Choose Between Fine-Tuning and RAG for Client Chatbot

Q: Choose Between Fine-Tuning and RAG for Client Chatbot

This question evaluates a data scientist's competency in applied machine learning for production chatbots, examining model adaptation methods (fine-tuning variants and LoRA), retrieval and embedding optimization, and system architecture for multi-domain knowledge.

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.

Question

Scenario

You are building a client-facing chatbot that must answer questions grounded in the client's proprietary documents. You must choose how to imbue the system with domain knowledge and ensure high-quality retrieval.

Tasks

Decide between fine-tuning a language model vs. Retrieval-Augmented Generation (RAG). Compare on cost, data needs, latency, maintainability, and risk.
List and compare fine-tuning approaches: full model fine-tuning, instruction tuning (SFT), LoRA/QLoRA adapters, and embedding-model fine-tuning. Explain when each is appropriate.
Explain LoRA's mechanism mathematically and the advantages it offers at inference time.
If retrieved documents show low relevance, propose concrete steps to improve retrieval quality.
The embedding model is the bottleneck. Describe how you would fine-tune it: data requirements, training objective, negatives, and evaluation.
Propose a high-level architecture for a chatbot that must answer across multiple knowledge domains (e.g., product docs, policies, tickets), including routing and evaluation.

Choose Between Fine-Tuning and RAG for Client Chatbot

Scenario

Tasks

Solution

Comments (0)

Choose Between Fine-Tuning and RAG for Client Chatbot

Overview

Scenario

Tasks

Solution

Comments (0)