Weekend batches
October 2026
09:00–15:00 · flexible hours
$799per learner
24 Oct 2026
4 of 25 seats available
31 Oct 2026
10 of 25 seats available
Generative AI
Google Gemini API Development in Iran
Master Google Gemini API Development with hands-on training in Generative AI, API integration, and AI-powered application development. Learn to integrate Gemini models, implement prompt engineering, generate structured outputs, and build intelligent chatbots using Python or JavaScript. Develop practical skills in connecting applications with Google's AI capabilities and creating innovative solutions. Enhance your development expertise with StepMerit's Google Gemini API Development training in Iran, and prepare for emerging opportunities in AI application development.
40 Hours/5 daysIntermediate4.5 · 1,000 reviews
Training calendar
Upcoming four-month course schedule
A new weekday and weekend batch starts every week. Each batch runs for 5 training days, based on the course duration of 40 Hours/5 days.
Weekend batches
November 2026
09:00–15:00 · flexible hours
$799per learner
07 Nov 2026
25 of 25 seats available
14 Nov 2026
25 of 25 seats available
21 Nov 2026
25 of 25 seats available
28 Nov 2026
25 of 25 seats available
Weekend batches
December 2026
09:00–15:00 · flexible hours
$799per learner
05 Dec 2026
25 of 25 seats available
12 Dec 2026
25 of 25 seats available
19 Dec 2026
25 of 25 seats available
26 Dec 2026
25 of 25 seats available
Weekend batches
January 2027
09:00–15:00 · flexible hours
$799per learner
02 Jan 2027
25 of 25 seats available
09 Jan 2027
25 of 25 seats available
16 Jan 2027
25 of 25 seats available
23 Jan 2027
25 of 25 seats available
30 Jan 2027
25 of 25 seats available
Course curriculum
What you’ll learn
40 Hours/5 daysIntermediate
Module 1: Introduction to Google Gemini API
- Overview of Google Gemini and Generative AI
- Understanding Large Language Models (LLMs)
- Gemini API capabilities and supported use cases
- Applications of Gemini in modern software development
- Introduction to Google Gemini API Development training in Iran
Module 2: Development Environment Setup
- Setting up the development environment
- Installing Python or configuring JavaScript tools
- Introduction to Google AI Studio
- Generating and configuring Gemini API keys
- Understanding API access, quotas, and usage limits
Module 3: API Fundamentals
- Understanding REST APIs and HTTP communication
- Working with JSON requests and responses
- API endpoints and request parameters
- Handling HTTP status codes and API errors
- Testing API requests using suitable development tools
Module 4: Connecting Applications to Gemini API
- Installing the appropriate Gemini SDK
- Connecting applications to Gemini models
- Sending prompts and receiving responses
- Configuring model parameters
- Managing API credentials securely
Module 5: Prompt Engineering Fundamentals
- Understanding prompt structure and instructions
- Writing clear and effective prompts
- Using context and examples in prompts
- Improving response relevance and consistency
- Testing and refining prompts for different tasks
Module 6: Text Generation and Content Processing
- Generating text using Gemini models
- Summarising documents and long-form content
- Extracting information from text
- Performing classification and text transformation
- Building content assistance applications
Module 7: Conversational AI Development
- Designing conversational applications
- Managing multi-turn conversations
- Maintaining conversation history and context
- Creating chatbot response workflows
- Implementing basic conversation error handling
Module 8: Multimodal AI Applications
- Introduction to multimodal model capabilities
- Processing supported image inputs
- Understanding supported audio and video use cases
- Combining text with other supported input types
- Developing practical multimodal application scenarios
Module 9: Structured Outputs and Data Extraction
- Understanding structured AI responses
- Generating JSON-formatted outputs
- Extracting data from unstructured text
- Defining response schemas where supported
- Validating outputs before application use
Module 10: Function Calling and Tool Integration
- Understanding function calling concepts
- Defining tools and function declarations
- Connecting model responses to application functions
- Processing tool results and returning responses
- Implementing tool execution and validation safeguards
Module 11: Embeddings and Semantic Search
- Introduction to embeddings and vector representations
- Understanding semantic similarity
- Exploring vector search concepts
- Connecting retrieval workflows with AI applications
- Introduction to Retrieval-Augmented Generation (RAG)
Module 12: Application Integration
- Integrating Gemini API into web applications
