AI Application Developer Training Course in Ahmedabad

Module 1: Software Development & AI Fundamentals

• • How software applications work: frontend, backend, databases and APIs.
• • AI, machine learning, generative AI and large language models.
• • Traditional programming versus AI-assisted development.
• • Understanding AI coding assistants and coding agents.
• • Development environment setup and terminal fundamentals.
• • Git, GitHub repositories and version control.
• • Software development lifecycle and project planning.

Module 2: Python Programming for AI Applications

• • Variables, data types, operators and control flow.
• • Functions, modules, packages and exception handling.
• • Lists, dictionaries, sets, tuples and comprehensions.
• • Object-oriented programming and reusable code.
• • File handling, JSON and environment variables.
• • Virtual environments, pip and dependency management.
• • Type hints, logging and debugging.
• • Using AI to explain, refactor and test Python code.

Module 3: Frontend Development for AI Applications

• • HTML5, CSS3 and responsive layouts.
• • JavaScript fundamentals, promises and async/await.
• • React components, props, state and hooks.
• • TypeScript fundamentals and type safety.
• • Forms, validation, API calls and loading states.
• • Chat interfaces, streaming responses and conversation history.
• • Accessibility, error messages and user experience.
• • AI-assisted UI generation and frontend debugging.

Module 4: Databases & Data Management

• • SQL fundamentals and relational database concepts.
• • PostgreSQL tables, keys, joins and constraints.
• • Database normalization and schema design.
• • CRUD operations and transaction handling.
• • Indexes and query optimization basics.
• • ORM concepts and database migrations.
• • Data validation, backups and access permissions.
• • Storing users, conversations, documents and AI usage records.

Module 5: Backend Development with FastAPI

• • HTTP, REST principles and request-response architecture.
• • FastAPI routes, path parameters and query parameters.
• • Request validation and response models.
• • Pydantic schemas and structured data validation.
• • Dependency injection and middleware.
• • Error handling, logging and configuration.
• • API documentation using OpenAPI.
• • Connecting the frontend, backend and PostgreSQL.

Module 6: LLM APIs & Generative AI Integration

• • How LLM APIs work and how to select a suitable model.
• • API keys, authentication, environment variables and secrets.
• • System instructions, prompts and conversation context.
• • Text generation, summarization and classification.
• • Structured outputs and schema validation.
• • Token usage, context windows, latency and cost management.
• • Streaming responses and conversation persistence.
• • Provider-independent integration patterns and error handling.

Module 7: Document AI, Embeddings & RAG

• • Document ingestion and text extraction.
• • Chunking, metadata and document preprocessing.
• • Embeddings and semantic similarity.
• • Vector database concepts and similarity search.
• • Retrieval-Augmented Generation architecture.
• • Building a document question-answering application.
• • Source citations and grounded answers.
• • Retrieval quality, relevance and hallucination reduction.

Module 8: AI Agents & Tool Calling

• • AI agents versus ordinary chatbots.
• • Tool calling and structured function arguments.
• • Connecting AI to approved application functions and APIs.
• • Agent state, context management and conversation memory.
• • Workflow orchestration using LangChain and LangGraph.
• • Human approval and controlled execution of sensitive actions.
• • Retry handling, timeouts and loop prevention.
• • Multi-step workflows and introductory multi-agent patterns.

Module 9: AI Coding Agents & Modern Developer Workflow

• • Using ChatGPT, Codex, Cursor, GitHub Copilot and Claude Code.
• • Writing clear coding tasks and project instructions.
• • Plan-first development and breaking features into smaller tasks.
• • Repository context, rules, reusable skills and tool integrations.
• • AI-assisted debugging, refactoring and code explanation.
• • Generating unit tests, API documentation and database migrations.
• • Reviewing diffs, dependencies and terminal commands.
• • Working with Git branches, pull requests and code reviews.

