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AIF-C01 Practice Question 13: Context Engineering

AWS Certified AI Practitioner (AIF-C01) · Test 2 · question 3 of 10

Fundamentals of GenAImedium

A support team's email-summarization prototype uses a single, carefully reworded prompt. The summaries are generic: they miss product names, ignore the customer's subscription tier, and don't mention earlier tickets. Several more rounds of rewording the instruction haven't helped. What should the team do next?

Select one answer
Show answer and explanation

Answer:

  • D. Apply context engineering, assembling each request from relevant customer profile data, ticket history, and retrieved product documentation

The model can't mention a subscription tier or earlier tickets that it was never shown. The quality of the output is limited by the quality of the input. Context engineering is the practice of designing systems that dynamically assemble the right information for each request. Prompt engineering focuses on *what to ask*; context engineering focuses on *what to show*. A well-built context can combine the system instructions, the user's input, profile data, memory of past interactions, tool results, and passages retrieved from a knowledge base (option D).

- A is wrong because temperature changes how random the wording is. More randomness doesn't supply facts the model doesn't have. - B is wrong because the team has already shown that rewording has hit its limit. No wording lets the model infer a specific customer's history. - C is wrong because pre-training is by far the most expensive option, and per-customer details such as the latest tickets change daily. They belong in the context at request time, not in the model's weights.

Reference: Understanding context engineering for generative AI

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