AI-Powered Knowledge Assistant
RAG-powered internal knowledge engine vectorising documentation for hallucination-free, grounded Q&A.
RAGOpenAI APIVector EmbeddingsVector DatabaseSemantic SearchNode.jsPython
01 — The Challenge
Problem & Context
Internal product specifications, company SOPs, and policy documentation were distributed across disparate formats, creating operational bottlenecks and repetitive queries for senior staff.
02 — Architecture & Design
System Architecture & Flow
Designed and implemented a Retrieval-Augmented Generation (RAG) pipeline that vectorises company documentation and uses OpenAI embeddings with semantic search to provide answers strictly grounded in verified internal data.
AI-Powered Knowledge Assistant Execution PipelineClick stage to inspect
03 — Implementation
Key Engineering Decisions
- Implemented deterministic document parsing with chunk overlapping to preserve semantic boundaries.
- Utilized cosine similarity vector retrieval with a confidence threshold to prevent hallucinations on unindexed queries.
- Grounded OpenAI prompts with strict contextual system prompts instructing the model to cite sources and reject unsupported inferences.
- Engineered lightweight, low-latency search endpoints suited for internal operations.
04 — Results & Value Delivered
Outcome & Operational Impact
Enabled instant, accurate answers to internal company queries grounded in real documentation, eliminating manual lookup overhead and reducing repetitive support requests.