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

What's the difference between encoder-only, decoder-only, and encoder-decoder models?

gen-aitransformersllm

Explain quantization in LLMs. Why is it important?

gen-aiquantizationllm

What's the difference between fine-tuning and prompt engineering?

gen-aifine-tuningprompt-engineering

Explain RAG. When would you use it over fine-tuning?

gen-airagfine-tuning

Walk me through a RAG pipeline architecture.

gen-airagsystem-design

How would you evaluate RAG system performance?

gen-airagevaluation
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What are common failure modes in RAG systems?

gen-airag

What's the difference between LoRA and full fine-tuning?

gen-aifine-tuninglora

Explain instruction tuning. Why is it important for chat models?

gen-aifine-tuningllm

What metrics would you track during model training?

gen-aimlopstraining

How would you handle imbalanced training data for a classification task?

gen-aitrainingmlops

Explain chain-of-thought (CoT) prompting. Why does it work?

gen-aiprompt-engineering

What's few-shot vs. zero-shot prompting?

gen-aiprompt-engineering

Design a prompt for sentiment analysis. What could go wrong?

gen-aiprompt-engineering

What's prompt injection? How do you mitigate it?

gen-aiprompt-engineeringsecurity

Explain decorators in Python. How would you use them in an LLM application?

gen-aipython

What are context managers? How would you use them for LLM resource management?

gen-aipython

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