Design a RAG system for vehicle documents
Quick Overview
This question evaluates a candidate's ability to design a Retrieval-Augmented Generation system, testing competencies in information retrieval, vector search, document ingestion and chunking, metadata modeling, citation handling, security, observability, and scalability within the system design and NLP/information-retrieval domain.
Sonatus
Feb 12, 2026, 12:00 AM
Software Engineer
Technical Screen
System Design
1
0
Design a Retrieval-Augmented Generation (RAG) architecture to power a Q&A chatbot over vehicle documentation.
Document types & characteristics
- Sources: engine specification manuals, warranty documents, repair documents, vehicle datasets.
- Formats: PDFs (1 to 1,000 pages), CSVs (potentially large), HTML, Markdown.
- Workload: read-heavy .
- Updates: documents are static (assume no edits after publishing, but new document versions may be added).
Core requirements
- Users ask natural-language questions (e.g., “What torque spec for …?”, “Is this covered under warranty?”).
- System retrieves relevant passages/snippets and generates an answer grounded in the documents.
- Provide citations (doc + page/section/row reference where possible).
- Handle both unstructured text (PDF manuals) and structured/semi-structured data (CSV datasets).
Non-functional requirements (discuss and propose targets)
- Latency and throughput goals for chat requests.
- Quality: relevance, correctness, citation accuracy.
- Security: document access control if documents differ by vehicle model, region, or user entitlements.
- Observability: tracing, retrieval diagnostics.
- Scalability: growing corpus size.
Deliverables
- High-level architecture (ingestion/indexing + query-time flow).
- Data model for documents/chunks/metadata.
- Vector database choice/strategy and retrieval approach.
- How to process PDFs/CSVs for chunking and citations.
- Key tradeoffs and failure modes.
Solution
Show