Gen AI and Agentic AI

The Generative AI + Agentic AI Engineering Mastery curriculum is a structured 14-module learning program that guides learners from AI fundamentals to real-world implementation. Covering prompt engineering, Python, APIs, RAG, AI agents, frameworks, deployment, operations, and a capstone project, it builds practical skills, engineering discipline, and career-ready expertise for designing, deploying, and managing modern AI solutions confidently.

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Course Overview

This 14-module curriculum provides a structured journey through Generative AI and Agentic AI Engineering, covering AI foundations, prompt engineering, Python, APIs, LLM applications, embeddings, RAG, AI agents, frameworks, deployment, LLMOps, automation, and a capstone project. Learners develop practical implementation skills, engineering discipline, and the confidence to build, deploy, evaluate, and present modern AI solutions.

Course Curriculum

This curriculum is designed to move learners from AI awareness to implementation readiness in a controlled sequence: foundation, prompt discipline, Python/API readiness, LLM application development, embeddings, RAG, agents, frameworks, deployment, operations, automation, and capstone readiness.

Disciplined 14-Module Course Curriculum

A concise, module-wise curriculum derived from the RBCloudGenX executive course structure.
This version removes project noise and focuses only on the course content that must be covered
in Part I and Part II.

Curriculum Purpose

Topic	Description
Part I – Foundations to RAG and Agents	M1: AI & GenAI Foundations M2: Prompt Engineering M3: Python & API Foundation M4: LLM Application Development M5: Embeddings & Vector Databases M6: RAG Engineering M7: Agentic AI Fundamentals
Part II – Frameworks, Ops, and Capstone	M8: Tool Calling & Agent Tools M9: AI Frameworks M10: MCP & Multi-Agent Systems M11: AI Engineering & Deployment M12: LLMOps, GenAIOps & AIOps M13: No-Code AI Automation M14: Capstone Portfolio Project
Learning Ladder	L0: Awareness → L1: Prompt → L2: Build → L3: RAG → L4: Agent → L5: Operations (Ops)
Teaching Discipline	Every module includes concept explanation, key terminology, architecture overview, instructor demonstration, guided practice, common risks and mistakes, and clearly defined learner outcomes.

Course Delivery Standard

Topic	Description
Audience	Students, fresh graduates, IT professionals, data engineers, cloud engineers, QA engineers, software developers, and career changers seeking practical AI engineering skills.
Teaching Style	Begin with clear concepts, followed by key terminology, architecture overview, instructor demonstrations, guided practice, common mistakes, and measurable learning outcomes.
Depth Level	Progresses from foundational concepts to practitioner-level implementation, focusing on practical application rather than unnecessary research depth.
Tools	Python, Jupyter Notebooks, LLM APIs, vector databases, RAG tools, agent frameworks, deployment utilities, tracing and evaluation tools, and selected no-code AI automation platforms.
Exclusions	Avoid overwhelming learners with too many disconnected projects, excessive tool usage, or unnecessary theoretical concepts that do not directly support practical AI implementation.

Curriculum Discipline and Learning Sequence

This page defines how the 14 modules should be delivered. The goal is structure, clarity, and execution readiness.

Standard Coverage Pattern for Every Module

Teaching Component	Description
1. Concept	Explain what the topic means in plain English before introducing technical concepts or implementation details.
2. Terminology	Define only the essential terms learners need to communicate confidently and work effectively with AI technologies.
3. Architecture View	Show where the topic fits within an AI system, including input, processing, models, data, tools, outputs, and monitoring.
4. Instructor Demo	Demonstrate one clear, end-to-end workflow that directly supports the learning objective of the module.
5. Controlled Practice	Allow learners to repeat or modify the demonstrated workflow through guided, hands-on practice.
6. Common Mistakes	Highlight the most common errors learners make and explain practical strategies to prevent or resolve them.
7. Learner Outcome	Conclude with a measurable capability statement describing what the learner should be able to accomplish after completing the module.

High-Level Outcome by Phase

Learning Phase	Description
Phase 1 – Foundation	Learners understand AI terminology, prompt structure, Python fundamentals, API basics, and the core mental model behind Generative AI systems.
Phase 2 – LLM Build	Learners understand how LLM applications are developed, how embeddings represent knowledge, and how RAG connects language models to trusted data sources.
Phase 3 – Agentic Systems	Learners understand AI agents, tool integration, frameworks, Model Context Protocol (MCP), multi-agent architectures, operational boundaries, and workflow orchestration.
Phase 4 – Delivery	Learners understand AI deployment, LLMOps, AIOps, no-code automation, production best practices, and portfolio presentation for career readiness.

Recommended Flow

Do not start with tools. Start with mental models. Then move to prompts, APIs, data, retrieval, agents, frameworks, deployment, and operations. This prevents students from memorizing tools without understanding the system.

Part I - Foundations to RAG and Agents

Modules 1 and 2 establish AI literacy and prompt discipline. These two modules must be simple, strong, and confidence-building.

M1. AI & Generative AI Foundations

Goal: Build clean understanding of modern AI, GenAI, LLMs, AI agents, risks, and practical business relevance.

AI vs ML vs deep learning vs GenAI.
LLMs, tokens, context window, model behavior, hallucination.
GenAI use cases: text, code, documents, search, assistants.
AI agent meaning and how it differs from a chatbot.
Responsible AI basics: accuracy, privacy, bias, safety, misuse.
Where GenAI fits in enterprise workflows.

Outcome: Learners can explain GenAI clearly, identify realistic use cases, and speak the language of modern AI without confusion.

M2. Prompt Engineering

Goal: Teach learners how to communicate with LLMs in a structured, repeatable, and testable way.

