Master in GenAI for .NET Developers
Evening Batch, Nov 2026
Master in GenAI for .NET Developers
Master Generative AI and build the next generation of intelligent .NET applications.
This hands-on program takes .NET developers from Generative AI fundamentals to production-ready AI applications in C#: LLM integration, prompt engineering, structured output, multimodal AI, tool calling and AI agents, RAG, evaluation, MCP, local models and Azure AI. You build 10 mini-projects and an end-to-end capstone.
What you'll learn
- How LLMs work, tokens, context windows and cost
- Calling OpenAI, Gemini, GitHub Models and Azure OpenAI from C#
- Prompt engineering, structured JSON output and strongly typed results
- Chat applications with memory, streaming and token management
- Tool calling, AI agents and agentic workflow patterns
- Microsoft.Extensions.AI and Semantic Kernel
- Retrieval-augmented generation (RAG) with vector search, and evaluating AI apps
- Model Context Protocol (MCP) servers and clients
- Local and open models with Ollama
- Security, prompt injection, PII, performance and cost in production
- Bonus: Azure AI fundamentals
Projects you'll build
AI-powered .NET assistant · AI email assistant · Resume analyzer · Invoice analyzer · Company knowledge chatbot · Employee assistant · Company policy assistant (RAG) · MCP-powered employee assistant · Capstone: Enterprise AI Employee Assistant
Who should join
- .NET developers who want to add Generative AI to their skills
- Freshers and graduates with C# basics aiming for AI-enabled .NET roles
Prerequisites
Basic knowledge of C#, data access with ADO.NET or EF Core, and SQL.
Course completion
Learners who complete the sessions, projects and capstone receive a course completion certificate through GeeksArray.
Curriculum · 19 modules, 63 lessons
Module 1 — Generative AI Fundamentals 3 lessons · 1 session
What AI and Generative AI are, how an LLM works (input, prompt, model, response), AI agents and real business use cases.
- What AI and Generative AI are
- How an LLM works
- AI agents and where GenAI fits
Module 2 — Your First LLM Application 4 lessons · 2 sessions
LLM APIs, keys, chat completion, system and user instructions, temperature, model selection and calling an LLM from C#. Mini project: AI-powered .NET assistant.
- Calling an LLM API
- Instructions, temperature and model choice
- Calling an LLM from C#
- Mini project: AI-powered .NET assistant
Module 3 — Exploring LLM Providers 3 lessons · 1 session
OpenAI, Google Gemini, GitHub Models and Azure OpenAI; small vs large and reasoning models, and when to use which.
- OpenAI, Gemini and GitHub Models
- Azure OpenAI
- Choosing a model
Module 4 — LLM Integration with .NET 3 lessons · 2 sessions
SDKs, REST and cURL, configuration and appsettings.json, dependency injection, IHttpClientFactory, error and retry handling.
- Provider SDKs, REST APIs and cURL
- Configuration the .NET way
- Robust HTTP calls
Module 5 — Prompt Engineering 4 lessons · 2 sessions
Role, context, task, constraints, examples and output format; few-shot prompting, templates, refinement and an introduction to prompt injection. Mini project: AI email assistant.
- Anatomy of a good prompt
- Few-shot prompting and templates
- Common mistakes and prompt injection
- Mini project: AI email assistant
Module 6 — Understanding the LLM 2 lessons · 1 session
Tokens, context windows and limits, billing, model pricing, cost estimation and optimisation.
- Tokens and the context window
- Pricing and cost control
Module 7 — Structured AI Applications 3 lessons · 2 sessions
Structured and JSON output, strongly typed C# objects, JSON schema, data extraction and validation. Mini project: resume analyzer.
- Structured and JSON output
- Strongly typed results in C#
- Mini project: resume analyzer
Module 8 — Multimodal AI 3 lessons · 1 session
Text plus image, document and image analysis, chat with images and image-based extraction. Mini project: invoice analyzer.
- Text and images together
- Document and image analysis
- Mini project: invoice analyzer
Module 9 — AI Chat Applications 4 lessons · 2 sessions
Chatbot architecture, conversation history, memory, context and token management, streaming responses and a basic UI. Mini project: company knowledge chatbot.
