Start by clarifying requirements (e.g., domain, latency, scale, accuracy) and then walk through the end-to-end pipeline: ingestion, indexing, retrieval, and generation. Emphasize trade-offs at each stage, such as chunking strategies, embedding models, and reranking, and how they impact answer quality and system performance.
Pro tip: Always ground your design in concrete metrics (e.g., recall@k, latency, cost) and discuss how you would evaluate and iterate on the system, showing a data-driven mindset.
Ask about the knowledge base size, update frequency, query types, latency and cost constraints, and accuracy expectations to tailor the design.
Outline document parsing, chunking, embedding generation, and indexing. Discuss trade-offs in chunk size, overlap, and metadata enrichment.
Choose between sparse (e.g., BM25) and dense (e.g., embeddings) retrieval, or hybrid. Explain indexing (e.g., FAISS, HNSW), query processing, and reranking strategies.
Describe how retrieved passages are fed into the LLM, prompt engineering for grounding, and techniques to reduce hallucination (e.g., citation, self-check).
Propose metrics (e.g., retrieval recall, answer faithfulness) and a feedback loop for continuous improvement, including A/B testing and user feedback.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through fixed-size chunks with overlap versus semantic boundary splitting.
Start by outlining a modular ingestion pipeline with source-specific adapters that normalize content into a unified format, then discuss chunking as a trade-off between retrieval granularity and context preservation. Emphasize that chunking should be content-aware and evaluated empirically for the target use case.
Pro tip: Mention that chunking strategy should be driven by retrieval evaluation metrics (e.g., recall@k, answer faithfulness) rather than intuition, and that you'd A/B test strategies like semantic vs. fixed-size chunking. Also note the importance of preserving document structure (headings, tables) during ingestion to improve chunk quality.
Discuss the unique characteristics of PDFs (scanned vs. digital, tables, multi-column), web pages (boilerplate, dynamic content), and internal wikis (structured markup, permissions). Highlight the need for specialized parsers and fallback OCR.
Propose a pipeline with pluggable source adapters that extract raw text and metadata, followed by normalization (e.g., cleaning, deduplication) and enrichment (e.g., adding source, timestamp). Emphasize scalability and fault tolerance.
Explain that chunking should balance semantic coherence and size limits. For technical documents, use structure-aware chunking (e.g., by sections); for general text, consider recursive character splitting or semantic chunking with embeddings.
Discuss trade-offs: smaller chunks improve retrieval precision but may lose context; larger chunks preserve context but reduce precision. Propose evaluating with retrieval metrics and end-to-end QA performance.
Suggest logging chunk statistics (size distribution, overlap) and monitoring retrieval quality. Plan for iterative improvements based on user feedback and performance data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by framing the problem: dense vectors capture semantic similarity while BM25 excels at exact keyword matching, so combining them improves recall. Then describe a concrete pipeline: parallel retrieval, fusion (e.g., reciprocal rank fusion), and a cross-encoder reranker for precision. Finally, discuss trade-offs like latency, cost, and tuning weights.
Pro tip: Emphasize that reranking is not just a final step but a critical precision booster; mention that you'd evaluate the system end-to-end with metrics like nDCG and MRR, and be ready to discuss how you'd handle latency budgets by parallelizing retrieval and using a lightweight reranker.
Ask about the use case (e.g., legal document search), latency requirements, and data characteristics to tailor the hybrid approach.
Explain how dense vectors (e.g., from bi-encoders like SBERT) capture semantics, while BM25 captures lexical matches; note their complementary strengths.
Detail parallel retrieval from both methods, then fusion using techniques like reciprocal rank fusion or weighted sum of scores.
Describe using a cross-encoder (e.g., BERT-based) to rerank top-k results from fusion, improving precision at the cost of latency.
Cover latency vs. accuracy, cost of reranking, tuning fusion weights, and evaluation metrics like nDCG, MRR, and recall@k.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with recall@k and MRR for retrieval, then faithfulness and answer relevance for end-to-end.
Start by breaking down the RAG pipeline into retrieval and generation components, then define separate metrics for each. For retrieval, focus on ranking and relevance metrics; for generation, assess faithfulness, relevance, and correctness. Emphasize the importance of end-to-end evaluation and continuous monitoring in production.
Pro tip: In practice, retrieval quality often dominates final answer quality, so invest in robust retrieval evaluation first. Also, consider using LLM-based evaluation for generation metrics, but validate with human judgments periodically.
Clearly separate the retrieval and generation stages to evaluate each independently. This helps isolate issues and attribute performance.
Use metrics like recall@k, precision@k, MRR, and NDCG to measure how well the retriever fetches relevant documents. Consider domain-specific relevance judgments.
Evaluate answer quality using faithfulness (does the answer stick to retrieved evidence?), answer relevance, and correctness. Use both automatic metrics (e.g., BLEU, ROUGE, BERTScore) and LLM-based or human evaluations.
Measure the final answer quality in the context of the full system, e.g., through human evaluation or task-specific success metrics. This captures interactions between retrieval and generation.
Set up dashboards to track metrics over time and run A/B tests to compare system changes. Use online metrics like user engagement or task completion rates.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by framing the core tension: RAG systems improve answer quality by retrieving from a broad corpus, but that same retrieval can leak data across permission boundaries. Then walk through the operational lifecycle—ingestion, indexing, retrieval, generation, and monitoring—highlighting access control and PII risks at each stage, and close with concrete mitigations like permission-aware retrieval and PII redaction.
Pro tip: Emphasize that access control must be enforced at query time, not just at ingestion, because user permissions change and documents get reclassified. Mention that you'd log retrieval decisions (which chunks were fetched and why) to enable audits and incident response.
Identify where sensitive data enters, is stored, and is retrieved. Clarify who can access what and where permission checks must occur.
Ensure the retriever filters documents based on the user's current entitlements, not just document-level ACLs. Consider row-level security and query-time policy evaluation.
Implement PII detection at ingestion and query time, with redaction or tokenization before embedding and generation. Handle false positives and negatives gracefully.
Treat embeddings as sensitive data: encrypt at rest, isolate tenants, and prevent embedding inversion attacks. Control access to the vector database itself.
Log retrieval and generation events for audit trails. Run regular red-team exercises to test for data leakage and permission bypasses.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.