Skip to main content
SKODOR Learning Academy
Beginner to AdvancedCutting EdgePractical / Project-Based

Applied Generative AI & Agentic AI

Engineer advanced LLM architectures, multi-agent autonomous systems, RAG pipelines, and enterprise-ready agentic software.

Explore Syllabus ↓
4.9/ 5 Rating★★★★★3,000+ Reviews
Applied Generative AI & Agentic AI system overview
Duration

3–4 Months

Internship

3 Months Practical

Capstone Delivery

Verified Projects

Modules

5 Core Modules

Engineered for High Technical Competence

Applied Generative AI & Agentic AI is an intensive, engineering-centric program designed for developers and AI practitioners. You will advance beyond basic prompt engineering to build sophisticated LLM systems, multimodal architectures, advanced RAG implementations, and autonomous multi-agent networks powered by LangGraph and the Model Context Protocol (MCP).

What You Will Master

  • Master the mechanics of Transformers, context windows, tokenization, and structured JSON outputs
  • Architect production Retrieval-Augmented Generation (RAG) with vector databases and rerankers
  • Build tool-using, reasoning AI agents capable of planning, memory management, and error recovery
  • Orchestrate collaborative multi-agent systems using LangGraph and state machine paradigms
  • Implement the Model Context Protocol (MCP) for seamless tool and enterprise resource integration
  • Deploy, optimize, and monitor scalable AI applications using FastAPI, Docker, and guardrail frameworks

Detailed Course Syllabus

All topics and modules are extracted directly from the official course specification.

5 Detailed Syllabus Modules

|
01Introduction to Generative AI & Enterprise Use Cases
02AI, Machine Learning, Deep Learning & Generative AI Taxonomy
03Large Language Models (LLMs) Architecture: Tokens, Parameters & Attention
04Transformer Architecture Foundations & How LLMs Predict Next Tokens
05Commercial & Open-Source LLM Landscape (OpenAI, Gemini, Claude, Llama)
06Working with LLM APIs & SDKs in Python
07Advanced Prompt Engineering: System, User & Developer Prompts
08Structured Outputs & Strict JSON Schema Enforcement
09Addressing Hallucinations, Model Boundaries & Responsible AI Practices

Tools & Technologies Taught

Directly aligned with production engineering environments.

PythonLanguage
LLM APIs (OpenAI / Anthropic / Gemini)AI & LLM
Hugging FaceAI & LLM
LangChainAI & LLM
LangGraphAI & LLM
LlamaIndexAI & LLM
Vector DatabasesAI & LLM
RAG SystemsAI & LLM
Model Context Protocol (MCP)AI & LLM
FastAPIFramework
DockerDevOps & Tools
Cloud PlatformsCloud
Git & GitHubDevOps & Tools

Capstone Projects

Four commercial-grade engineering projects culminating in a multi-agent autonomous enterprise application.

Enterprise RAG Knowledge Assistant

Sprint Project

High-accuracy retrieval system over multi-modal company documentation with semantic search, reranking, and citation verification.

AI Research Agent

Sprint Project

Autonomous agent executing multi-step web searches, document synthesis, and automated structured report drafting.

AI Business Automation Agent

Sprint Project

Tool-using agent integrated with external REST APIs and relational databases to execute commercial operations autonomously.

Multi-Agent AI Application Capstone

Sprint Project

Orchestrated collaborative multi-agent architecture using LangGraph with specialized roles, human-in-the-loop oversight, and persistent state.

3-Month Practical Internship

Advanced internship building real-world enterprise RAG systems, tool-calling agents, and production multi-agent workflows.

Internship Highlights

  • Structured engineering sprint cadence
  • Peer code reviews and architecture defense
  • Portfolio deployment to production cloud instances

Verified Course Learning Outcomes

Develop commercial-grade Generative AI software using modern LLM APIs and structured output schemas.
Build and optimize advanced RAG pipelines featuring semantic reranking, hybrid search, and automated evaluation.
Architect autonomous, tool-using AI agents capable of planning, executing, and recovering from runtime errors.
Orchestrate complex multi-agent workflows with LangGraph and integrate enterprise systems using MCP.
Deploy scalable, observable, and secured AI microservices to the cloud using FastAPI and Docker.

Course Specific Questions

What programming prerequisites are necessary?

Proficiency in Python and basic software engineering practices (git, APIs) are recommended. The course focuses deeply on application engineering with LLMs and agents.

What agent frameworks are taught?

The course focuses primarily on industry-standard toolkits including LangGraph, LangChain, LlamaIndex, and the Model Context Protocol (MCP).

How does the capstone multi-agent application work?

Learners architect a production multi-agent system where multiple specialized agents (e.g. researcher, analyzer, coder, critic) collaborate to solve multi-step problems autonomously with human oversight.

Earn Your Applied Generative AI & Agentic AI Certification

Every successful graduate receives an industry-recognized certificate demonstrating verifiable competence in real-world systems, architectures, and practical engineering execution.

Official SKODOR Academy Certificate of Excellence
Authenticated SKODOR Digital CredentialSample Certificate

Proof of Practical Engineering Autonomy

Unlike traditional course certificates awarded for passive video watching, SKODOR credentials prove that you built, debugged, and shipped commercial-grade systems.

Verifiable Credential

Includes unique cryptographic credential ID and instant QR code verification for recruiters and hiring managers.

Demonstrated Production Skills

Issued only upon completing all technical modules, code reviews, and defending live capstone deliverables.

Shareable Globally

Seamlessly add your verified SKODOR certificate to LinkedIn, personal engineering portfolios, and resumes.

Verified with immutable credential ID • Globally shareable on LinkedIn

Enroll in Applied Generative AI & Agentic AI

Cohorts have limited seating to maintain high mentor-to-student interaction and rigorous code reviews. Speak with an admissions advisor today.