CCDV-F practice questions: Prompt and context engineering.

A multi-turn assistant's history grows until requests fail. What is the sane fix?

• Summarize older turns and keep the most recent turns verbatim
• Raise max_tokens so the request budget covers the longer history
• Add a cache_control breakpoint so older turns stop counting toward the window
• Send only the latest user message and rely on the system prompt to carry state

Where should a long reference document sit relative to the question in a prompt?

• Before the question, clearly delimited
• In the system prompt instead of a user turn
• Split evenly across several user turns
• After the question, so the task is read first

Which technique reduces the chance that instructions inside a document are followed?

• Shorten the document so injected text has fewer tokens to work with
• Wrap documents in tags, label them as data, and keep instructions outside them
• Set temperature to 0 so the model sticks to its original instructions
• Put the document in the system prompt so it carries top-level authority

What is the benefit of putting the system prompt in the system parameter rather than the first user message?

• It removes the instructions from the request's input token count
• It lets you change instructions mid-conversation without a new request
• Standing instructions stay stable and separate, and the prefix caches well
• The model gives the system parameter absolute priority over any later user turn or tool result

How can you make an extraction prompt more reliable without changing the model?

• Raise temperature so the model handles unusual inputs flexibly
• Remove the schema so the model is not constrained by rigid fields
• Give the model a persona of an experienced data analyst
• Add examples of input and expected output, including an edge case

A prompt has grown to 300 lines with contradictory rules. What should you do?

• Add a final rule stating that whenever two rules conflict, the later one in the prompt wins
• Split it across two models, one per half of the rules
• Move it to the system parameter so the model weights it more
• Refactor it: remove conflicts, group rules, and test against a golden set

Which is a good reason to summarize conversation history rather than truncate it?

• Summaries are cached, so they do not count as input tokens
• Truncated histories are rejected by the API with a 400 invalid_request_error
• Summaries let you skip the system prompt on later turns
• Summarization keeps decisions and state that truncation would drop