Prompting Gemini Effectively

Gemini has a distinct personality and set of strengths compared to Claude and ChatGPT. Its training on diverse multimodal data, its real-time grounding in Google Search, and its optimization for Google's product ecosystem create specific behaviors you should understand and prompt around. This module covers what works differently in Gemini and the techniques that unlock its best outputs.

How Gemini's training shapes its behavior

Gemini was trained on a significantly larger and more diverse multimodal dataset than most competitors — including text, images, audio, video, and code — processed simultaneously rather than separately. This creates a model that's naturally comfortable with cross-modal reasoning but sometimes less focused on pure text depth than Claude.

Gemini thinks in connections. It naturally draws relationships across different types of information — connecting a concept in text to visual examples, linking code to documentation, relating current search results to underlying knowledge. Use this by giving it cross-modal tasks rather than purely sequential ones.

Gemini leans toward current information. Its Search grounding makes it default toward referencing recent information when available. This is useful for current-events queries but can mean it cites recent sources when established knowledge would serve better. Specify when you want established knowledge vs. current information.

Gemini is verbose by default. More so than Claude, Gemini tends toward comprehensive responses. If you want concision, specify it explicitly and enforce it — "Answer in 3 sentences. No more."

Leveraging Google Search grounding

One of Gemini's most distinctive prompting opportunities is its real-time connection to Google Search. When grounding is enabled, Gemini can pull current information into its responses in a way that goes beyond a training cutoff.

Prompting for the 1 million token context

Gemini's long context window unlocks workflows that require explicit prompting strategies to use well.

Loading documents at the start

When working with long documents, load them at the beginning of the conversation with a brief framing statement: "I'm going to share [X documents] about [topic]. Please read all of them before responding to any questions." This signals that Gemini should fully process all content before answering rather than responding to whichever part it processes first.

Asking cross-document questions

With long context, Gemini can hold multiple documents simultaneously. Frame questions explicitly as cross-document: "Across all three documents I've shared, identify where they contradict each other" or "Compare how each document handles the question of [topic]." These questions are impossible without a large context window — and Gemini handles them well.

Gemini's multimodal prompting

Because Gemini is natively multimodal, you can prompt it to reason across different types of content simultaneously rather than treating each modality separately.

Working with Gemini in Extensions

When Gemini has access to your Google data through Extensions (Gmail, Drive, Docs, YouTube, etc.), your prompts can reference your actual data rather than hypotheticals. This changes what good prompting looks like.

Instead of providing context in the prompt, you can reference context that Gemini can find: "Look through my recent emails and find any threads related to [project]" or "Check my Drive for the proposal I was working on last week."

The key skill is being specific enough about what to find without being so specific that you'd have to remember the exact file name. "The Q3 budget proposal" works better than "a financial document" (too vague) or "Q3_Budget_Final_v3_REVISED.xlsx" (too exact).

Gems — system prompts for recurring tasks

Gems are Gemini's Custom Assistant feature (available on Gemini Advanced). You create a Gem once with specific instructions, and every conversation within that Gem starts with those instructions active. This is Gemini's version of Claude Projects or ChatGPT's Custom GPTs.

Effective Gem instructions follow the same principles as good system prompts: specify the role explicitly, define always-do and never-do behaviors, describe the output format, and give examples of what good looks like.