Case Study

ChatGenius

Production RAG assistant that ingests company documents and answers questions with source citations. Custom retrieval pipeline with vector search, chunking strategies, and streaming LLM responses.

Next.jsOpenAIPineconeLangChainTailwind

10K+ documents indexed

<800ms time-to-first-token

95% citation accuracy

The Problem.

Companies struggle to make internal knowledge accessible. Employees waste hours searching through scattered docs, wikis, and Slack threads to find answers.

Architecture.

Document ingestion pipeline with PDF/DOCX/TXT parsing

Custom chunking strategy with overlap for context preservation

OpenAI embeddings → Pinecone vector store for semantic retrieval

Hybrid search combining keyword matching + vector similarity

Streaming LLM responses with source citation linking

Next.js frontend with real-time chat interface

Technical Challenges.

Optimized chunking strategy to preserve context across document sections — tested 5 approaches before settling on recursive splitting with 200-token overlap

Built citation system that maps LLM output spans back to source documents with paragraph-level precision

Achieved sub-second time-to-first-token through streaming and parallel retrieval

Results.

Handles 10K+ documents with consistent retrieval quality
Sub-second response time with streaming output
Source citations on every answer for verifiability

Interested in building something similar?

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