- Connecting AI features with backend services
- Processing user input and displaying responses
- Working with databases and external services
- Designing reusable application components
Module 13: Security and Responsible AI
- Secure API key storage and management
- Understanding authentication and access controls
- Handling sensitive information responsibly
- Applying input validation and output checks
- Understanding safety filters and responsible AI practices
Module 14: Testing, Troubleshooting, and Optimisation
- Testing API integrations and application workflows
- Handling errors, timeouts, and rate limits
- Understanding token usage and API costs
- Evaluating response quality and consistency
- Improving performance, reliability, and maintainability
Module 15: Deployment and Capstone Project
- Preparing an application for deployment
- Configuring environment variables and secrets
- Testing the complete application workflow
- Building a Gemini-powered chatbot or content assistant
- Demonstrating the project and reviewing implementation outcomes
- Identifying opportunities for future improvements
Entry requirements and prerequisites
- Basic Programming Knowledge: Familiarity with Python or JavaScript fundamentals, including variables, functions, loops, and conditional statements.
- REST API Fundamentals: Understanding how APIs work and how applications communicate with external services.
- HTTP and JSON Basics: Knowledge of HTTP requests, responses, status codes, and JSON data formats.
- Basic Generative AI Knowledge: A general understanding of Artificial Intelligence, Large Language Models (LLMs), and Generative AI concepts is helpful but not mandatory.
- Development Environment: Familiarity with using an IDE such as Visual Studio Code, a terminal, and package managers is recommended.
- API Authentication: Basic awareness of API keys, environment variables, and secure credential management.
- Problem-Solving Skills: Ability to understand programming logic, troubleshoot errors, and develop simple applications.
- Internet Connectivity: Access to a reliable internet connection and a compatible development environment for practical exercises.
- Google AI Studio and Gemini API Access: Access to Google AI Studio or an eligible Google Cloud environment may be required to obtain API credentials and complete hands-on activities, subject to current availability and usage limits.
- Suitable for Beginners: Prior experience with the Gemini API is not required. Learners with basic programming knowledge can build their skills through guided exercises and practical projects.
Career value
Reasons to choose this course
Master Google Gemini API
Learn to integrate Gemini models into applications and access powerful Generative AI capabilities.
Build AI-Powered Applications
Develop intelligent chatbots, content generation tools, and interactive AI solutions.
Learn Prompt Engineering
Create effective prompts to improve the relevance, quality, and usefulness of AI-generated responses.
Gain Practical Programming Skills
Use Python or JavaScript to connect applications with Gemini API services.
Implement Structured Outputs
Generate organised responses in formats such as JSON for application workflows.
Explore Function Calling
Connect AI models with external tools, functions, and application services.
Advantages
Turn learning into practical capability
Multimodal AI Integration
Understand how to work with supported text, image, audio, and video inputs to develop applications for different business requirements.
Intelligent Workflow Design
Learn how to incorporate Gemini API capabilities into existing software workflows to support information processing and task automation.
Context-Aware Application Development
Explore techniques for managing conversation context and providing relevant information to improve the usefulness of AI-powered applications.
AI Output Evaluation
Understand how to assess generated responses for relevance, consistency, accuracy, and suitability for specific application requirements.
Application Performance Optimisation
Learn to consider latency, token usage, API limits, and response quality when designing efficient AI-enabled software.
Flexible Solution Architecture
Explore how to design modular applications that can adapt to changing business needs, evolving AI capabilities, and future integration requirements.
Exam format & assessment
- Exam Level: Beginner to Intermediate – Google Gemini API Developer
- Exam Type: Online computer-based assessment consisting of multiple-choice and scenario-based questions.
- Number of Questions: 40 questions.
- Exam Duration: 90 minutes.
- Passing Score: 70% (28 correct answers out of 40).
- Assessment Platform: StepMerit online assessment platform.
