By completing this course, learners will:

• Understand Semantic Kernel’s core components (skills, planners, memory).

• Write and manage semantic and native functions.

• Chain prompts and APIs into multi-step workflows.

• Integrate AI services with databases, search engines, and APIs.

• Build AI copilots, chatbots, and productivity assistants.

• Deploy applications with Azure, AWS, or local setups.

Course Syllabus

Module 1: Introduction to Semantic Kernel

• What is Semantic Kernel?

• Use cases and architecture overview

• Setting up SK in .NET, Python, or Java

Module 2: Semantic & Native Functions

• Creating semantic functions (prompt templates)

• Writing native functions in code

• Combining semantic and native functions

Module 3: Skills & Orchestration

• Organizing functions into skills

• Chaining multiple skills together

• Context passing and orchestration patterns

Module 4: Memory & Context

• Types of memory in SK (short-term, long-term, embeddings)

• Storing and retrieving contextual data

• Using vector databases for memory (Pinecone, Redis, etc.)

Module 5: Planners & Workflows

• Introduction to SK planners

• Automatic plan generation from goals

• Executing multi-step workflows

Module 6: Connectors & Integrations

• Connecting to APIs and external services

• Database and cloud connectors

• Real-world integration examples

Module 7: Observability & Debugging

• Logging and monitoring AI workflows

• Error handling in SK apps

• Optimizing prompts and plans

Module 8: Real-World Projects

• Building a knowledge assistant with memory

• Automating workflows with planner-driven orchestration

• AI-powered summarizer and content generator

Module 9: Deployment

• Deploying on Azure Functions and AWS Lambda

• Containerizing SK apps with Docker

• CI/CD for AI applications

Module 10: Best Practices

• Designing ethical and safe AI workflows

• Optimizing cost and performance

• Future trends in AI orchestration

Learners will receive a Certificate of Completion from Uplatz, validating their expertise in Semantic Kernel, AI orchestration, and intelligent app development. This certification highlights readiness for roles in AI engineering, software development, and automation design.

Semantic Kernel skills prepare learners for roles such as:

• AI Engineer (Semantic Kernel)

• Software Developer (AI Integration)

• Automation Engineer

• AI Product Engineer

• Conversational AI Developer

With the rise of AI copilots and assistants, SK knowledge is highly relevant for careers in enterprise AI, SaaS platforms, and startups.

1. What is Semantic Kernel?
   An open-source SDK from Microsoft for integrating LLMs with traditional code and workflows, enabling AI orchestration.

2. What are semantic functions in SK?
   Semantic functions are prompt-based templates used to call LLMs for tasks like summarization, Q&A, or text generation.

3. What are native functions in SK?
   Native functions are written in programming languages (e.g., Python/.NET) to handle logic, APIs, or utilities.

4. What is a skill in Semantic Kernel?
   A skill is a collection of semantic and native functions, packaged together for reuse.

5. What are planners in SK?
   Planners automatically generate and execute multi-step workflows to achieve user-defined goals.

6. How does SK handle memory?
   SK supports short-term, long-term, and vector-based memory, enabling context-aware interactions.

7. What is the difference between SK and LangChain?
   Both are AI orchestration frameworks. SK integrates deeply with Microsoft Azure ecosystem, while LangChain is language-agnostic and broader in scope.

8. How does SK integrate with external services?
   Through connectors and native functions, SK can interact with APIs, databases, and cloud services.

9. What programming languages does SK support?
   Currently .NET, Python, and Java, with ongoing community extensions.

10. Where is Semantic Kernel commonly used?
    In enterprise AI copilots, chatbots, task automation, and workflow orchestration.