Our Business Analysis team spends hours summarizing lengthy market research documents into executive briefs. I want to leverage Vertex AI and the Gemini models to automate this. How do I set up a simple pipeline where a PDF uploaded to a GCS bucket automatically triggers a Vertex AI prompt to summarize the content? Also, how can we ensure the "Temperature" setting is adjusted so the AI stays factual and doesn't hallucinate data that isn't in the original report?
3 answers
To build this, you should use a "Cloud Function" as the glue. Set the function to trigger on "Finalize" events in your GCS bucket. When a PDF is uploaded, the function sends the text to the Vertex AI gemini-pro API. For factual summaries, you must set the temperature to 0.0 or 0.1. This makes the model "deterministic," meaning it will choose the most likely words based strictly on the input text rather than getting "creative." You should also use "System Instructions" to tell the model: "Summarize the following document. Use only the information provided. If the information is missing, say 'not available'."
Are you planning to use "Grounding" with Vertex AI Search to allow the model to cite the exact page and paragraph numbers where it found the information?
Gemini 1.5 Pro's massive context window is perfect for this. You can upload 10+ long reports at once and ask it to find common themes across all of them.
I agree, Cynthia. The "Long Context" window completely changes how we handle competitive analysis. We can now compare five different annual reports in a single prompt.
Robert, that is exactly our next step. "Grounding" is essential for Business Analysts because an unverified summary is a liability. By connecting Gemini to our internal corpus via Vertex AI Search, the model provides "Citations" for every claim it makes. This allows our analysts to quickly click the link and verify the data in the original source document. It’s a huge "Productivity Hack" that has reduced our report preparation time by nearly 70%. We also implemented a feedback loop where analysts can "Thumbs Up/Down" a summary to help us fine-tune the prompt instructions over time.