Evaluate RAG System Accuracy and Cost Control Strategies

Q: Evaluate RAG System Accuracy and Cost Control Strategies

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.

Q: How do I approach Machine Learning interview questions?

Machine Learning questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master machine learning interviews.

Q: What difficulty level is this interview question?

This is a hard difficulty Machine Learning question, commonly asked during Technical Screen rounds at Amazon.

Q: What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Amazon during technical interviews.

Question

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

Cost control for knowledge graphs
- How would you control the cost of building, storing, updating, and serving a knowledge graph used by an LLM?
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.
Model comparison
- Compare RNN, LSTM, and Transformer architectures. Why are Transformers preferred for modern LLMs?
Scaled dot-product attention
- Derive the scaled dot-product attention formula and explain each term and the motivation for scaling.
RAG end-to-end workflow
- Explain the end-to-end workflow of Retrieval-Augmented Generation: ingestion, indexing, retrieval, ranking, prompting, and generation.
Reranker role in RAG
- What is a reranker model and where does it sit in the RAG stack? Discuss trade-offs.
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?
LoRA
- What is LoRA, how does it work, and why is it parameter-efficient? Where is it applied in LLMs?
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.

Evaluate RAG System Accuracy and Cost Control Strategies

Quick Overview

Technical Phone Screen: LLM Pipelines, Knowledge Graphs, and RAG

Context

Questions

Solution

Comments (0)