RAG-Powered AI Assistant
Context-Aware AI Chat Platform
Overview
Built a production-ready RAG (Retrieval-Augmented Generation) AI assistant that businesses can embed into their websites. The system ingests custom knowledge bases and provides contextually accurate responses while supporting multiple LLM providers for flexibility and cost optimization.
The Challenge
Create an intelligent, context-aware AI assistant that can be embedded into any website with domain-specific knowledge
- Generic chatbots couldn't answer domain-specific questions accurately
- Existing solutions required significant technical expertise to integrate
- No easy way to switch between AI providers based on cost/performance needs
- Real-time streaming responses were essential for good UX but complex to implement
The Solution
Technical Approach
- Implemented vector embeddings using OpenAI's ada-002 for semantic search
- Built chunking pipeline with overlap to maintain context across document segments
- Created abstraction layer supporting OpenAI GPT-4 and Google Gemini interchangeably
- Designed embeddable widget using Shadow DOM for style isolation
Key Decisions
PostgreSQL with pgvector over dedicated vector DB
Why: Reduced infrastructure complexity while maintaining acceptable performance for our scale
Server-Sent Events for streaming
Why: Better browser compatibility than WebSockets for unidirectional real-time data
Admin panel for knowledge management
Why: Non-technical users needed to update FAQs and documentation without developer involvement
Results
Lessons Learned
- 1.Chunk size and overlap significantly impact retrieval quality - requires experimentation
- 2.Prompt engineering is as important as the retrieval system itself
- 3.Users expect instant responses - streaming is not optional for chat interfaces
Tech Stack
Interested in Working Together?
I help companies build scalable systems and solve complex engineering challenges.