I started with fixed-size chunking and they immediately pushed back.
Start by clarifying the requirements and constraints of the enterprise LLM assistant, then propose a modular pipeline that handles each document type with appropriate parsing and chunking strategies. Emphasize trade-offs between chunk size, overlap, and retrieval performance, and discuss how to evaluate and iterate on the pipeline.
Pro tip: Focus on the importance of metadata and structure preservation during chunking, as it significantly impacts retrieval accuracy and LLM performance. Also, mention the need for a feedback loop to continuously improve chunking based on downstream task performance.
Ask about the scale, latency requirements, document types, and retrieval needs. Understand the downstream tasks (e.g., question answering, summarization) and how chunks will be used.
For each source (wikis, tickets, code, PDFs), outline parsing strategies: HTML/Markdown parsing for wikis, structured field extraction for tickets, AST parsing for code, and text extraction with layout awareness for PDFs.
Propose chunking methods tailored to each content type: semantic chunking for prose, function/class-based for code, and logical sectioning for tickets. Discuss chunk size, overlap, and metadata attachment.
Discuss trade-offs between chunk size, retrieval accuracy, and computational cost. Propose metrics (e.g., retrieval precision/recall, answer quality) and an iterative evaluation framework.
Consider how the pipeline scales with data volume, handles updates, and integrates with vector databases. Mention monitoring and feedback loops for continuous improvement.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about extracting things like author, team, classification level, and document type at ingest time and storing them alongside the vector index.
Start by outlining the end-to-end pipeline: metadata extraction during ingestion, storage in a structured index, and enforcement at query time via pre-filtering. Emphasize the trade-offs between accuracy, latency, and security, and how you would design for scalability and robustness.
Pro tip: Highlight the importance of pre-filtering over post-filtering to avoid leaking sensitive information and to maintain performance. Also, mention the need for a fallback mechanism to handle metadata extraction failures without compromising security.
Describe how you extract metadata from documents (e.g., using NLP, regex, or ML models) and what types of metadata (e.g., author, department, sensitivity labels) are relevant for access control.
Explain how you store and index metadata alongside document embeddings or content, ensuring it is queryable and scalable (e.g., using a vector database with metadata filtering support).
Discuss how you define and manage access control policies, mapping user attributes or roles to metadata-based filters (e.g., RBAC or ABAC).
Detail how you enforce access control at query time, such as by injecting metadata filters into the query or using a pre-filtering approach in the vector search to ensure only authorized documents are considered.
Analyze trade-offs between pre-filtering and post-filtering, latency vs. security, and discuss how to handle missing or incorrect metadata to prevent unauthorized access.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Multi-tenancy isolation was the part I was least prepared for.
Start by clarifying requirements and constraints, then propose a layered architecture that separates tenant data at the index level. Explain how you would combine dense and sparse retrieval using rank fusion, and discuss trade-offs around isolation, performance, and cost.
Pro tip: Emphasize that tenant isolation should be enforced at the data layer, not just the application layer, to prevent data leakage. Also, mention that using per-tenant indexes can simplify isolation but may increase operational overhead, so consider a hybrid approach with shared indexes and tenant-specific filters for scalability.
Ask about scale (number of tenants, data volume), latency requirements, and isolation guarantees (e.g., compliance). This shapes the design.
Choose between per-tenant indexes, shared index with tenant ID filtering, or a hybrid. Discuss trade-offs: isolation vs. resource efficiency.
For each tenant, run dense vector search (e.g., using FAISS or HNSW) and sparse BM25 (e.g., using Elasticsearch or Lucene) in parallel. Combine results using rank fusion (e.g., Reciprocal Rank Fusion).
Ensure queries are scoped to the tenant: either by querying tenant-specific indexes or by adding a tenant filter to shared indexes. Handle cross-tenant leakage risks.
Cover trade-offs: per-tenant indexes offer strong isolation but higher cost; shared indexes are efficient but require careful filtering. Mention caching, sharding, and monitoring.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Structure your answer as a clear three-stage pipeline: candidate generation, reranking, and answer generation. For each stage, explain the goal, methods, and trade-offs, and tie it back to how OpenAI might apply it (e.g., for retrieval-augmented generation in ChatGPT). Emphasize scalability, latency, and quality considerations.
