Discuss large language models

Q: Discuss large language models

This question evaluates a Software Engineer's competence in recent large language model (LLM) advancements, product integration design, production deployment challenges, and approaches for managing hallucinations and bias within the Machine Learning domain.

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

LLMs: Advances, Product Integration, Production Challenges, and Risk Mitigation

Context

You are interviewing for a Software Engineer role focused on machine learning. Assume you need to assess recent large language model (LLM) capabilities and propose how to integrate them into a large-scale product with web/mobile clients, an existing knowledge base (docs, tickets, FAQs), and APIs.

Tasks

Latest Advancements: Summarize notable LLM advancements from the past 12–18 months and why they matter for production systems.
Product Applications: Propose 2–3 high-impact ways to apply LLMs in our product. For each, outline key user value, a high-level architecture, and success metrics.
Production Challenges: Identify the main challenges when deploying an LLM to production and how you would address each.
Hallucinations and Bias: Explain concrete techniques to handle hallucinations and mitigate bias in LLM outputs.

Discuss large language models

LLMs: Advances, Product Integration, Production Challenges, and Risk Mitigation

Context

Tasks

Solution

Comments (0)