Start by clarifying requirements (corpus size, latency, accuracy, freshness) and then propose a modular RAG pipeline: ingestion, chunking, embedding, hybrid retrieval, and generation with long-context handling. Emphasize trade-offs at each stage, especially chunking strategies, retrieval methods, and how to handle queries/documents exceeding context windows.
Pro tip: Show awareness of evaluation and iteration: propose a golden dataset and metrics (e.g., recall@k, faithfulness) to measure retrieval and generation quality, and discuss how to monitor and improve the system over time.
Ask about corpus size, document types, query types, latency/throughput needs, accuracy targets, and privacy/security requirements to scope the design.
Outline document parsing (PDF, HTML, logs), chunking strategies (semantic, recursive), embedding model selection, and vector database choice with hybrid search support.
Combine dense and sparse retrieval (e.g., embeddings + BM25), use re-ranking (cross-encoder) to improve precision, and handle long queries via query expansion or decomposition.
Use retrieved chunks to form a prompt within the model's context window; for long documents, employ hierarchical summarization or iterative refinement; for long queries, use query-focused summarization.
Define offline and online metrics, build a golden dataset, and set up monitoring for retrieval quality, answer faithfulness, and latency to continuously improve the system.
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I talked about chunking with overlap and using natural boundaries like section headers.
Start by clarifying the goal: is the task retrieval, summarization, or question answering? Then describe a hierarchical or retrieval-based approach that chunks the document, embeds chunks, and uses a model to process relevant parts, possibly with iterative refinement. Emphasize trade-offs between accuracy, latency, and cost, and mention evaluation to ensure the approach works.
Pro tip: Show awareness that context window limits are not just about size but about attention dilution and cost; propose a hybrid approach that combines retrieval with summarization to balance performance and efficiency. Also, mention that you would benchmark against a baseline like truncation to quantify improvements.
Ask what the end goal is (e.g., extract specific info, summarize, answer questions) and note constraints like latency, cost, and accuracy requirements.
Split the document into overlapping chunks, embed them, and use a retriever to fetch only the most relevant chunks for the query or task.
Use a model to process retrieved chunks, then aggregate or refine results (e.g., map-reduce summarization, iterative refinement) to produce a final answer.
Measure performance against a baseline (e.g., truncation) and iterate on chunk size, retrieval method, and aggregation to optimize trade-offs.
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Start by clarifying the user's goal and the query's structure, then propose a multi-stage pipeline that preserves intent through hierarchical summarization and retrieval-augmented generation. Emphasize evaluation and iterative refinement to ensure the final response aligns with the original ask.
Pro tip: Anchor your answer in concrete ML system design patterns (e.g., map-reduce summarization, RAG with intent embeddings) and mention how you'd measure intent preservation (e.g., human eval, intent classification accuracy).
Ask clarifying questions to understand the user's goal, expected output format, and any latency/cost constraints. Identify key entities or sections in the long query that signal intent.
Break the long query into logical chunks (e.g., paragraphs, log entries) and create embeddings or metadata for each. This enables efficient retrieval and preserves local context.
Apply a map-reduce summarization approach: summarize each chunk, then combine summaries to form a global view. Simultaneously, extract and track the user's intent using a classifier or LLM prompt.
Use the global summary and intent representation to retrieve relevant chunks and generate a response. Condition the generation on the original intent to avoid drift.
Measure intent preservation via human evaluation or automated metrics (e.g., intent classification accuracy, ROUGE). Iterate on the pipeline based on feedback.
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Start by defining what a 'missed answer' means in your RAG system—likely a retrieval failure where the correct chunk isn't in the top-k results. Then, propose a systematic evaluation: create a labeled set of queries with known relevant chunks, measure retrieval recall and answer accuracy, and compare against alternative chunking strategies to isolate the impact.
Pro tip: Don't just look at aggregate metrics—slice by query type (e.g., multi-hop, factoid, long-context) to see if chunking hurts specific categories. Also, inspect the actual retrieved chunks for failed queries to diagnose whether the issue is chunk boundaries, size, or overlap.
Clarify what constitutes a missed answer: retrieval miss (correct chunk not retrieved), context miss (chunk retrieved but answer not extractable), or generation miss. Choose metrics like recall@k, answer accuracy, and chunk-level precision/recall.