Pro tip: Show awareness of the end-to-end latency budget and how each stage contributes to it; mention that you'd instrument each stage with metrics (e.g., recall@k, nDCG) to enable iterative improvements.
Briefly state assumptions about scale, latency, and quality requirements. This sets the context for your design choices.
Describe how you'd retrieve a broad set of relevant candidates efficiently, using methods like dense retrieval (bi-encoders), sparse retrieval (BM25), or hybrid approaches. Mention indexing and approximate nearest neighbor search.
Explain how you'd refine the candidate set with a more expensive model, such as a cross-encoder or a listwise reranker, to improve precision. Discuss trade-offs between quality and latency.
Describe how the top-ranked passages are used to generate a final answer, e.g., via a large language model with retrieval-augmented generation. Mention techniques to ensure faithfulness and attribution.
Outline how you'd evaluate each stage (e.g., recall for retrieval, nDCG for ranking, human eval for generation) and iterate to improve the pipeline.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pointwise scores each document independently so it can't capture any relationship between documents in the candidate set.
Start by defining the three reranking paradigms in terms of their learning objectives and input-output structures. Then compare them across key dimensions such as training signal, computational complexity, and effectiveness. Finally, focus on pointwise reranking's specific drawbacks, including its inability to model relative preferences and its sensitivity to score calibration.
Pro tip: Emphasize that pointwise methods are often used in industry due to their simplicity and scalability, but they can be suboptimal for ranking tasks where relative order matters. Mention that pairwise and listwise approaches directly optimize ranking metrics, which is crucial for applications like search and recommendation.
Briefly explain pointwise, pairwise, and listwise reranking: pointwise predicts a score for each item independently; pairwise predicts the relative order between pairs; listwise predicts the optimal ordering of a list of items.
Discuss how each approach formulates the learning problem: pointwise as regression/classification, pairwise as binary classification of preference pairs, and listwise as optimizing a list-level loss (e.g., ListNet, ListMLE).
Compare them in terms of training complexity, data requirements, and alignment with ranking metrics. Pointwise is simplest but may not optimize ranking directly; pairwise and listwise are more complex but better at capturing relative order.
Enumerate specific drawbacks: ignores inter-item dependencies, cannot model relative preferences, sensitive to score calibration, and may not optimize ranking metrics like NDCG. Also, it treats each item independently, which can lead to suboptimal ordering.
Summarize when pointwise might still be useful (e.g., large-scale retrieval) and why pairwise/listwise are preferred for reranking tasks where precision at top ranks is critical.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went through recall and precision at k for retrieval, then RAGAS-style metrics for the answer side including faithfulness and answer relevance.
Start by clarifying the system's components (retrieval and generation) and the user's goal, then propose a layered metrics framework that separates retrieval quality from end-to-end answer quality. For each layer, specify concrete metrics, how to measure them (offline and online), and how to balance trade-offs like relevance vs. latency.
Pro tip: Emphasize that metrics should be tied to user value and business goals, and mention the importance of guardrail metrics to catch regressions in safety or latency. Also, discuss how to combine automated metrics with human evaluation for nuanced aspects like factuality and helpfulness.
Ask clarifying questions about the system architecture (e.g., retrieval-augmented generation), the user task, and success criteria. This ensures metrics align with the actual use case.
Propose metrics like recall@k, precision@k, MRR, NDCG to evaluate the retriever's ability to fetch relevant documents. Mention both offline evaluation on labeled data and online proxy metrics like click-through rate on retrieved items.
Suggest metrics such as answer relevance, factuality, fluency, and helpfulness, measured via human ratings or automated metrics (e.g., BLEU, ROUGE, BERTScore, or LLM-based evaluation). Include task-specific metrics like exact match for QA.
Explain how to measure impact in production via A/B tests: user engagement (e.g., session length, task completion), satisfaction (e.g., thumbs up/down), and business KPIs (e.g., retention, conversion). Highlight the need for guardrail metrics (latency, safety).
Acknowledge trade-offs between retrieval and generation quality, and between automated and human evaluation. Describe how to prioritize metrics and iterate based on user feedback and business impact.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.