How to Prompt Gemini (Text, Image, Video)

Gemini's text generation follows similar principles to other large language models, but it has quirks worth knowing. It tends to produce well-organized, structured responses by default, often using bullet points and headers without being asked. This is helpful for research and summaries, but it can be too rigid for creative writing.

Adjust your format instruction based on the task:

• Creative or conversational outputs -- explicitly tell Gemini "Write in flowing paragraphs, not bullet points."
• Analytical tasks -- lean into its natural structure by asking for tables, ranked lists, or step-by-step breakdowns.

The Gemini prompting guide recommends being explicit about format, especially when the default doesn't match your needs.

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Multi-turn conversations: Gemini retains context across long exchanges. You can build on previous responses without restating everything. However, if a conversation veers off track, starting a fresh chat often produces better results than trying to course-correct mid-thread.

Google Search grounding: When you enable it via the API, Gemini can pull in current information and cite its sources. This makes it particularly strong for research tasks where recency matters.

Gemini's built-in image generation (powered by Imagen) works differently from standalone tools like DALL-E or Midjourney. You prompt it conversationally, the same way you'd ask for text. There's no separate syntax or parameter system. Just describe the image you want in natural language.

The key advantage: you can iterate on images within the same chat. Generate an image, then say "make the background darker" or "change the dog to a golden retriever." Gemini tracks what it generated previously and applies your edits, which saves you from rewriting the entire prompt each time.

The same fundamentals from any image prompt guide apply here: specify subject, style, lighting, composition, and color. But Gemini also responds well to plain-language descriptions that would be too verbose for Midjourney's terse syntax:

"A cozy coffee shop on a rainy afternoon, seen through a foggy window, with warm yellow light inside and blue-gray tones outside"

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Current limitations to keep in mind:

• Gemini may decline to generate images of identifiable real people
• It adds visible watermarks to indicate AI-generated content
• For commercial projects, check Google's current usage policies before building workflows around Gemini-generated images

Veo 3 is Google's video generation model, accessible through Gemini. It creates short video clips from text descriptions or reference images. Prompting for video requires a different mindset than prompting for still images because you need to describe motion, timing, and transitions.

Structure your video prompt around five elements:

• Subject -- what's in the scene
• Action -- what happens
• Camera -- how the viewer sees it
• Atmosphere -- lighting and mood
• Duration cues -- pacing

Example: "A ceramic mug fills with steaming coffee as morning light streams through a kitchen window. Slow push-in on the mug. Warm, golden tones."

According to the Google Veo documentation, prompts that describe a single continuous action work better than those describing a sequence of events. Keep each clip focused on one moment or movement. If you need a longer video with multiple scenes, generate individual clips and edit them together.

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Audio generation: Veo 3 also supports audio alongside video, including dialogue, ambient sound, and sound effects. You can include audio direction in your prompt: "birds chirping in the background" or "the sound of rain on a tin roof." This multimodal capability is relatively new, so experiment with different levels of audio specificity to see what the model handles well.

Both models respond to structured prompts, but they have different strengths:

• Gemini -- native integration with Google services (Search, Workspace, Maps) makes it stronger for tasks that benefit from real-time data or file access
• ChatGPT -- broader plugin ecosystem and more mature image generation through DALL-E 3

For text generation, prompts that work well in ChatGPT will generally transfer to Gemini with minor adjustments. Gemini tends to be more concise by default, so you may need to explicitly request longer, more detailed responses when you want depth. ChatGPT tends toward verbosity, so you'll often need the opposite constraint.

For image generation, the workflow differs significantly. ChatGPT routes image requests to DALL-E with specific parameters, while Gemini uses its integrated Imagen model conversationally. Neither is strictly better; they produce different aesthetics. Test both with the same prompt and compare results for your specific use case.

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The practical takeaway: don't marry yourself to one model. Write your prompts in a portable way (clear structure, explicit constraints, examples) so they work well across both. The Role-Task-Format framework transfers perfectly between Gemini and ChatGPT because it's based on communication clarity, not model-specific tricks.

Start simple and add complexity. Write a basic prompt first, review the output, then add constraints or details in follow-up messages. Gemini's conversational memory makes this iterative approach efficient. You don't need to front-load everything into a single massive prompt.

Use system instructions when working through the API. The Gemini API docs support a system instruction field that sets persistent behavior across all messages in a session. This is the right place for role definitions, output format rules, and constraints you want applied to every response.

Take advantage of multimodal input. Gemini accepts images, PDFs, and code files as part of your prompt. Instead of describing a chart you want analyzed, upload it directly. Instead of pasting code as text, attach the file. Multimodal prompts that combine text instructions with visual references consistently outperform text-only equivalents.

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Use temperature settings intentionally:

• Low temperature (closer to 0) -- more predictable, factual responses. Best for data extraction.
• Default (middle) -- fine for general use
• High temperature (closer to 1) -- more creative, varied output. Best for brainstorming.

Adjusting temperature for specific tasks makes a noticeable difference in output quality.