RAG Knowledge Search
Ask questions about AI, machine learning, or software engineering. I'll search my knowledge base and provide answers with source citations.
Semantic Search
Your question is embedded and matched against document chunks
Context Retrieval
The most relevant chunks are selected as context
AI Synthesis
GPT generates an answer grounded in retrieved sources
Try asking:
💡 What to Try
Add Your Own Documents
Click "Add Doc" above to ingest your own text. The system will chunk, embed, and index it for retrieval. Try adding notes, articles, or documentation.
Enable Dev Mode
Toggle Dev Mode (wrench icon) to see the full RAG pipeline: embeddings, similarity scores, retrieved chunks, and the prompts sent to GPT.
Compare Responses
Ask the same question before and after adding documents. See how retrieval-augmented generation differs from pure LLM responses.
Track Usage
Click "Stats" to see token usage and estimated costs. Useful for understanding the economics of RAG systems.