20+ practice questions focused on Techniques to Improve Generative AI Model Output — one of the most tested topics on the Google Cloud Generative AI Leader Generative AI Leader exam. Each question includes a detailed explanation so you learn why the right answer is correct.
Start Techniques to Improve Generative AI Model Output PracticeA healthcare company is using a fine-tuned version of PaLM 2 on Vertex AI to generate clinical notes from doctor-patient conversations. The model was fine-tuned on a dataset of 10,000 de-identified transcripts and corresponding notes. During testing, the generated notes are grammatically correct and well-structured, but they often contain subtle inaccuracies: for example, they might mention a medication that was not discussed, or omit a key symptom. The team has already tried increasing the training epochs and adjusting learning rates, with minimal improvement. They need a solution that can be implemented quickly to improve factual accuracy without retraining the entire model. The team has access to a large archive of verified clinical notes and a small set of recent conversation-to-note pairs that have been manually reviewed and corrected. The inference pipeline currently uses a single call to the model with the conversation transcript as input. What should the team do?
Explanation: Retrieval-augmented generation (RAG) directly addresses the core issue of factual inaccuracy without retraining. By retrieving verified clinical notes similar to the current conversation from the archive and injecting them as context in the prompt, the model gains access to ground-truth examples that anchor its output to factual details. This approach leverages the team's existing archive and small set of corrected pairs to provide relevant, accurate context, improving precision without modifying the model's weights.
For a document summarization task, a team wants to produce concise summaries without losing key information. Which combination of techniques is most effective?
Explanation: Using few-shot examples provides the model with explicit patterns of desired summarization behavior, guiding it to produce concise outputs, while reducing max output tokens enforces a hard length constraint that prevents verbosity. This combination directly addresses the goal of conciseness without losing key information by conditioning the model on high-quality examples and capping the response length.
A travel company fine-tuned a language model on customer chat logs to provide travel recommendations. After deployment, they receive complaints that the model sometimes generates inappropriate or offensive content. What is the most effective approach to improve output safety while preserving overall performance?
Explanation: A post-processing safety classifier acts as a guardrail that can detect and filter or rewrite unsafe outputs without altering the underlying model's weights or training data. This approach preserves the model's overall performance on safe, relevant recommendations while adding a dedicated safety layer that can be independently tuned and updated as new safety requirements emerge. Unlike prompt engineering or hyperparameter adjustments, a classifier provides a robust, policy-enforced mechanism to catch edge cases that the model might otherwise generate.
A team deployed a fine-tuned model for code generation. After training, the model produces syntactically correct but functionally wrong code. What is the most likely cause?
Explanation: Overfitting to training data causes the model to memorize specific code patterns and syntax from the training set without learning the underlying logic or functional requirements. This results in syntactically correct outputs that fail to generalize to new, unseen coding tasks, producing functionally wrong code despite proper syntax.
A team notices the RAG pipeline sometimes retrieves irrelevant documents. Which THREE improvements should they consider? (Choose three.)
Explanation: Option A (Add a reranking step) is correct because a cross-encoder or LLM-based reranker re-scores the initially retrieved candidates against the query, promoting truly relevant passages and demoting semantically similar but off-topic ones, which directly reduces irrelevant retrievals. Option D (Reduce the number of retrieved documents) is correct because lowering top-k limits the context to the highest-scoring matches, cutting the chance that marginal, irrelevant chunks are passed to the generator. Option E (Use a higher quality embedding model) is correct because better embeddings produce more discriminative vector representations, improving recall and precision of the similarity search so irrelevant documents are less likely to be retrieved. Option B is not appropriate because replacing embedding similarity with exact keyword matching sacrifices semantic recall and would worsen results for paraphrased or conceptual queries. Option C is not appropriate because increasing chunk size dilutes the embedding with multiple topics, typically making retrieval less precise rather than more relevant.
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