prompting
FDE interview questions tagged prompting, across every topic.
3 questions · 0 unlocked for you
Concepts behind "prompting"
The curriculum that explains the ideas these questions test.
Foundational
The Context WindowThe context window is the fixed number of tokens a language model can attend to at once, and input and output share that same budget. Understanding it is what separates engineers who can size a prompt, control cost and latency, and decide when to reach for RAG from those who just paste everything in and hope.🧠 Foundations of LLMs & GenAI
Foundational
Prompt EngineeringPrompt engineering is the practice of shaping a model's behavior through the instructions, examples, and format constraints you give it, before reaching for retrieval or fine-tuning. FDE loops test it because it is the cheapest, fastest lever you have, and a candidate who can make a model reliable with a well-structured prompt has saved a project weeks of unnecessary infrastructure.🧠 Foundations of LLMs & GenAI
Foundational
Fine-tuning vs RAG vs PromptingPrompting, RAG, and fine-tuning are the three ways to adapt a model to your problem, and choosing among them is the decision FDE interviewers probe most. The framework: prompt first, add RAG when the model needs facts it lacks or must cite, and fine-tune to change behavior or format rather than knowledge. They compose; they are not rivals.🧠 Foundations of LLMs & GenAI
Core
Chain-of-Thought PromptingChain-of-thought prompting tells a model to write out intermediate reasoning steps before its final answer, which raises accuracy on multi-step math, logic, and planning by spending more compute per problem. FDE loops probe it because knowing when it helps, when it just burns tokens, and why the stated reasoning is not always the true cause of the answer separates people who have shipped from people who have read a blog post.🧠 Foundations of LLMs & GenAISign in
Advanced
Context Failure ModesLong context does not fail by overflowing. It fails by degrading, and it does so in four distinct ways that look identical from the outside: poisoning, overload, brevity bias and collapse. Naming which one you are seeing is what turns a vague complaint that the model got worse into a fix.🤖 Retrieval & Agents🔒 Premium