🧠 AI production systems ⚡

prompting · chain of thought · fine‑tuning — knowledge check with recommendations

① 📝 prompting

1 What is the primary purpose of prompting?

Modify weights Guide behavior at runtime Change architecture Increase dataset size

✅ correct: Guide behavior at runtime

2 Structured prompting includes:

Retraining Output formatting constraints Weight adjustment Tokenization change

✅ correct: Output formatting constraints

3 Prompting is best when:

Flexibility is needed Permanent specialization required Architecture redesign needed Hardware optimization needed

✅ correct: Flexibility is needed

4 Production prompts usually contain:

Role and structure instructions Training datasets Learning rate configs GPU settings

✅ correct: Role and structure instructions

5 Prompting improves consistency by:

Controlling structure Increasing randomness Changing model size Altering embeddings

✅ correct: Controlling structure

② 🔗 chain of thought

6 Chain of Thought improves:

Multi-step reasoning Model compression Hardware efficiency Tokenization

✅ correct: Multi-step reasoning

7 Best reasoning prompt:

Explain step-by-step Give answer only One word Random output

✅ correct: Explain step-by-step

8 In production, CoT improves:

Reliability Randomness GPU speed Model size

✅ correct: Reliability

9 CoT is implemented as:

Reasoning scaffold Weight reset Data duplication Hardware upgrade

✅ correct: Reasoning scaffold

10 CoT is most useful for:

Complex analysis Simple recall Single-word tasks Compression

✅ correct: Complex analysis

③ ⚙️ fine-tuning

11 Fine-tuning modifies:

Model weights Prompt only Browser settings GPU drivers

✅ correct: Model weights

12 Best use case:

Domain specialization One-time task Casual chat Formatting only

✅ correct: Domain specialization

13 Fine-tuning reduces need for:

Long repeated prompts Internet Tokens UI

✅ correct: Long repeated prompts

14 Production justification:

Consistent risk labeling Brainstorming Translation Simple math

✅ correct: Consistent risk labeling

15 Fine-tuning occurs at:

Training time Inference only UI layer Browser level

✅ correct: Training time