Evaluate RAG System Accuracy and Cost Control Strategies
Overview
This question evaluates understanding of LLM pipelines and Retrieval-Augmented Generation (RAG), including knowledge-graph cost control, retrieval and reranker roles, embedding dimensionality, attention mechanisms and architectures (RNN/LSTM/Transformer), LoRA, and end-to-end accuracy and grounding metrics within Machine Learning, natural language processing, and information retrieval. It is commonly asked to assess theoretical knowledge alongside production-minded trade-offs—scalability, cost, latency, and metric-driven evaluation—testing both conceptual understanding and practical application.
Technical Phone Screen: LLM Pipelines, Knowledge Graphs, and RAG
Context
You are designing and operating LLM-based applications that integrate a knowledge graph (KG) and Retrieval-Augmented Generation (RAG). Answer the following to demonstrate both theoretical understanding and production-minded trade-offs.
Questions
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Cost control for knowledge graphs
- How would you control the cost of building, storing, updating, and serving a knowledge graph used by an LLM?
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Measuring LLM accuracy (offline and online)
- How do you measure the accuracy and quality of LLM outputs offline and online? Include task-specific metrics (e.g., EM/F1 for QA), generation metrics (e.g., BLEU/ROUGE), and production signals.
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Model comparison
- Compare RNN, LSTM, and Transformer architectures. Why are Transformers preferred for modern LLMs?
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Scaled dot-product attention
- Derive the scaled dot-product attention formula and explain each term and the motivation for scaling.
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RAG end-to-end workflow
- Explain the end-to-end workflow of Retrieval-Augmented Generation: ingestion, indexing, retrieval, ranking, prompting, and generation.
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Reranker role in RAG
- What is a reranker model and where does it sit in the RAG stack? Discuss trade-offs.
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Embedding dimensionality and retrieval quality
- How does embedding vector dimensionality influence retrieval quality, memory, and latency? What are the trade-offs and heuristics for choosing a dimension?
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LoRA
- What is LoRA, how does it work, and why is it parameter-efficient? Where is it applied in LLMs?
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Evaluating a RAG system
- How would you evaluate the accuracy and groundedness of a RAG system end-to-end? Include retrieval, grounding, and generation metrics, as well as online evaluation.
Hint: Relate theory to production trade-offs: costs, equations, evaluation metrics (BLEU, EM, precision@k), latency and quality trade-offs.
Solution
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