Databricks-GenAI-Assoc Application Development Practice Question
A developer is configuring a RAG application and needs to ensure that the LLM response is based on specific, trusted document snippets. Which technique, when implemented correctly, helps mitigate hallucination by grounding the response in provided context?
⚠ Common exam trap
Candidates often confuse RAG with Fine-tuning or Prompt Engineering. They assume the model's internal weights are being updated, when RAG is strictly about providing external context at inference time.
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
✓
Retrieval Augmented Generation (RAG)
Retrieval Augmented Generation (RAG) is the primary technique for grounding LLM responses. By retrieving relevant, trusted documents from a vector store based on a user's query and injecting those documents into the LLM's prompt, the developer forces the model to synthesize an answer based on specific retrieved context rather than relying solely on its internal training data. This significantly reduces hallucinations and increases the accuracy and relevance of the generated responses.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increasing the model's temperature parameter
Why it's wrong here
Increasing temperature makes the model's output more random and creative. In a RAG application, this often increases the likelihood of hallucinations, as the model is encouraged to deviate from the provided context. A lower temperature is generally preferred for factual, grounded answers retrieved from documentation sources.
- ✓
Retrieval Augmented Generation (RAG)
Why this is correct
RAG grounds the model's output by providing relevant, factual information from trusted sources within the prompt. This context-based approach limits the model's tendency to hallucinate by forcing it to answer based on the provided document snippets, which are retrieved via similarity search before the generation step occurs.
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Fine-tuning the base model on all internal documents
Why it's wrong here
Fine-tuning is expensive and difficult to maintain as documentation changes. It also does not provide a source for citations. RAG is more scalable, allows for real-time document updates, and provides better grounding by including the reference context directly in the inference request, making it far superior for factual answering.
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Using a larger foundation model without context injection
Why it's wrong here
Using a larger model without providing context does not mitigate hallucinations. Even the largest models can hallucinate facts that are not present in their training data. Providing context through a RAG pipeline is the most effective method to ensure the model uses verified information for its responses.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
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