for various query types
- Hybrid and re-ranking options
- Controls to reduce retrieval errors
- Prompt structuring
- Context window optimisation
- Techniques to reduce unsupported claims
- Accuracy, relevance and groundedness checks
- Risk evaluations: unsupported statements, leakage, sensitivity issues
- Cost modelling and usage tracking
- Logging and traceability
- Model versioning and audit support
- Approval points and human-in-the-loop design
- Implementation options (local, cloud, managed services)
- Latency and cost trade-offs
- Operational considerations
- Retrieval-aware agents
- Multi-step reasoning patterns
- Tool-use integration
- AI CoE teams
- Enterprise architects
- Data and ML teams working in regulated sectors
- Risk and governance functions reviewing GenAI projects
- Add a worked end-to-end example with evaluation
- Add retrieval-aware agent example
- Add cost-monitoring integration