About This Architecture

Hybrid AI pipeline combining vector retrieval, deterministic business logic, and LLM generation for technician allocation and cost calculation. User requests flow through a preprocessing layer into vector retrieval, then deterministic engines compute technician assignments and costs before context injection feeds an LLM generation layer. Validation, governance, and confidence scoring layers ensure output quality before presentation to AMS integration with human review override. This architecture demonstrates best practices for blending rule-based systems with generative AI to maintain control over critical business logic while leveraging LLMs for natural language output. Fork this diagram on Diagrams.so to customize layers, add monitoring components, or adapt the pipeline for your domain-specific hybrid AI workflow.

People also ask

How do I architect a hybrid AI system that combines deterministic business logic with LLM generation while maintaining governance and confidence scoring?

This diagram shows a production hybrid AI pipeline where user requests pass through preprocessing and vector retrieval, then deterministic engines handle critical calculations like technician allocation and cost before context injection feeds an LLM generation layer, followed by validation, governance, confidence scoring, and human review override to ensure controlled, auditable AI outputs.