LLM Interview Questions | Top 30 LLM Interview Questions and Answers (2026) | Edureka

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In this video, we cover the most important LLM (Large Language Model) interview questions and answers in a clear, simple, and practical way. Before jumping into the questions, we first understand how Large Language Models are used in the real world, including how they power modern search engines, conversational AI systems, and enterprise applications. You’ll learn key concepts such as RAG (Retrieval-Augmented Generation), fine-tuning vs LoRA vs PEFT, temperature, top-k and top-p sampling, chain-of-thought prompting, and model quantization. This video is perfect for AI engineers, machine learning professionals, prompt engineers, and beginners entering the Generative AI space who want strong conceptual clarity and confident interview preparation.

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Prompt Engineering Course Overview and Key Features
This LLM Prompt Engineering Certification Course guides you through basic to advanced generative AI techniques, including prompt engineering, retrieval-augmented generation (RAG), and vector databases. You will gain practical skills to design and deploy cutting-edge GenAI applications using popular tools such as Python, PyTorch, LangChain, and OpenAI. The course also focuses on mastering LLM APIs, application architecture, and production-ready deployment strategies, equipping you to build real-world AI solutions.

Who should take this LLM prompt engineering certification course?
If you are an AI enthusiast, developer, or professional working with natural language processing, AI product development, or automation and you want to get better at designing effective prompts to make your AI applications smarter, the Prompt Engineering with LLM Course is a great fit for you.

What are the prerequisites for this course?
To succeed in this course, you should have a basic understanding of Python, machine learning, deep learning, natural language processing, generative AI, and prompt engineering concepts. However, you will receive self-learning refresher materials on generative AI and prompt engineering before the live classes begin.

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