AI PlatformInternal R&D Deployment
RAG-Powered Questionnaire Engine
Challenge: The team needed dynamic, context-specific question generation without static templates.
Solution: Designed a retrieval-augmented generation (RAG) system that dynamically grounds question generation in real-time contextual data and task intent.
System Architecture
User → API → RAG Layer → Vector DB → LLM → Validation Layer → Output
Scale & Stack: Built on Supabase + vector embeddings with retrieval latency under 200ms.
RAGNLPKnowledge Systems
32%
Fewer revision cycles
<200ms
Retrieval latency
High
Output grounding