GenAI Interview Questions asked in different companies

2026-03-02 · Source: Towards AI - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, long

Summary

This article compiles common interview questions for Generative AI roles, focusing on Transformer architectures, evaluation metrics, prompting techniques, and RAG system assessment. It defines encoder-only (e.g., BERT), decoder-only (e.g., GPT-3, LLaMA 3), and encoder-decoder (e.g., T5, BART) models, detailing their use cases and limitations. The content also explains key text generation evaluation metrics like BLEU, ROUGE, METEOR, and BERTScore, highlighting their strengths and weaknesses. Furthermore, it covers various prompting strategies, including Zero-Shot, Few-Shot, Chain of Thought, Role, and Iterative Prompting. Finally, the article delves into RAG system evaluation using RAGAS metrics (Answer Relevancy, Correctness, Context Recall, Precision, Faithfulness), methods for detecting and mitigating LLM hallucinations, and the role of reranking in RAG pipelines, emphasizing the advantages of cross-encoders over bi-encoders for semantic matching.

Key takeaway

For AI Engineers and Machine Learning Engineers building or deploying LLM-based applications, understanding the architectural nuances of Transformers and their appropriate use cases is critical. Your choice of model (encoder-only vs. decoder-only) directly impacts performance for tasks like classification versus generation. Additionally, mastering prompting techniques and utilizing RAGAS for comprehensive RAG system evaluation will be essential for developing robust, accurate, and reliable GenAI solutions, especially when mitigating hallucinations and optimizing retrieval quality.

Key insights

Understanding Transformer architectures, evaluation metrics, and prompting is crucial for GenAI roles.

Principles

• Encoder-only models excel at classification, decoder-only at generation.
• Evaluation metrics balance precision, recall, and semantic understanding.
• Prompting techniques significantly influence LLM output quality.

Method

RAGAS provides a structured framework for evaluating Retrieval-Augmented Generation systems by assessing retrieval and generation quality through automated metrics like Answer Relevancy, Context Recall, and Faithfulness.

In practice

• Use cross-encoders for superior RAG document reranking.
• Employ "Let's think step by step" for complex reasoning tasks.
• Lower LLM temperature to reduce hallucination rates.

Topics

• Transformer Architectures
• LLM Evaluation Metrics
• Prompt Engineering
• Retrieval-Augmented Generation
• LLM Hallucination Mitigation

Best for: AI Engineer, Machine Learning Engineer, AI Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.