Module 10: Application Security & Responsible AI

• • Authentication, authorization and role-based access.
• • Secure sessions, password hashing and access tokens.
• • Input validation, SQL injection and cross-site scripting.
• • API rate limiting and abuse prevention.
• • Prompt injection and untrusted document handling.
• • Protecting secrets and sensitive user information.
• • AI output validation and least-privilege tool access.
• • Data privacy, audit logs and human oversight.

Module 11: Testing, Debugging & AI Evaluation

• • Unit, integration and API testing.
• • Pytest and automated test organization.
• • Mocking external APIs and handling failures.
• • Testing frontend and backend integration.
• • Creating representative AI evaluation datasets.
• • Evaluating answer accuracy, relevance and source grounding.
• • Regression testing prompts and model changes.
• • Monitoring failures, latency, token consumption and cost.

Module 12: Software Architecture & Application Quality

• • Modular application architecture and separation of concerns.
• • Service layers, reusable components and dependency management.
• • Database transactions and reliable background tasks.
• • Caching, pagination and performance optimization.
• • API versioning and integration design.
• • File storage and document processing pipelines.
• • Error recovery, retries and idempotency.
• • AI-assisted architecture reviews and technical documentation.

Module 13: Docker, CI/CD & Cloud Deployment

• • Linux and command-line fundamentals.
• • Docker images, containers and Docker Compose.
• • Environment configuration and secrets management.
• • GitHub workflows and automated test pipelines.
• • Application and database deployment.
• • Domain configuration, HTTPS and reverse proxies.
• • Logging, health checks and production monitoring.
• • Rollbacks, backups and release management.

Module 14: Advanced AI Application Patterns

• • Choosing between prompts, RAG, tool calling and agents.
• • Hybrid search, metadata filtering and retrieval strategies.
• • Multimodal AI fundamentals for text, images and documents.
• • Background processing for long-running AI tasks.
• • Model routing, fallbacks and provider abstractions.
• • Human-in-the-loop workflows.
• • Cost optimization, caching and model selection.
• • Evaluating trade-offs before adopting new AI frameworks.

Module 15: Project Application Development

• • Requirements gathering, project planning and architecture.
• • Database schema, API contracts and interface design.
• • Building authentication and core application features.
• • Integrating an LLM and document-based RAG workflow.
• • Implementing access control and usage limits.
• • AI-assisted coding, debugging and testing.
• • Code review, Git workflow and deployment preparation.
• • Measuring project quality against defined acceptance criteria.

Module 16: Production Launch, Portfolio & Career Readiness

• • Deploying and demonstrating the capstone application.
• • Preparing README files and setup documentation.
• • API documentation and application architecture diagrams.
• • Demonstrating test results and AI evaluation.
• • Explaining technical choices and debugging decisions.
• • GitHub portfolio, résumé and project presentation.
• • Technical interviews and practical coding exercises.
• • Responsible use of AI coding tools in workplace environments.

What career opportunities are available after AI Application Developer training?

Depending on your skills and project experience, you can work towards roles such as Junior AI Application Developer, Python Developer, Generative AI Developer Trainee, AI Integration Developer or Software Developer working with AI features. Job requirements vary by employer, and completing a course alone does not guarantee employment.

Will AI replace AI Application Developers in the future?

AI tools can generate code and simplify some development tasks, but building reliable AI applications still requires people who understand software architecture, data, APIs, security, testing and business requirements. Learning to use AI tools effectively while developing strong programming fundamentals can help you adapt to changing industry needs.

Can I build real-world AI applications after completing this course?

You will work towards building practical projects such as AI chatbots, document question-answering systems, intelligent customer-support assistants and AI-powered business automation tools. Your ability to build production-ready applications will depend on your practice, project complexity and understanding of security and testing.

Do I need programming knowledge to learn AI Application Development?

Basic programming knowledge is helpful, but beginners can start with Python fundamentals and gradually progress to APIs, AI integration and application development. The course should build your skills step by step before introducing advanced concepts.

Is AI Application Development a good career option for IT freshers?

Yes, it can be a promising career path for freshers who enjoy programming and problem-solving. By learning Python, APIs, AI models and application development, you can build practical projects and prepare for relevant entry-level software and AI development opportunities.