Building AI Apps with Nano Banana Training in Astana, Kazakhstan

The Building AI Apps with Nano Banana Training is a practical, hands-on program focused on designing and developing fast, lightweight, and cost-efficient AI applications using Nano Banana–style compact Generative AI models.
The course emphasizes application development, workflow design, prompt efficiency, and performance optimization, making it ideal for teams that want to build scalable AI apps without relying solely on large, expensive LLMs.

Participants learn how to combine small AI models, smart prompting, and modular workflows to deliver real-world AI functionality.

Introduction to Nano Banana AI Apps

• What is Nano Banana in AI application development

• Why lightweight AI models matter

• Nano Banana vs large LLM-based apps

• Common application patterns

Architecture of Lightweight AI Applications

• Components of a Nano Banana AI app

• Client, backend, and AI workflow roles

• Stateless vs stateful AI apps

• Design principles for speed and efficiency

Choosing the Right Model

• When to use small vs large AI models

• Accuracy, cost, and latency trade-offs

• Task-focused model selection

• Fallback and hybrid model strategies

Prompt Engineering for Nano Banana

• Writing concise and effective prompts

• Prompt constraints and structured outputs

• Reducing hallucinations in small models

• Prompt reuse and templates

Building Core AI App Features

• Text generation and transformation

• Classification and tagging

• Summarization and extraction

• Simple reasoning workflows

Multi-Step & Agent-like Workflows

• Breaking tasks into small AI steps

• Orchestrating workflows

• Human-in-the-loop patterns

• Error handling and retries

Performance & Cost Optimization

• Token and compute efficiency

• Caching and reuse strategies

• Latency measurement and tuning

• Cost-aware AI app design

Integrating Nano Banana into Applications

• API-based integration patterns

• Using Nano Banana in web and backend apps

• Automation and workflow tools

• Logging and monitoring basics

Security, Safety & Limitations

• Understanding model limitations

• Safe prompt practices

• Data privacy and access control

• Responsible AI usage guidelines

Testing & Evaluation

• Validating AI outputs

• Testing prompt reliability

• Measuring consistency and quality

• Continuous improvement strategies

Deployment & Scaling Strategies

• Deploying lightweight AI apps

• Scaling usage efficiently

• Managing updates and versions

• Observability and maintenance

Hands-on Exercises

Summary and Conclusion