• Prompt: A conversation starter or instructional message to guide Gemini AI.
• Gemini for Google Workspace: Google's AI assistant integrated into Workspace apps.
• Persona: Specifying the AI's role or point of view.
• Task: The specific action or output requested from the AI.
• Context: Background information provided to the AI.
• Format: Specifying the desired structure or style of the output.

This guide introduces effective prompting for Google's Gemini AI within Workspace. It explains prompts, specifies elements for better responses, and provides tips, scenarios, and samples for various roles. It emphasizes iterative refinement to improve results.

Many specific examples demonstrate prompt writing concepts. For each business role, there's a detailed prompt iteration example and other sample prompts for common use cases.

Well-crafted prompts enable Gemini to assist with various tasks like writing, research, and organization. However, limitations exist as generative AI is new and can be unpredictable, requiring careful review of outputs.

Generative AI is promising but imperfect. Prompts may need refinement, outputs are starting points, and humans are ultimately responsible.

Prompting Gemini has major implications for productivity. Mastering this skill can harness AI to enhance tasks, impacting individual and organizational productivity. However, it raises questions about work, skills, and judgment.

• Surprised by breadth and specificity of use case examples.
• Interested in prompt writing as an iterative process.

1. Key elements to specify in prompts?
2. Main business functions and use cases covered?
3. Current limitations of generative AI?
4. Role of human vs. AI in final outputs?

1. Evolution of prompt writing as AI advances?
2. Mechanisms for Gemini to learn from prompts?
3. Balancing individual customization with organizational standardization?
4. Other benefiting business functions or industries? Challenges?
5. Expected evolution of Gemini based on feedback?

• Underlying language models and architectures.
• Prompt engineering techniques.
• Human-AI interaction and collaboration theories.
• Evolving skill sets for knowledge work.
• Measuring and improving AI reliability and accuracy.
• Applying generative AI to other domains.
• Future advancements in controllable and explainable AI.

The guide doesn't contain a formal bibliography or citations.