- Chatbot architecture
- Managing context and tokens
- Streaming responses and a basic UI
- Mini project: company knowledge chatbot
Module 10 — Tool Calling & AI Agents 4 lessons · 2 sessions
Function and tool calling, tool definitions and parameters, .NET methods as tools, the agent loop and agents vs chatbots. Mini project: employee assistant.
- What tool calling is
- Running tools from .NET
- The agent loop
- Mini project: employee assistant
Module 11 — Agent Frameworks 3 lessons · 2 sessions
Microsoft.Extensions.AI abstractions and chat clients; Semantic Kernel kernel, plugins, functions, memory, multi-tool and multi-agent orchestration.
- Microsoft.Extensions.AI
- Semantic Kernel basics
- Agents in Semantic Kernel
Module 12 — RAG 4 lessons · 3 sessions
Embeddings, chunking, vector databases, similarity search, retrieval, context injection, RAG architecture in .NET and agent + RAG. Mini project: company policy assistant.
- Embeddings and chunking
- Vector databases and similarity search
- Building RAG in .NET
- Mini project: company policy assistant
Module 13 — Evaluating AI Applications 3 lessons · 1 session
Why unit tests are not enough: correctness, relevance, groundedness, hallucination, LLM-as-a-judge, RAG evaluation and regression testing for AI.
- Why AI apps need evaluation
- Measuring answer quality
- Evaluating RAG systematically
Module 14 — Agentic Workflow Patterns 3 lessons · 1 session
Router, intent classifier, parallel, aggregator and supervisor patterns for multi-agent systems.
- Routing and intent classification
- Parallel and aggregator patterns
- The supervisor pattern
Module 15 — MCP (Model Context Protocol) 4 lessons · 2 sessions
Why MCP, its architecture, clients and servers, tools, resources and prompts; building an MCP server and connecting an app to it. Mini project: MCP-powered employee assistant.
- Why MCP
- MCP clients and servers
- Building and connecting an MCP server
- Mini project: MCP-powered employee assistant
Module 16 — Local & Open Models 2 lessons · 1 session
Ollama, running open-source models locally, connecting .NET to local models, privacy and when local models make sense.
- Running models locally with Ollama
- Open-source vs cloud models
Module 17 — Production-Ready GenAI with .NET 4 lessons · 2 sessions
AI service layer, logging, rate limiting and caching; API-key and secrets security, prompt injection, PII and responsible AI; latency, streaming and cost optimisation.
- Architecture of an AI service
- Security and responsible AI
- Performance
- Cost
Module 18 — Bonus Track: Azure AI Fundamentals 4 lessons · 2 sessions
Azure AI services and AI Foundry, Azure OpenAI, agents on Azure, Azure AI Search and RAG, language, vision and Document Intelligence, responsible AI and exam-oriented practice.
- Azure AI and Azure AI Foundry
- Agents, search and RAG on Azure
- Language, vision and documents
- ML basics, responsible AI and exam practice
Module 19 — Capstone: Enterprise AI Employee Assistant 3 lessons · 2 sessions
An end-to-end GenAI .NET project bringing together LLM integration, prompts, structured output, chat history, tools, agents, RAG, evaluation, security, cost and production architecture.
- Capstone: plan the Enterprise AI Employee Assistant
- Capstone: build with RAG and evaluation
- Capstone: production architecture and demo
Cancellation & refund policy
Refund Policy
- If a cancellation request is received 7 days or more before the batch start date, 25% of the course fee will be deducted as cancellation charges and the remaining amount will be refunded.
- If a cancellation request is received between 3 and 6 days before the batch start date, 50% of the course fee will be deducted as cancellation charges and the remaining amount will be refunded.
- If a cancellation request is received less than 3 days before the batch start date, no refund will be applicable.
- No refund will be provided once the batch has started.
- If the batch is cancelled by ConfidentHire, the full course fee paid by the student will be refunded.
- Course fees are non-transferable to another student or batch.
- Approved refunds will be processed within 7–10 working days.
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