Enterprise Policy & Pricing Intelligence Assistant
“A targeted RAG assistant engineered to answer customer inquiries strictly from verified documents to reduce unsupported responses.”
Bài Toán & Bối Cảnh Thực Tế
Off-the-shelf ChatGPT integrations answer unpredictably, inventing discounts, quoting obsolete terms, or guessing return policies when queried by prospective customers. Rule-based chatbots are too brittle to understand natural conversational intent.
Yêu Cầu & Ràng Buộc Kỹ Thuật
Grounded retrieval: Answers must be 100% cited from approved internal policy and price documentation.
Explicit fallback: The assistant must decline answering when information is absent from source chunks.
Automated sales lead detection with instant staff notification.
Giải Pháp Kiến Trúc Cốt Lõi
We built a Retrieval-Augmented Generation (RAG) assistant using Supabase pgvector. Internal policy documents are chunked and embedded with vector representations. When a query is received, high-dimensional cosine similarity locates the top 3 relevant chunks, which are injected into a gated system prompt that forbids extrapolating beyond the text.
PostgreSQL Vector Search via pgvector
Embeddings are indexed directly inside relational database tables, eliminating the need for expensive third-party vector databases.
Gated System Prompt Engineering
System instructions enforce strict epistemic boundaries: If the source chunk does not contain the answer, the model gracefully redirects to human staff.
Real-Time Lead Extraction Hook
A background function scans conversational intent for contact requests and immediately forwards prospective client inquiries to Telegram.
Các Hạng Mục Đã Chuyển Giao
Bài Học Thực Chiến & Đúc Kết Kỹ Thuật
“Constraining the LLM context window to verified internal documents significantly reduces unsupported answers while delivering instant 24/7 responsiveness under defined test queries.”
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