Homework 3: Orchestration

Deadline: 6 July 2026 (Mon), 23:00

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Questions

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1. After trying the same prompt in ChatGPT vs Kestra AI Copilot, what is the primary reason AI Copilot generates better Kestra flows? (1 point)

AI Copilot uses a more powerful model

AI Copilot has access to current Kestra plugin documentation

AI Copilot uses more tokens

AI Copilot has internet access

2. The non-RAG response about Kestra 1.1 features is best described as: (1 point)

Accurate and specific, matching the actual release notes

Vague, generic, or fabricated — the model guesses from training data

Empty — the model refuses to answer without context

Identical to the RAG version

3. What is the approximate output token count for multilingual_agent when running with summary_length = short? (1 point)

5-15 tokens

60-100 tokens

200-400 tokens

500+ tokens

4. With summary_length = long, roughly how many times more output tokens does multilingual_agent use compared to the short summary? (1 point)

About the same (within 20%)

2-5x more

10-20x more

50x more

5. After changing english_brevity to ask for 3 sentences instead of 1, how does the output token count compare to the original 1-sentence version? (1 point)

About the same (within 20%)

2-4x more

5-10x more

10x+ more

6. For production workflows requiring deterministic, repeatable results with strict compliance requirements, which approach is most appropriate? (1 point)

Always use AI agents for maximum flexibility and adaptation

Use traditional task-based workflows for predictability and auditability

Use only RAG without agents for better performance

Use web search tools exclusively to ensure current data