This was basically the whole interview rolled into one question.
Structure your answer as an end-to-end pipeline, starting with ingestion and chunking, then moving through embedding, storage, hybrid retrieval, and prompt construction. Emphasize evaluation for faithfulness and strategies for content freshness, tying each choice to trade-offs in latency, cost, and accuracy. Use the vehicle documentation context to illustrate domain-specific challenges like multi-modal files and versioning.
Pro tip: Proactively discuss how you would handle versioned documents and stale content—e.g., by attaching metadata and using time-decay in retrieval—since this is a common pain point in automotive documentation and shows you think beyond the happy path.
Design a pipeline to ingest PDFs, HTML, images, and structured data, extracting text and metadata while handling OCR for scanned documents. Normalize content and attach source, version, and timestamp metadata for later filtering.
Choose a chunking strategy (e.g., semantic or fixed-size with overlap) that preserves context, and select an embedding model suited to technical text. Discuss trade-offs between chunk size, retrieval granularity, and computational cost.
Store embeddings in a vector database (e.g., Pinecone, Weaviate) and implement hybrid retrieval combining dense vectors with sparse methods like BM25. Explain how to fuse results and apply metadata filters for version or vehicle model.
Construct prompts that include retrieved context, user query, and instructions to cite sources and avoid hallucination. Discuss techniques like re-ranking, context window management, and fallback strategies when confidence is low.
Define metrics for faithfulness (e.g., groundedness, answer relevance) and set up automated evaluation with human-in-the-loop. Implement freshness by monitoring document updates, re-indexing changed content, and using time-aware retrieval.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.