NCP-GENL Prompt Engineering Practice Question
A developer is using an NVIDIA NIM for a customer support chatbot. The chatbot must handle multi-turn conversations and maintain context about the user's issue. The developer notices that after several turns, the bot starts giving generic responses and forgets earlier details. Which prompt engineering approach is most effective to maintain context?
⚠ Common exam trap
The trap here is assuming that summarizing the conversation will preserve all necessary details, when in fact summarization can omit specifics that are crucial for support interactions.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Include the entire conversation history in the prompt for each turn, ensuring the model has access to all previous messages.
Including the full conversation history in the prompt is the most direct way to maintain context across turns. The model can attend to all previous messages, ensuring it remembers details from earlier in the conversation. While summarization can reduce token usage, it risks losing critical information. Temperature and system prompts do not address context retention.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a summarization prompt at each turn to condense the conversation so far, and include only the summary in the next prompt.
Why it's wrong here
Summarization can help fit long conversations into the context window, but it may lose important details. If the summary omits a key detail from earlier, the model will not have access to it. For a support chatbot where specific details matter, summarization risks information loss. Including the full history is more reliable for maintaining context, though it may require truncation if too long.
- ✗
Add a system prompt that instructs the model to always ask the user to repeat their issue if it is unsure.
Why it's wrong here
This approach places the burden on the user and does not maintain context. It would lead to a poor user experience. While it might prevent incorrect answers, it does not solve the underlying problem of the model forgetting earlier details. The goal is to maintain context, not to offload it to the user. A system prompt alone cannot retain information that is not in the context.
- ✗
Use a higher temperature setting to make the model more creative in interpreting the user's issue.
Why it's wrong here
Higher temperature increases randomness and does not help maintain context. It could make the model less consistent and more likely to forget or misinterpret details. For a support chatbot that needs to remember earlier parts of the conversation, a lower temperature is preferable to ensure deterministic and focused responses. Temperature adjustments do not address context retention.
- ✓
Include the entire conversation history in the prompt for each turn, ensuring the model has access to all previous messages.
Why this is correct
Including the full conversation history in the prompt allows the model to attend to all previous turns, maintaining context. This is a standard approach for multi-turn dialogues. However, it is limited by the context window size; if the conversation exceeds the window, older messages may be truncated. Despite this, for many support conversations, it is the most straightforward and effective method to preserve context without additional infrastructure.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official NVIDIA exam blueprint
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