Google AI Studio Workflow Optimization Training in Kraków, Poland

The Google AI Studio Workflow Optimization Training is a practical, hands-on program designed to help professionals design, test, optimize, and scale Generative AI workflows using Google AI Studio.
The course focuses on prompt lifecycle management, rapid prototyping, evaluation, iteration, and production-readiness for building reliable AI-powered applications using Gemini models.

Introduction to Google AI Studio

• Overview of Google AI Studio and Gemini models

• AI Studio vs traditional AI development

• Common enterprise and product use cases

• Workflow-centric AI development approach

Understanding AI Workflows

• From prompt to production

• Single-step vs multi-step workflows

• Human-in-the-loop workflows

• Designing repeatable AI processes

Prompt Design & Structuring

• Prompt anatomy and best practices

• System, user, and instruction prompts

• Controlling tone, format, and verbosity

• Reducing ambiguity and hallucinations

Prompt Iteration & Versioning

• Rapid prompt experimentation

• Comparing prompt versions

• Tracking changes and improvements

• Managing prompt libraries

Model Selection & Configuration

• Choosing the right Gemini model

• Temperature, tokens, and safety settings

• Trade-offs between quality, cost, and latency

• Model comparison strategies

Testing & Evaluation Workflows

• Creating test cases and datasets

• Evaluating response accuracy and relevance

• Structured output validation

• Regression testing for prompts

Multi-Step & Chained Workflows

• Breaking tasks into sub-prompts

• Chaining prompts for complex tasks

• Error handling in workflows

• Improving reliability with structured steps

Data-Aware & Contextual Workflows

• Injecting contextual data into prompts

• Working with documents and knowledge sources

• Guardrails for sensitive data

• Foundations of grounded responses

Optimization for Cost & Performance

• Token usage optimization

• Caching and reuse strategies

• Latency reduction techniques

• Scaling workflows efficiently

Safety, Governance & Responsible AI

• Content safety and moderation controls

• Bias and risk mitigation

• Compliance considerations

• Auditability and traceability

From AI Studio to Production

• Exporting workflows to applications

• API integration patterns

• Collaboration between AI, dev, and product teams

• Monitoring and continuous improvement

Hands-on Exercises

Summary and Conclusion