- Practical Assessment: May include an optional hands-on task involving Gemini API integration, prompt creation, or basic AI application development.
- Evaluation Criteria: Learners are assessed on their understanding of Gemini API fundamentals, API integration, prompt engineering, multimodal capabilities, structured outputs, function calling, security, and error handling.
- Certification: Learners who meet StepMerit's assessment and course completion requirements may receive a StepMerit Course Completion Certificate. This is not an official Google certification.
- Preparation Guidelines: Review course modules, practise API requests, understand authentication and error handling, and complete the recommended hands-on exercises before attempting the assessment.
- Training Support: StepMerit's Google Gemini API Development training in Iran, helps learners prepare through guided instruction and practical exercises.
Bring your learning together
Course conclusion
The Google Gemini API Development course equips learners with practical skills to integrate Google's Generative AI capabilities into modern applications. Learn API integration, prompt engineering, text generation, conversational AI, multimodal processing, structured outputs, function calling, and application security through hands-on exercises and project-based learning. StepMerit's Google Gemini API Development training in Iran, helps aspiring AI developers, software engineers, Python developers, and technology professionals strengthen their development skills, build intelligent AI-powered applications, and prepare for opportunities in AI engineering, software development, and intelligent automation.
Target audience
- Python Developers
- JavaScript Developers
- Software Engineers
- AI and Machine Learning Professionals
- Generative AI Developers
- Web Application Developers
- API Developers and Integration Specialists
- Cloud and Application Developers
- Data Engineers and Data Professionals
- IT Professionals
- Technical Consultants and Solution Architects
- Students and Aspiring AI Developers
- Technology Entrepreneurs and Startup Founders
- Automation Engineers
- Professionals Interested in Generative AI Application Development
Verified learning evidence
Expertise, recognition and learner outcomes
Course-specific expertise
- Professional Experience: 4+ years in software development and related technologies.
- Training Experience: 100+ hours of technical training delivered.
- Candidates Trained: 150+ candidates trained across relevant technical subjects.
- Technical Expertise: Google Gemini API integration, Python or JavaScript development, REST APIs, Generative AI, Large Language Models (LLMs), and prompt engineering.
- Application Development: Experience with AI-powered chatbots, intelligent shopping assistants, product information summarisation, structured outputs, and application integration.
- Hands-on Training: Practical demonstrations covering API configuration, prompt design, response processing, error handling, and secure credential management.
- Project-Based Learning: Guidance on developing Gemini-powered applications, including shopping assistants and AI-driven product discovery prototypes.
- Assessment and Support: Assistance with coding exercises, project implementation, troubleshooting, and evaluation of learner outcomes.
- Industry-Relevant Learning: Focus on practical development techniques, responsible AI practices, API security, and application reliability.
Documented learner outcomes
- Problem Statement – An e-commerce organisation manages a large product catalogue containing product descriptions, prices, specifications, customer enquiries, product images, and availability information. Customers often struggle to discover relevant products, compare product features, understand specifications, and receive quick answers to shopping-related questions. The organisation wants to develop an AI-powered shopping application using Google Gemini API to improve product discovery, provide intelligent shopping assistance, generate product summaries, and support personalised recommendations while maintaining product information accuracy, customer privacy, secure API access, and responsible AI practices.
- How We Overcame the Problem – Learners analysed common shopping workflows and identified suitable use cases for Google Gemini API integration. They developed a Python- or JavaScript-based application connected to a supported Gemini model, prepared sample product catalogue data, designed context-aware prompts, and implemented workflows for product search assistance, comparison, summarisation, and customer enquiry handling. Learners integrated verified product information into model prompts, applied input validation, implemented error handling, secured API credentials, and tested generated responses to improve reliability and prevent unsupported product claims.
- Overall Implementation – Learners implemented a shopping AI application workflow covering requirement identification → product catalogue preparation → product data organisation → development environment configuration → Gemini API access setup → Python or JavaScript application development → relevant product retrieval → prompt construction → Gemini API integration → response generation → output validation → security checks → testing → final response delivery. The implementation demonstrated how Google Gemini API can support intelligent product discovery, product comparison, product description summarisation, shopping enquiries, and conversational customer assistance.
