Gemini 3 prompting guide Preview This product or feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms, and the Additional Terms for Generative AI Preview Products. You can process personal data for this product or feature as outlined in the Cloud Data Processing Addendum, subject to the obligations and restrictions described in the agreement under which you access Google Cloud. Pre-GA products and features are available "as is" and might have limited support. For more information, see the launch stage descriptions. Caution: gemini-3-pro-preview will be deprecated and removed on March 26, 2026. Migrate any workflows to gemini-3.1-pro-preview. To see an example of Gemini 3 Pro, run the "Intro to Gemini 3 Pro" notebook in one of the following environments: Open in Colab | Open in Colab Enterprise | Open in Vertex AI Workbench | View on GitHub Prompting is a key part of working with any Gemini model and the new features of Gemini 3 models can be prompted to help solve complex problems and achieve other tasks, such as interpreting large amounts of text, solving complex mathematical problems, or even creating images and videos. This guide provides a variety of prompting strategies to help you get the most from Gemini 3 on Vertex AI for a variety of use cases. Temperature tuning For Gemini 3, we strongly recommend keeping the temperature parameter at its default value of 1.0. Gemini 3's reasoning capabilities are optimized for the default temperature setting and don't necessarily benefit from tuning temperature. Changing the temperature (setting it to less than 1.0) may lead to unexpected behavior, looping, or degraded performance, particularly with complex mathematical or reasoning tasks. Prompting strategies The following sections describe a variety of prompting strategies that you can use with Gemini 3 models. Lowering response latency For lower latency responses, try setting the thinking level to LOW and using system instructions like think silently. Distinguishing between deduction and external information In some cases, providing open-ended system instructions like do not infer or do not guess may cause the model to over-index on that instruction and fail to perform basic logic or arithmetic or synthesize information found in different parts of a document. Rather than a large blanket negative constraint, tell the model explicitly to use the provided additional information or context for deductions and avoid using outside knowledge. Examples What was the profit? Do not infer. This instruction is ineffective because the do not infer instruction is too broad. You are expected to perform calculations and logical deductions based strictly on the provided text. Do not introduce external information. Here, the instruction makes it clear that the model should use the provided context for calculations and reasoning. Using split-step verification When the model encounters a topic it doesn't have sufficient information for (such as an obscure place) or is asked to perform an action it doesn't have capability for (such as accessing a specific live URL), it may generate seemingly plausible but incorrect information in an attempt to satisfy the request. To avoid this, split the prompt into two steps: first, verify that the information or intended capability exists, then generate the answer based off of that information or capability. Example Verify with high confidence if you're able to access the New York Times home page. If you cannot verify, state 'No Info' and STOP. If verified, proceed to generate a response. Query: Summarize the headlines from The New York Times today. Organizing important information and constraints When dealing with sufficiently complex requests, the model may drop negative constraints (specific instructions on what not to do) or formatting or quantitative constraints (instructions like word counts) if they appear too early in the prompt. To mitigate this, place your core request and most critical restrictions as the final line of your instruction. In particular, negative constraints should be placed at the end of the instruction. A well-structured prompt might look like this: [Context and source material] [Main task instructions] [Negative, formatting, and quantitative constraints] Using personas The model is designed to treat the persona it is assigned seriously and will sometimes ignore instructions in order to maintain adherence to the described persona. When using a persona with your prompts, review the persona that's assigned to the model and avoid ambiguous situations. Example You are a data extractor. You are forbidden from clarifying, explaining, or expanding terms. Output text exactly as it appears. Do not explain why. Maintaining grounding The model may use its own knowledge to answer your prompt, which might conflict with any provided context. While the model is designed to be helpful, if you provide a hypothetical scenario that contradicts real-world facts (prompting with context such as Crabs are fictional and have never existed.), the model may revert to its training data rather than your prompt to align your request with its existing information. If you need to work in context that isn't grounded in real-world information, explicitly state that the provided context is the only source of truth for the current session. Example You are a strictly grounded assistant limited to the information provided in the User Context. In your answers, rely **only** on the facts that are directly mentioned in that context. You must **not** access or utilize your own knowledge or common sense to answer. Do not assume or infer from the provided facts; simply report them exactly as they appear. Your answer must be factual and fully truthful to the provided text, leaving absolutely no room for speculation or interpretation. Treat the provided context as the absolute limit of truth; any facts or details that are not directly mentioned in the context must be considered **completely untruthful** and **completely unsupported**. If the exact answer is not explicitly written in the context, you must state that the information is not available. Synthesizing multiple sources of information When information is presented in multiple places across a source of context, the model can sometimes stop processing additional information after the first relevant match. When working with large datasets, like entire books, codebases, or long videos, place your specific instructions or questions at the end of the prompt, after the data context. You can also anchor the model's reasoning to the provided data by starting your question with a phrase like Based on the entire document above.... Example instruction Based on the entire document above, provide a comprehensive answer. Synthesize all relevant information from the text that pertains to the question's scenario. Steering output verbosity By default, Gemini 3 models are less verbose and designed to prioritize providing direct and efficient answers. If your use case requires a more conversational persona, you must explicitly steer the model to be chattier in the prompt. Example instruction Explain this as a friendly, talkative assistant. Tune the sampling parameters In each request, you send not only the multimodal prompt but a set of sampling parameters to the model. The model can generate different results for different parameter values. Experiment with the different parameters to get the best values for the task. The most commonly adjusted parameters are the following: Temperature top-P Temperature Temperature is used for sampling during response generation, which occurs when top-P and top-K are applied. Temperature controls the degree of randomness in token selection. Lower temperatures are good for prompts that require a more deterministic and less open-ended or creative response, while higher temperatures can lead to more diverse or creative results. A temperature of 0 is deterministic, meaning that the highest probability response is most likely to be selected. For most use cases, start with a temperature of 0.4. If you need more creative results, increase the temperature. If you observe clear hallucinations, reduce the temperature. Top-P Top-P changes how the model selects tokens for output. Tokens are selected from the most to least probable until the sum of their probabilities equals the top-P value. For example, if tokens A, B, and C have a probability of 0.6, 0.3, 0.1 and the top-P value is 0.9, then the model will select either A or B as the next token by using temperature and excludes C as a candidate. Specify a lower value for less random responses and a higher value for more random responses. The default value of top-P is 1.0.