Prompt engineering
50 practice items · needed by 4 roles
Easy 21Medium 16Hard 13
Practice questions
- Purpose of a system prompteasy
- What few-shot prompting iseasy
- Asking the model to reason firsteasy
- The required keyword in JSON Schemaeasy
- Parsing a JSON reply in Pythoneasy
- Getting reliable JSON outputmedium
- What schema-constrained output guaranteesmedium
- Shipping a prompt change safelyhard
- Field order in an output schemahard
- Improving a vague prompteasy
- Few-shot promptingeasy
- Separating instructions from dataeasy
- Giving the model a roleeasy
- Getting a consistent formateasy
- Zero-shot promptingeasy
- The model forgot the last messageeasy
- Using XML-style tagseasy
- Prefilling the responseeasy
- Chain-of-thought promptingeasy
- Keeping track of prompt changeseasy
- Message roles in chat APIseasy
- Same text, more tokenseasy
- Asterisks in text messageseasy
- Examples that bias the labelmedium
- When to ask for reasoningmedium
- Negative instructionsmedium
- Where to put the questionmedium
- Where to put fixed rulesmedium
- Settings for a ticket classifiermedium
- JSON that sometimes breaksmedium
- Handling invalid model outputmedium
- Sampling several answersmedium
- Debugging a contradictory promptmedium
- Every reply sounds like example 1medium
- Parsing an answer after reasoningmedium
- Picking examples per inputmedium
- Answers too basic for expertsmedium
- Prompting a reasoning modelhard
- Adding 30 more exampleshard
- Invented values in extractionhard
- Agent picks the wrong toolhard
- Prompt cache hit rate near zerohard
- Where is the RAG bug?hard
- Strict JSON hurt accuracyhard
- Forced into the wrong labelhard
- Tiny wording, big swinghard
- Reasoning model bill surprisehard
- The model agrees with everythinghard
- Why validate a JSON schemaeasy
- Advance received from a customereasy