- Product Catalogue Processing – Learners prepared sample shopping catalogue data containing product names, categories, prices, descriptions, specifications, stock status, and relevant product attributes. They organised and validated the information so that the application could retrieve appropriate product records and provide the Gemini model with reliable context for customer requests.
- AI-Powered Shopping Assistance – Learners developed a prototype shopping assistant that responds to customer questions using Gemini API and relevant catalogue information. The application can assist customers with finding products, understanding features, exploring suitable alternatives, and obtaining answers to common shopping enquiries. Responses are grounded in available product data to reduce misleading claims about products, prices, and availability.
- Intelligent Product Recommendations – Learners explored how Gemini API can interpret customer preferences, budgets, intended use, and product requirements to help identify suitable options. They designed workflows that retrieve candidate products from the catalogue before generating recommendation explanations, ensuring that suggested products correspond to actual catalogue records.
- Product Comparison and Summarisation – Learners implemented workflows for comparing product specifications, summarising descriptions, and presenting key differences in a readable format. They designed prompts that organise product attributes into clear comparisons and ensure that important details, such as price, warranty, compatibility, and specifications, are based on verified source data.
- Prompt Engineering Evidence – Learners created prompts containing clear instructions, customer requirements, relevant product information, response constraints, and expected output formats. They tested alternative prompt designs to improve recommendation relevance, response consistency, and clarity, while instructing the model to acknowledge missing information rather than invent product details.
- Google Gemini API and Application Integration – Learners connected a Python or JavaScript application to a supported Gemini model through an appropriate SDK or API interface. They configured API credentials, submitted customer prompts, processed model responses, and developed reusable functions for product discovery, product summaries, comparisons, and shopping enquiry handling.
- Multimodal Shopping Application – Learners explored supported Gemini multimodal capabilities for shopping use cases, such as analysing product images supplied by users when the selected model and API support the required inputs. They demonstrated how image-related information could assist product discovery while validating any extracted details against trusted catalogue records before presenting them as confirmed facts.
- Structured Output and Product Information Extraction – Learners implemented structured responses, such as JSON-formatted product summaries, comparison attributes, and shopping enquiry categories, where supported by the selected model and API. They validated returned data against expected fields and application requirements before displaying the results or passing them to other application components.
- Conversational Shopping Workflow – Learners developed a conversational interface that allows customers to ask follow-up questions, refine product preferences, and compare alternative products. They explored how to maintain relevant conversation context, retrieve updated catalogue records when needed, and provide responses appropriate to the customer's latest requirements.
- Error Handling and Application Reliability – Learners tested scenarios involving invalid inputs, authentication failures, API errors, request timeouts, rate limits, unavailable services, malformed responses, and missing product information. They implemented exception handling, fallback messages, response validation, and suitable retry strategies to improve the reliability of the shopping application prototype.
- Security and Responsible AI Implementation – Learners applied secure API key management, server-side credential handling, input validation, and privacy-aware development practices. They explored the importance of protecting customer information, avoiding unnecessary collection of personal data, validating generated product claims, and requiring explicit confirmation for consequential actions. Product prices, stock availability, checkout, payment processing, and order placement remain controlled by trusted application services rather than generated model responses.
- Practical Evidence of Learning – Learners produced Python or JavaScript source code, Gemini API integration examples, prompt templates, sample product catalogue data, product recommendation demonstrations, product comparison summaries, structured response examples, multimodal input demonstrations where supported, conversational shopping workflows, test cases, error-handling implementations, security checklists, and project documentation demonstrating their practical development skills.
- Overall Learner Outcome – By completing the implementation, learners demonstrated the ability to develop AI-powered shopping application prototypes using Google Gemini API, prepare and retrieve product information, design effective prompts, generate product summaries and comparisons, implement conversational assistance, explore multimodal AI capabilities, validate structured responses, handle API errors, evaluate response quality, and apply security and responsible AI practices. The project provides practical evidence of how Generative AI can improve product discovery and customer assistance while relying on trusted catalogue data for factual product information.
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