Gen AI Core Concepts
212 Concepts to Master Gen AI Engineering
Comprehensive collection covering LLMs, RAG, prompt engineering, vector databases, fine-tuning, Python for AI, MLOps, agents, safety, and more.
Showing 212 concepts
Explain the Transformer architecture. What are attention mechanisms and why are they important?
gen-aitransformersattention
What's the difference between a Large Language Model (LLM) and other ML models?
gen-aillm
Explain these LLM concepts: Tokens, Context window, Temperature & Top-p sampling, Beam search.
gen-aillmtokens
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What's the difference between encoder-only, decoder-only, and encoder-decoder models?
gen-aitransformersllm
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What's the difference between fine-tuning and prompt engineering?
gen-aifine-tuningprompt-engineering
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How would you evaluate RAG system performance?
gen-airagevaluation
Explain instruction tuning. Why is it important for chat models?
gen-aifine-tuningllm
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How would you handle imbalanced training data for a classification task?
gen-aitrainingmlops
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Explain chain-of-thought (CoT) prompting. Why does it work?
gen-aiprompt-engineering
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Design a prompt for sentiment analysis. What could go wrong?
gen-aiprompt-engineering
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Explain decorators in Python. How would you use them in an LLM application?
gen-aipython
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What are context managers? How would you use them for LLM resource management?
gen-aipython
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