ψ CodingEnvironment Applied AI · Mathematical Systems

Technical Architecture

graph TD
    subgraph Input
        A[Policy Documents] --> B[Chunking Layer]
        C[SOPs] --> B
        D[Process Guides] --> B
    end
    
    subgraph Embedding
        B --> E[Embedding Model]
        E --> F[Vector Database]
    end
    
    subgraph Retrieval
        G[User Query] --> H[Query Embedding]
        H --> I[Similarity Search]
        F --> I
        I --> J[Top-K Chunks]
    end
    
    subgraph Generation
        J --> K[Context Assembly]
        K --> L[LLM Generation]
        L --> M[Response with Citations]
    end
    
    subgraph Guardrails
        M --> N[Hallucination Filter]
        N --> O[Grounded Response]
    end
    
    O --> P[User Interface]
            

Architecture: Documents are chunked and embedded into a vector database. User queries are embedded and matched via similarity search. Top-K chunks are assembled with context and passed to an LLM for generation. Guardrails filter hallucinations before returning grounded responses with citations.

Decision Tradeoffs

Problem

HR and Finance teams fielded recurring questions about policies, procedures, and SOPs. Staff spent significant time searching for information in PDFs, SharePoint, and email threads. Response times were slow and inconsistent.

Before/After

Approach

I led design and deployment of a retrieval-augmented generation (RAG) system that indexes internal documents, matches user queries via semantic search, and generates grounded responses with source citations. The system includes guardrails to minimize hallucinations and ensure responses are traceable to approved documents.

Outcome

Deployed for HR and Finance functions with a reusable pattern for subsequent assistants. The architecture supports multi-plant expansion with consistent knowledge base and governance controls.