AI RAG Document Chat & Assistant Platform
Intelligent conversational search platform using Next.js, OpenAI API, Qdrant Vector DB & TypeScript
AI Application Developer (RAG pipeline implementation, vector embeddings, prompt engineering, Next.js frontend UI).
The Problem & Business Challenge
Users struggled to extract relevant answers quickly from massive PDF documentation and technical manuals.
Business Goal
Build an AI tool that indexes technical documents and provides instantly accurate contextual answers grounded strictly in user documents.
Technical Architecture & Stack Rationale
Retrieval-Augmented Generation (RAG) architecture. Documents are chunked, converted into vector embeddings via OpenAI Ada models, stored in Qdrant, and retrieved semantically to inject into GPT-4 prompts.
Key Implemented Features
- Drag-and-drop document upload (PDF, TXT, DOCX) with automated chunking
- Semantic vector similarity search returning precise document citations
- Streaming AI response rendering for natural conversational chat UX
- Markdown formatting with syntax highlighting for code snippets
- Persistent conversation thread history stored securely
Technical Challenges & Solutions
Challenges Faced:
- Preventing LLM hallucinations when asked out-of-scope questions
- Optimizing vector retrieval latency for multi-page documents
Solutions Implemented:
- Engineered strict system prompt boundaries enforcing retrieval citations or fallback state
- Tuned vector similarity thresholds and document chunk overlap settings in Qdrant
Verified Outcomes & Results
Delivered average response turnaround in under 1.2 seconds
Achieved 95%+ factual accuracy on technical document querying
Demonstrated state-of-the-art AI integration capabilities