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Design a RAG system for vehicle documents

SonatusSoftware Engineer · Technical Screen
HardSoftware EngineerTechnical ScreenSystem Design

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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.

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

  1. Users ask natural-language questions (e.g., “What torque spec for …?”, “Is this covered under warranty?”).
  2. System retrieves relevant passages/snippets and generates an answer grounded in the documents.
  3. Provide citations (doc + page/section/row reference where possible).
  4. 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.