When working with PDFs or slides, the best practice is to treat Gemini 3 as a reader that benefits from structure and clear intent. Start by telling the model what the document is (“This is a sales contract,” “These are quarterly results slides”) and what you want (“Summarize risks,” “Extract key metrics,” “Generate talking points for an executive briefing”). This helps Gemini 3 avoid generic summaries and focus on the aspects that matter for your use case. Give it explicit instructions about format too, such as “Return a bullet list of three sections: Summary, Risks, Questions.”
Next, be mindful of document size. Gemini 3 can handle long documents, but that doesn’t mean you should always send everything. If you know which sections are relevant—by page range, heading names, or content type—tell the model or pre-select those pages. For slide decks, you can instruct it to “treat each slide as a section and label your output by slide title,” which preserves the original structure. If your documents are scanned or image-based, make sure you use a pipeline that preserves reasonable text extraction or passes the pages as images Gemini 3 can interpret.
In document-heavy environments, pairing Gemini 3 with a vector database is often the most effective approach. You can chunk PDFs or slides into pages or sections, embed them, and store them inMilvus or Zilliz Cloud.. When a user asks a question about a topic, you retrieve the most relevant chunks and send those into Gemini 3 with a clear prompt: “Using only the context below, answer the question and cite which pages you used.” This improves precision, avoids noise from unrelated sections, and makes it easier to debug answers by tracing them back to specific parts of the document.