Curate a diverse set of queries with ground-truth relevant chunks and answers. Ensure coverage of different query types and document structures to detect chunking-specific weaknesses.
Compare your current chunking strategy against alternatives (e.g., fixed-size vs. semantic, different sizes/overlaps) while keeping the retriever and generator constant. Measure the chosen metrics and perform statistical significance testing.
For queries where answers are missed, inspect the retrieved chunks and the original document. Determine if the answer spans chunk boundaries, is split across chunks, or if the chunk is too large/small to be retrieved effectively.
Based on findings, adjust chunking parameters or switch strategies, then re-evaluate on the same set to confirm improvement. Monitor for regressions on other query types.
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This is where multi-hop retrieval comes in and I knew the concept but fumbled the explanation.
Frame the problem as multi-hop retrieval and synthesis, then walk through a concrete system design that combines retrieval, reasoning, and generation. Emphasize trade-offs between accuracy, latency, and cost, and how you would evaluate such a system.
Pro tip: Show that you understand the difference between retrieval and synthesis: retrieval finds relevant documents, but synthesis requires reasoning over them. Mention that you would use a combination of dense retrieval, re-ranking, and a large language model with chain-of-thought or iterative reasoning to aggregate information.
Ask clarifying questions to understand the scale, document types, and what 'synthesizing' means (e.g., summarization, comparison, inference).
Propose a multi-stage retrieval system: initial dense retrieval to get candidate documents, then re-ranking with a cross-encoder to improve precision.
Use a large language model to iteratively read and reason over retrieved documents, possibly with chain-of-thought or a graph-based approach to connect information.
Discuss trade-offs between accuracy and latency (e.g., number of retrieval rounds), and between cost and performance (e.g., model size).
Describe evaluation metrics (e.g., answer correctness, faithfulness) and how you would set up an offline evaluation pipeline with human feedback.
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Permission filtering at retrieval time, metadata tags on chunks, cache keys scoped to user or team.
Start by framing the core challenge: retrieval and caching must respect access control boundaries to prevent data leakage. Then describe a layered architecture that enforces permissions at query time and cache key design, and discuss trade-offs between security, latency, and cost.
Pro tip: Emphasize that permission checks must happen at the data layer, not just the application layer, and that cache keys should incorporate user/team identifiers and permission scopes to avoid cross-contamination. Also mention the importance of auditing and monitoring for permission leaks.
Ask about the scale, latency requirements, and the granularity of permissions (e.g., per-user, per-team, per-document). Understand the sensitivity of data and compliance needs.
Ensure that document retrieval queries include permission filters at the database or search index level. Use row-level security or document-level ACLs to enforce access.
Design cache keys that include user/team IDs and permission scopes. Consider using separate caches per user/team or a shared cache with strict key isolation. Invalidate cache on permission changes.
Balance security with performance: e.g., caching permission checks, using short TTLs, or employing a permission service. Mention potential latency and cost implications.
Describe how to log access and detect anomalies. Implement regular audits to ensure no permission leaks and to comply with regulations.
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Contrast the two tasks by highlighting the shift from retrieval-based, query-focused answering to holistic, long-context summarization. Discuss how this changes the model architecture, training objective, and evaluation metrics, emphasizing trade-offs between context length, computational efficiency, and summary quality.
Pro tip: Emphasize that summarization requires capturing global coherence and salience across the entire document, not just local relevance to a query. Mention techniques like hierarchical attention or memory mechanisms to handle long contexts efficiently.
Explain that search queries over a corpus involve retrieval and localized answer generation, while summarizing a 200-page document requires global understanding and compression of information.
Consider using hierarchical models (e.g., segment-level encoding then document-level fusion) or sparse attention to handle long inputs, as standard transformers may exceed memory limits.
Shift from retrieval-augmented or question-answering losses to sequence-to-sequence summarization losses (e.g., cross-entropy on summary tokens) with techniques like teacher forcing or reinforcement learning for coherence.
Discuss memory and latency trade-offs: use gradient checkpointing, mixed precision, or model parallelism; consider retrieval-based summarization to select salient passages first.
Move from precision/recall of retrieved answers to summarization metrics like ROUGE, BERTScore, or human evaluation of coherence, coverage, and conciseness.
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