Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
An AI team is building a customer support chatbot for a telecom company using a fine-tuned LLM on Vertex AI. The model performs well on common issues but fails to answer correctly for rare or novel problems, often providing plausible-sounding but incorrect solutions. The team has a large corpus of internal troubleshooting documents. They want to minimize incorrect answers while keeping latency low. Which approach should they take?
⚠ Common exam trap
Google often tests the misconception that fine-tuning or larger models alone can solve knowledge gaps, when in fact retrieval-augmented generation is the standard approach for grounding LLM outputs in up-to-date, domain-specific documents without retraining.
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
✓
Implement a retrieval-augmented generation (RAG) pipeline using Vertex AI Search to fetch relevant documents before generating answers.
Implementing a RAG pipeline with Vertex AI Search allows the chatbot to retrieve relevant troubleshooting documents from the internal corpus in real-time, grounding the LLM's responses in authoritative sources. This approach directly addresses the problem of plausible-sounding but incorrect answers for rare/novel issues without requiring retraining, and it keeps latency low by fetching only the most relevant documents before generation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch to a larger base model (e.g., Gemini Ultra) without any retrieval.
Why it's wrong here
A larger model may still hallucinate on niche topics and increases latency.
- ✓
Implement a retrieval-augmented generation (RAG) pipeline using Vertex AI Search to fetch relevant documents before generating answers.
Why this is correct
RAG dynamically retrieves relevant context, enabling accurate answers for rare issues.
- ✗
Collect more data on rare issues and continue fine-tuning the model weekly.
Why it's wrong here
Fine-tuning requires large amounts of data and may not scale to every rare issue; also time-consuming.
- ✗
Use a few-shot prompt with 10 examples of rare problems and solutions.
Why it's wrong here
Few-shot examples are limited and cannot cover the diversity of rare issues.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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