Generative AI Interview Questions

Discover how generative ai creates new content from learned data patterns, contrast it with discriminative ai, and explore models, prompts, biases, and applications in text, images, audio, and video.

Explore what large language models are, how they learn from vast text data to understand and generate human-like language, and how attention, transformers, and embeddings enable context-aware, multi-task performance.

Design clear, specific prompts to guide language models, control tone, length, and style; explore meta prompting, role playing, chain-of-thought, trees of thoughts, and prompt tuning for reliable, factual responses.

Discover the retrieval augmented generation framework, combining external data retrieval with text generation via embeddings and a vector database to deliver contextually relevant responses.

Discover how ai agents combine llms, apis, and databases to perform tasks, make decisions, and automate actions. Uncover use cases, multi-agent orchestration, and key challenges.

Fine-tune large language models by training on domain-specific data to adapt performance for targeted tasks while preserving pre-trained knowledge. Compare with pre-training and RAG, and consider LoRA and quality samples.

Explore common evaluation metrics for large language models, including bleu/rouge, perplexity, f1, and embedding-based scores, and understand when to use human evaluation for translation, summarization, and conversation.

Understand why language models hallucinate, the risks to accuracy, and how retrieval augmented generation and prompt engineering reduce falsehoods.

Learn how reinforcement learning from human feedback guides language model outputs. Explore long chain frameworks, memory updates, and model families like GPT, Gemini, and Llama.