Introduction to Nano Banana Training in Copenhagen, Denmark

The Introduction to Nano Banana Training is a beginner-friendly program designed to introduce participants to Nano Banana, a lightweight Generative AI / small-model concept focused on fast, cost-efficient, and task-specific AI workflows.
The course explains how compact AI models are used for quick inference, edge/near-edge deployments, rapid prototyping, and workflow automation, especially alongside modern AI platforms and prompt-driven development.

Introduction to Nano Banana

• What is Nano Banana (concept and positioning)

• Why lightweight AI models matter

• Nano models vs large language models

• Common misconceptions about “small AI”

Understanding Lightweight AI Models

• Characteristics of compact AI models

• Speed, latency, and resource efficiency

• Accuracy vs cost trade-offs

• Typical deployment environments

Nano Banana Use Cases

• Task-focused AI assistants

• Text summarization and classification

• Simple content generation

• Automation and workflow augmentation

• Edge and near-edge AI scenarios

Prompting for Small Models

• Prompt design principles for lightweight AI

• Keeping prompts short and effective

• Structured outputs and constraints

• Avoiding over-complex instructions

Workflow Design with Nano Banana

• Single-step vs multi-step AI workflows

• Human-in-the-loop patterns

• Combining Nano Banana with larger models

• Fallback and escalation strategies

Cost, Performance & Optimization

• Token and compute efficiency

• Latency optimization techniques

• Choosing the right model for the task

• Measuring ROI of lightweight AI

Integration Concepts

• Using Nano Banana in applications

• API-driven workflows

• Automation and orchestration basics

• AI toolchain integration overview

Safety, Limitations & Responsible Use

• Understanding limitations of small models

• Hallucination and accuracy risks

• Safe prompt practices

• Responsible AI guidelines

Hands-on Exercises

Summary and Conclusion