Design a Memo Q&A Agent for a Large Law Firm
Design a Memo Q&A Agent for a Large Law Firm
Design an AI system that allows attorneys at a large law firm to ask natural-language questions and receive answers grounded in the firm's published legal memos. The system should ingest memos by crawling law-firm websites, chunk them, and index them for retrieval. A Large Language Model (LLM) will then compose answers based on the most relevant retrieved passages, including citations back to the source memos.
This is an open-ended design discussion focusing on the practical aspects of building a production-ready LLM-grounded retrieval system, not just the Retrieval-Augmented Generation (RAG) mechanics. The evaluation should cover ingestion, retrieval quality, answer grounding/faithfulness, and evaluation.
Constraints & Assumptions:
- Corpus Size: Approximately 10^5 memos (tens of millions of tokens), with weekly updates.
- Memo Length: Each memo is typically 5-50 pages, covering specific legal topics.
- Users: A few thousand attorneys.
- Query Volume: Modest (thousands of queries/day), with bursts during business hours. The system prioritizes quality over throughput.
- Latency Target: Interactive; first token within a couple of seconds, full answer within ~10 seconds.
- Correctness Bar: Very high. Incorrect or unsupported legal statements are unacceptable. All claims must be traceable to cited memo passages.
- LLM/Embedding: Assume access to commercial LLM and embedding model APIs; no foundation model training required.
Key Areas to Cover:
- Ingestion: Crawling, parsing heterogeneous formats (HTML, PDF), preserving structure, chunking strategies (fixed-size vs. structure-aware), and metadata attachment.
- Retrieval & Generation: Query path, embedding/index choice, hybrid retrieval (lexical + vector), reranking, context window management, prompt engineering for grounding and citation, and handling cases with no relevant retrieval.
- Evaluation & Monitoring: Offline evaluation (retrieval vs. answer quality), building labeled datasets, automatic hallucination detection, and production monitoring (citation coverage, refusal rate, latency, user feedback, drift).
AI interview prep
Core dilemma
Balancing the need for comprehensive legal knowledge retrieval and synthesis with the absolute requirement for factual accuracy and citation, preventing hallucinations in a high-stakes domain.
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 Memo Q&A Agent for a Large Law Firm
Design a Memo Q&A Agent for a Large Law Firm
Design an AI system that allows attorneys at a large law firm to ask natural-language questions and receive answers grounded in the firm's published legal memos. The system should ingest memos by crawling law-firm websites, chunk them, and index them for retrieval. A Large Language Model (LLM) will then compose answers based on the most relevant retrieved passages, including citations back to the source memos.
This is an open-ended design discussion focusing on the practical aspects of building a production-ready LLM-grounded retrieval system, not just the Retrieval-Augmented Generation (RAG) mechanics. The evaluation should cover ingestion, retrieval quality, answer grounding/faithfulness, and evaluation.
Constraints & Assumptions:
- Corpus Size: Approximately 10^5 memos (tens of millions of tokens), with weekly updates.
- Memo Length: Each memo is typically 5-50 pages, covering specific legal topics.
- Users: A few thousand attorneys.
- Query Volume: Modest (thousands of queries/day), with bursts during business hours. The system prioritizes quality over throughput.
- Latency Target: Interactive; first token within a couple of seconds, full answer within ~10 seconds.
- Correctness Bar: Very high. Incorrect or unsupported legal statements are unacceptable. All claims must be traceable to cited memo passages.
- LLM/Embedding: Assume access to commercial LLM and embedding model APIs; no foundation model training required.
Key Areas to Cover:
- Ingestion: Crawling, parsing heterogeneous formats (HTML, PDF), preserving structure, chunking strategies (fixed-size vs. structure-aware), and metadata attachment.
- Retrieval & Generation: Query path, embedding/index choice, hybrid retrieval (lexical + vector), reranking, context window management, prompt engineering for grounding and citation, and handling cases with no relevant retrieval.
- Evaluation & Monitoring: Offline evaluation (retrieval vs. answer quality), building labeled datasets, automatic hallucination detection, and production monitoring (citation coverage, refusal rate, latency, user feedback, drift).