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Design a RAG System for Vehicle Documents
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 (ranging from 1 to 1,000 pages), CSVs (potentially large), HTML, Markdown.
- Workload: The system is expected to be read-heavy.
- Updates: Documents are considered static (no edits after publishing), but new document versions may be added over time.
Core Requirements
- Users must be able to ask natural-language questions (e.g., “What torque spec for …?”, “Is this covered under warranty?”).
- The system must retrieve relevant passages or snippets from the documentation and generate an answer that is strictly grounded in these documents.
- Citations must be provided, including the document name and, where possible, a specific page, section, or row reference.
- The system needs to handle both unstructured text (e.g., PDF manuals) and structured or semi-structured data (e.g., CSV datasets).
Non-Functional Requirements (Discuss and Propose Targets)
Candidates should discuss and propose targets for the following non-functional requirements:
- Latency and Throughput: Goals for chat requests.
- Quality: Relevance, correctness, and citation accuracy of generated answers.
- Security: Implement document access control based on factors like vehicle model, region, or user entitlements.
- Observability: Include mechanisms for tracing and retrieval diagnostics.
- Scalability: The system must be scalable to accommodate a growing corpus size.
Deliverables
- A high-level architecture covering both the ingestion/indexing pipeline and the query-time flow.
- A data model for documents, chunks, and associated metadata.
- A strategy for vector database choice and retrieval approach.
- A detailed plan on how to process PDFs and CSVs for effective chunking and citation generation.
- Identification and discussion of key tradeoffs and potential failure modes.
AI interview prep
Core dilemma
Balancing the complexity of processing and accurately retrieving information from highly diverse document formats (unstructured PDFs vs. structured CSVs) for grounded answer generation and precise citation, while meeting stringent requirements for low-latency, high-quality responses, scalability, and fine-grained access control.
Mid-level signals3
Senior signals4
Red flags4
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Design a RAG System for Vehicle Documents
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 (ranging from 1 to 1,000 pages), CSVs (potentially large), HTML, Markdown.
- Workload: The system is expected to be read-heavy.
- Updates: Documents are considered static (no edits after publishing), but new document versions may be added over time.
Core Requirements
- Users must be able to ask natural-language questions (e.g., “What torque spec for …?”, “Is this covered under warranty?”).
- The system must retrieve relevant passages or snippets from the documentation and generate an answer that is strictly grounded in these documents.
- Citations must be provided, including the document name and, where possible, a specific page, section, or row reference.
- The system needs to handle both unstructured text (e.g., PDF manuals) and structured or semi-structured data (e.g., CSV datasets).
Non-Functional Requirements (Discuss and Propose Targets)
Candidates should discuss and propose targets for the following non-functional requirements:
- Latency and Throughput: Goals for chat requests.
- Quality: Relevance, correctness, and citation accuracy of generated answers.
- Security: Implement document access control based on factors like vehicle model, region, or user entitlements.
- Observability: Include mechanisms for tracing and retrieval diagnostics.
- Scalability: The system must be scalable to accommodate a growing corpus size.
Deliverables
- A high-level architecture covering both the ingestion/indexing pipeline and the query-time flow.
- A data model for documents, chunks, and associated metadata.
- A strategy for vector database choice and retrieval approach.
- A detailed plan on how to process PDFs and CSVs for effective chunking and citation generation.
- Identification and discussion of key tradeoffs and potential failure modes.