To reduce hallucinations with Gemini 3, the most important step is to ground the model in clear, specific context and give it permission to say “I don’t know.” Instead of vague prompts like “Explain everything about our product’s pricing,” provide explicit context and instructions: “Using only the context below, answer the question. If the answer is missing, respond with ‘I don’t know based on this information.’” That one sentence often makes a noticeable difference, because you are telling the model the correct behavior when data is incomplete. Also avoid leading questions that imply facts; phrase questions neutrally to discourage the model from fabricating details.
Structure helps a lot. Use sections in your prompt: “System instructions,” “Context,” and “User question.” Under “Context,” paste retrieved documents, code snippets, or data. Under “User question,” ask clearly and concisely. This teaches Gemini 3 where it should look for answers and keeps it from mixing up instructions with content. For multi-step tasks, you can ask it to show its work in a constrained way, like “First list the relevant facts from the context, then answer the question based only on those facts.” That pattern encourages reasoning anchored in your data rather than free association.
Retrieval-augmented generation is the main tool for keeping Gemini 3 honest in knowledge-heavy scenarios. Store your documents or records in a vector database such asMilvus or Zilliz Cloud., retrieve the top relevant chunks for each query, and include them as context in the prompt. If the retrieval result is empty or low-confidence, you can decide not to call Gemini 3 at all or ask it to respond with a “no information” message. You can also log hallucination cases, review them periodically, and update your prompts and retrieval strategy based on real failures. Over time, this feedback loop is what brings hallucination rates down to an acceptable level for production use.