RAG Knowledge Assistant — Internal AI Support Prototype
Internal Retrieval-Augmented Generation prototype for grounded Q&A across service pages, profile content, and operational documentation.
About the Project
This project is a grounded-answer prototype designed to test how a RAG assistant can answer business and service questions using only curated project knowledge.
The goal is not generic chatbot output. The goal is controlled, source-bound answers with clear boundaries and lower hallucination risk.
Problem
Most generic assistants produce plausible text, but often miss business context or mix unsupported claims. For service pre-sales and internal handoff, that is too risky.
Architecture
- Source layer: profile data, service content, route/structure docs, and selected project pages
- Retrieval layer: chunking and relevance filtering against curated documents
- Generation layer: constrained prompts that force source-grounded responses
- Review layer: manual check for unsupported claims and terminology drift
Measurable Outcomes
- Single-source domain glossary in place for terminology consistency
- Structured service corpus covering 6 service pillars
- Multilingual source base across DE, EN, and HR content layers
- Explicit anti-hallucination rule set documented in service-facing AI content pages
Why It Matters
The prototype validates a practical AI service direction: assistants that are useful in real workflows because they are grounded in your actual documentation and service model.
Next Iteration
Next step is adding task-specific evaluation sets (support, pre-sales, and internal onboarding) with precision scoring and regression checks.