hardMultiple Choice
Generative AI Leader Practice Question: A legal firm wants to automate contract analysis…
A legal firm wants to automate contract analysis to extract key clauses and risks. They have 10,000 contracts in PDF format. The solution must handle varying layouts and be cost-effective. Which approach is BEST?
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 Document AI to convert PDFs to structured text, then use Vertex AI with a prompt that specifies clauses to extract
Using Document AI to parse PDFs into text, then Vertex AI with a structured prompt for clause extraction combines robust document understanding with flexible GenAI. Fine-tuning on 100 contracts is insufficient for layout variation. Agent Builder is overkill. Direct Gemini API on raw PDFs loses document structure.
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 Document AI to convert PDFs to structured text, then use Vertex AI with a prompt that specifies clauses to extract
Why this is correct
Document AI handles layout parsing, and the structured text is then fed to a GenAI model with a well-designed prompt. This combination is scalable and cost-effective.
- ✗
Build a retrieval-augmented generation (RAG) system in Vertex AI Agent Builder
Why it's wrong here
RAG is for answering questions over documents, not for structured extraction of specific clauses. Agent Builder is more suited for conversational interfaces.
- ✗
Use the Gemini API directly with the raw PDF files as input
Why it's wrong here
Gemini API can accept PDFs, but without preprocessing, the model may not correctly interpret complex layouts, leading to errors. Cost may also be higher for large PDFs.
- ✗
Fine-tune a Gemini model on 100 annotated contracts and run inference on all contracts
Why it's wrong here
100 contracts are too few to handle layout variations; fine-tuning is expensive and may not generalize well to diverse document formats.
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