Start by clarifying requirements and constraints, then walk through the system architecture end-to-end: ingestion, retrieval, generation, and evaluation. Emphasize trade-offs at each stage, especially around accuracy, latency, and cost, and how you would measure and iterate on quality.
Pro tip: Attorneys care most about trust and verifiability, so prioritize citation-backed answers and a human-in-the-loop review flow over raw model sophistication. Also, mention that legal language is nuanced and domain-specific, so fine-tuning or domain adaptation of embeddings and LLMs is often necessary.
Ask about corpus size, document types, update frequency, latency requirements, and compliance needs (e.g., data privacy, audit trails). This shapes the entire design.
Outline how to parse, chunk, and index legal memos, including handling of citations, metadata, and versioning. Consider OCR for scanned documents and incremental updates.
Propose a hybrid retrieval approach combining keyword search (e.g., BM25) and dense vector search, with re-ranking. Discuss embedding models, index types (e.g., HNSW), and filtering by metadata.
Describe how to generate grounded answers using retrieved passages, with citations. Cover prompt engineering, LLM selection, and techniques to reduce hallucination (e.g., constrained decoding, self-consistency).
Define offline and online metrics (e.g., retrieval recall, answer faithfulness, citation accuracy) and a human-in-the-loop feedback loop. Discuss A/B testing and continuous improvement.
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Acknowledge the conflict as a signal of legal ambiguity rather than a system failure, and design the agent to surface both memos with clear provenance instead of silently picking one. Then propose a user-facing mechanism that highlights the contradiction, explains the implications, and offers paths to resolution.
Pro tip: Frame the conflict as valuable information that prevents overconfident wrong answers, and suggest logging these conflicts to improve the corpus over time. This shows you think about the system's long-term health, not just the immediate query.
Use retrieval and contradiction detection to identify that two memos from different firms directly oppose each other on the same legal question. Classify the conflict type (e.g., jurisdictional, temporal, or interpretive) to inform the response.
The agent should not arbitrarily choose one memo. Instead, it should present both positions neutrally, clearly attributing each to its source firm, and avoid synthesizing a false consensus.
In the UI, display a prominent conflict alert that shows the two memos side-by-side with key excerpts, firm names, dates, and a concise explanation of the disagreement. Provide links to the full documents for context.
Suggest next steps such as consulting a senior attorney, checking for a controlling jurisdiction, or requesting an updated memo. Optionally, allow the user to flag the conflict for corpus curation.
Record the conflict in a feedback loop to improve future retrieval and contradiction detection, and to inform knowledge base maintenance.
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I talked about re-crawling and updating metadata with a 'superseded' flag, and linking newer memos to older ones they invalidate.
Start by framing the problem as a data freshness and authority management challenge in a RAG pipeline. Then walk through a layered defense: ingestion-time supersession detection, metadata-driven retrieval filtering, and runtime citation validation. Emphasize that the system must treat regulatory changes as first-class events that trigger re-indexing and invalidation of affected memos.
Pro tip: Mention that you would log every time a stale memo is retrieved but filtered out, and use that as a signal to improve supersession detection. This shows you think about observability and continuous improvement, not just a static fix.
Build a watcher that monitors regulatory sources for new publications. When a change is detected, parse it to identify which memos it supersedes (e.g., by matching citations, topics, or explicit references).
For each affected memo, update its metadata to mark it as superseded, including the superseding regulation ID and effective date. Optionally, move it to a 'historical' index or add a tombstone flag.
At query time, apply filters to exclude memos marked as superseded unless the user explicitly asks for historical context. Use metadata such as 'status: current' or 'superseded_by: null' in the retrieval query.
After the agent generates a response, run a post-hoc check that every cited memo is still current. If a stale memo is cited, either regenerate the answer or flag it for human review.
Log all instances where stale memos are retrieved or cited, and use this data to refine supersession detection rules and retrieval filters. Set up alerts for high rates of stale citations.
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Frame the problem as a precision-recall trade-off: lowering the refusal threshold increases coverage but risks hallucinations, so you need a principled way to tune it. Propose a data-driven approach using a labeled evaluation set to measure both refusal rate and hallucination rate, then optimize the threshold to maximize helpfulness while keeping hallucinations below an acceptable bound. Emphasize continuous monitoring and A/B testing to validate improvements in production.
Pro tip: Define a single north-star metric that combines helpfulness and hallucination cost (e.g., weighted F-beta score) so you can make objective threshold decisions and communicate trade-offs clearly to stakeholders.
Establish clear metrics: refusal rate (false refusals), hallucination rate (false answers), and coverage. Set a hard constraint on hallucination rate (e.g., <1%) based on business risk tolerance.
Create a diverse dataset of queries with ground-truth answers and labels for whether a partial answer is acceptable. Include edge cases where the model could partially answer without hallucinating.
Use the evaluation set to sweep refusal thresholds and plot the precision-recall curve. Select the threshold that maximizes coverage subject to the hallucination constraint, or optimize a weighted metric like F-beta.
Deploy the new threshold to a small percentage of traffic and compare against control on key metrics: user satisfaction, task completion, and hallucination reports. Use statistical significance to confirm improvement.
Continuously monitor refusal and hallucination rates in production, and retune as data distribution shifts. Implement guardrails to automatically revert if hallucination rate spikes.
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Ran out of time here so this was more of a quick back-and-forth than a real design question.
Treat the two scenarios as separate but related scaling challenges: first, address the 100x query volume by identifying bottlenecks and proposing horizontal scaling, caching, and sharding; then, for multi-turn conversations, introduce a stateful session layer with context storage and retrieval. Show how the design evolves incrementally, balancing trade-offs between consistency, latency, and cost.
Pro tip: Anchor your answer in the specific domain of legal AI (Harvey's focus): emphasize that multi-turn context must handle long documents and precise citations, so context window management and retrieval-augmented generation (RAG) are critical. Also, mention that 100x volume may require a shift from synchronous to asynchronous processing for non-interactive queries.
Ask about query types (read vs. write, latency SLAs), data size, consistency needs, and whether multi-turn conversations require exact recall or can use summarization. This shows you avoid premature optimization.
Describe the baseline architecture (e.g., monolithic API, single database) and pinpoint components that would fail under 100x load or stateful conversations, such as database connections, compute, and session storage.
Outline horizontal scaling (stateless services, load balancers), caching (Redis for frequent queries), database sharding/replication, and asynchronous processing (queues) for non-urgent tasks. Mention auto-scaling and CDN for static assets.
Introduce a session service that stores conversation history and context, using a fast datastore (e.g., Redis) for active sessions and a persistent store (e.g., DynamoDB) for long-term. Use RAG to fetch relevant past turns or documents, and manage context window limits with summarization or vector search.
Compare consistency vs. availability (CAP), cost implications of caching and storage, and latency impact of context retrieval. Emphasize observability (metrics, tracing) to detect bottlenecks and iterate.
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