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Generative AI Leader Practice Question: A law firm wants to use generative AI to analyze…
A law firm wants to use generative AI to analyze contracts and extract key clauses. They need high accuracy and the ability to handle diverse contract formats. Which three steps should they take in their proof-of-concept (PoC) phase? (Choose THREE)
⚠ Common exam trap
The Generative AI Leader exam often tests the misconception that a proof-of-concept should aim for a production-ready solution immediately, leading candidates to choose options like D (pre-built API) or E (custom training) instead of focusing on iterative, low-cost validation steps like few-shot prompting and human review.
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
✓
Use few-shot prompting with examples of desired clause extraction
Few-shot prompting provides the model with specific examples of desired clause extraction, guiding it to produce accurate outputs without requiring fine-tuning. This technique is efficient for a proof-of-concept because it leverages the model's existing capabilities while adapting to the task through in-context learning, which is critical for handling diverse contract formats with high accuracy.
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 few-shot prompting with examples of desired clause extraction
Why this is correct
Few-shot examples guide the model to produce consistent, structured outputs.
- ✓
Implement a human-in-the-loop review process for extracted clauses
Why this is correct
Human review ensures accuracy and builds trust before moving to production.
- ✓
Test on a diverse set of 50-100 contracts covering common variations
Why this is correct
A representative sample helps evaluate performance across formats without overwhelming the PoC.
- ✗
Deploy a pre-built contract analysis API without customization
Why it's wrong here
Pre-built APIs may not handle the firm's specific contract language; customization is needed.
- ✗
Train a custom model from scratch using all historical contracts
Why it's wrong here
Training from scratch is expensive and time-consuming; a PoC should use fine-tuning or prompt engineering first.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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