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Document-based question-answering application that uses retrieved context to answer questions about a private knowledge base.
01 · Context
Built the application interface, document/knowledge workflow, conversational experience, and LLM integration. Responses are generated from retrieved context rather than relying only on the model's general knowledge.
02 · Problem
Make answers about private documents more grounded and useful than relying only on a model's general knowledge.
03 · Approach
A conversational RAG workflow that retrieves relevant knowledge before generating an answer.
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Full-Stack
A full-stack business application for managing customers, workflows, records and day-to-day operations.
04 · Systems
Next.js, TypeScript, PostgreSQL, OpenAI, RAG
05 · Ownership
I built the application end to end, including the frontend, data flow, retrieval workflow, AI integration, conversational UI, and supporting application logic.
06 · Outcome
Provides a practical interface for asking questions against a private knowledge base with retrieved context.