AI Automation
RAG Assistant for Knowledge Base Search
Prototype of a retrieval-augmented assistant for querying manuals, policies or internal technical documentation.
Dataset type: simulated. No confidential client or employer data.
Executive Summary
This case describes a retrieval-augmented assistant that answers questions from a controlled knowledge base while keeping source references visible.
Business Question
Can users find reliable answers in manuals, policies or technical documentation faster without losing source traceability?
Statistical Question / Hypothesis
The evaluation asks whether retrieval quality and answer grounding are sufficient for controlled knowledge-base search, using precision-oriented review rather than open-ended generation quality alone.
Dataset
The dataset is simulated and contains policy-like documents, manuals, procedural notes and metadata for source attribution.
Methodology
The workflow uses document chunking, embeddings, vector search, retrieval-augmented generation, answer constraints and source citation review. The assistant is scoped to retrieval and synthesis, not autonomous decision-making.
Implementation
Python handles ingestion, chunking and evaluation. LangChain and LangGraph structure retrieval and response flow. n8n can orchestrate scheduled ingestion or notification steps.
Results
Outputs include answer examples, retrieved source passages, failure cases and a checklist for evaluating unsupported answers.
Limitations
Limitations include source freshness, retrieval misses, ambiguous questions, model hallucination, permission boundaries and the need for human review in high-stakes contexts.
Executive Recommendation
Use RAG for controlled knowledge search where source visibility matters, and define clear escalation paths for incomplete or unsupported answers.
Tools Used
Python, LangChain, LangGraph and n8n.
Links
Notebook, GitHub repository and executive PDF are coming soon.