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Core
Prompting vs RAG vs Fine-Tuning
Given an LLM use case, the senior move is matching the technique to what is missing rather than defaulting to one. Need external or changing knowledge? RAG. Need a specific behavior, format, or skill? Fine-tuning. Need to take actions or use live systems? Tools/agents. Just need better instructions? Prompting. They combine, and you escalate from cheapest (prompting) to most involved (fine-tuning). Applied-AI interviews probe it because choosing wrong wastes months, fine-tuning to inject changing facts is the classic mistake.
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PRACTICE THIS IN REAL QUESTIONS
LLM & GenAI FundamentalsGiven a new LLM use case, how do you decide between prompting, RAG, fine-tuning, and tools/agents?→LLM & GenAI FundamentalsWhen do you choose prompting vs RAG vs fine-tuning for a customer problem?→System Design for AI in ProductionDesign a text-to-SQL feature: let users ask questions in natural language over a real database.→LLM & GenAI FundamentalsWhat is RAFT (Retrieval-Augmented Fine-Tuning), and how does it combine RAG and fine-tuning?→LLM & GenAI FundamentalsYour LLM coding assistant keeps suggesting deprecated APIs. How do you fix stale knowledge?→RAG & Agent System DesignDesign a production RAG system over 10M documents serving ~1,000 QPS at sub-second latency.→
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