Stanford AI Playground: The Next Level

This session builds on foundational knowledge of the Stanford AI Playground and focuses on using the platform as a practical problem-solving environment. Participants explore strategies for defining problems clearly, selecting appropriate AI models, and designing prompts that guide AI tools toward useful outcomes.

Through demonstrations and guided activities, the class highlights how the Playground can help analyze documents and datasets, compare information, generate insights, and support decision-making.

The session also introduces ways to shape AI responses using parameters and presets, allowing participants to adjust how models generate results for different tasks. Participants work with advanced platform features that support structured AI-assisted work, including the Prompt Library for saving and reusing prompts with customizable variables, Memories to preserve helpful context across interactions, Artifacts for creating and refining structured outputs such as data visualizations, prototyping interfaces, or code, and File Search for locating and analyzing information within uploaded materials.

Emphasis is placed on evaluating AI outputs, refining prompts through iteration, and applying human oversight to ensure accuracy and responsible use. By the end of the session, participants will have hands-on experience using the Stanford AI Playground to address complex questions, break problems into manageable steps, and leverage AI as a collaborative tool for research, analysis, and everyday workflow challenges.

You will have the opportunity to:

1. Apply structured prompting techniques to guide AI models toward useful problem-solving outcomes.
2. Save and reuse prompts using the Prompt Library, including prompts with variables that function as flexible templates.
3. Select appropriate AI models and adjust parameters or presets based on task requirements.
4. Use Memories to retain relevant context and improve AI responses across interactions.
5. Generate and refine structured outputs using Artifacts for documents, code, and analytical results.
6. Use File Search to locate, analyze, and synthesize information from uploaded materials.

Introduction and Review

• Review of key Stanford AI Playground features
• Moving from experimentation to practical problem-solving

Prompting for Problem Solving

• Framing problems effectively for AI
• Prompt refinement and iteration
• Role-based prompting and structured prompts
• Using the Prompt Library to save, organize, and reuse prompts
• Creating prompt templates with variables for flexible, repeatable workflows

Model and Tool Selection

• Comparing model strengths and capabilities
• Choosing models based on reasoning, analysis, or speed
• Combining AI tools for multi-step workflows

Parameters and Presets

• Understanding model parameters
• Using presets to shape AI responses
• Adjusting generation settings for different tasks

Working with Information and Documents

• Uploading and analyzing files
• Extracting insights from long documents
• Comparing information across sources

Memories, Artifacts, and File Search

• Using Memories to store helpful context
• Creating and refining Artifacts such as data visualization, prototyping interfaces, and code execution
• Using File Search to locate information within uploaded files and datasets

Data and Analysis Workflows

• Using AI for exploratory analysis
• Generating summaries, insights, and visualizations
• Interpreting AI-generated results