Prompt anatomy: role, task, context, constraints, examples, output format
Zero-shot, few-shot, chain-of-thought style prompting without exposing private reasoning
Prompt refinement, prompt debugging, and prompt comparison
Output control: tables, JSON, summaries, bullets, structured formats
Prompt safety: avoiding vague, biased, or over-trusting prompts
Reusable prompt templates for business and technical tasks

Outcome: Learners can design prompts that are clear, structured, reusable, and suitable for business workflows.

Instructor Discipline for Modules 1-2

Section	Details
Area Coverage Required	Must cover AI vocabulary, Generative AI behavior, hallucinations, prompt structure, output control, and prompt iteration techniques.
Must Avoid	Do not begin with complex framework names. Avoid overwhelming learners with excessive AI models or advanced research terminology.
Completion Check	Learners should be able to explain Generative AI concepts and create a structured prompt for a business task with clear output expectations.

M3. Python & API Foundation

Goal: Provide the minimum Python and API foundation required to build LLM-based applications.

Python environment setup: notebooks, VS Code, packages, virtual environments
Python basics needed for AI apps: variables, functions, lists, dictionaries, loops
Files, JSON, environment variables, API keys, and configuration
REST API basics: request, response, headers, status codes, payloads
Working with Python libraries and reading documentation
Error handling, logging basics, and clean notebook discipline

Outcome: Learners can run Python notebooks, call APIs, handle JSON, and prepare for LLM application development.

M4. LLM Application Development

Goal: Move from prompting in a UI to building simple LLM-powered applications through APIs.

LLM API request/response workflow
Model selection basics: capability, cost, latency, context length
Chat completion patterns and structured outputs
Prompt templates, system instructions, and application-level context
Input validation, response parsing, and error handling
Building a simple LLM assistant flow from user input to model output

Outcome: Learners can build a basic LLM application flow using API calls, templates, and structured output handling.

Instructor Discipline for Modules 3-4

Topic	Description
Area Coverage Required	Environment setup, Python essentials, JSON, REST APIs, API keys, request/response flow, prompt templates, and structured outputs.
Must Avoid	Do not turn this into a full Python course. Focus only on the Python concepts required for AI application development.
Completion Check	Learners can run a notebook, call an LLM API, send input, parse responses, and handle basic API errors confidently.

M5. Embeddings & Vector Databases

Goal: Teach how AI systems represent meaning and retrieve relevant information using semantic search.

What embeddings are and why they matter
Similarity search: semantic matching vs keyword search
Chunking principles: size, overlap, boundaries, and meaning
Vector indexes and vector database concepts
Metadata design for retrieval filtering
Common retrieval mistakes: poor chunks, weak metadata, duplicate content, stale indexes

Outcome: Learners understand how documents become searchable knowledge for RAG and AI assistants.

M6. RAG Engineering

Goal: Teach how to connect LLMs with trusted knowledge so responses are grounded and explainable.

RAG architecture: ingestion, chunking, embedding, indexing, retrieval, generation
Document preparation and knowledge-base design
Retriever design: top-k, similarity threshold, filters, reranking basics
Grounded answering with citations and source references
RAG evaluation: relevance, faithfulness, completeness, and failure analysis
RAG risks: hallucination, stale data, irrelevant retrieval, over-trust

Outcome: Learners can explain and design a grounded RAG workflow from document ingestion to final answer.

M7. Agentic AI Fundamentals

Goal: Introduce agentic thinking: AI systems that can reason over a task, use tools, and execute workflows under control.

Agent vs chatbot vs workflow automation
Core agent components: model, instructions, tools, memory, state, planner, executor
Planning and task decomposition
Human-in-the-loop and approval checkpoints
Agent boundaries: what agents should not do automatically
Failure modes: loops, wrong tool use, over-execution, unsafe action

Outcome: Learners can describe agentic system design and identify where agents add value beyond simple chat.

Instructor Discipline for Modules 5-7

Topic	Description
Area Coverage Required	Embeddings, chunking, indexing, vector search, Retrieval-Augmented Generation (RAG) architecture, grounding, citations, agent components, planning, tools, and operational boundaries.
Must Avoid	Do not present RAG as a simple document upload process. Avoid portraying AI agents as fully autonomous or magical automation without planning, tools, and defined constraints.
Completion Check	Learners can explain how knowledge retrieval enables grounded, evidence-based responses and clearly distinguish AI agents from traditional chatbots.

Part II - Frameworks, Ops, and Capstone

M8. Tool Calling & Agent Tools

Goal: Teach how models interact with external tools, APIs, databases, files, and functions.

Function/tool calling concepts and request lifecycle
Designing tool schemas: name, description, parameters, validation
Common tools: search, calculator, database lookup, document reader, API action
Tool orchestration and deciding when a tool is needed
Tool result handling and final answer synthesis
Security controls: permissions, validation, audit, human approval

Outcome: Learners can design safe tool-enabled AI workflows and understand how agents connect to external systems.

M9. AI Frameworks

Goal: Give learners a structured understanding of major AI engineering frameworks and when to use them.

Framework purpose: abstraction, orchestration, retrieval, agents, tracing
LangChain concepts: chains, tools, retrievers, memory patterns
LangGraph concepts: state, nodes, edges, workflow control
LlamaIndex concepts: data connectors, indexes, query engines
OpenAI Agents SDK concepts: agents, tools, handoffs, tracing
Framework selection: when to use plain API vs framework

Outcome: Learners can compare frameworks and select the right engineering approach instead of blindly using tools.

M10. MCP & Multi-Agent Systems

Goal: Teach context-sharing and multi-agent coordination patterns for complex AI workflows.

MCP purpose: connecting models to tools, data, and context through standard interfaces
Client, server, tools, resources, prompts, and context boundaries
Multi-agent roles: specialist, supervisor, reviewer, router, executor
Handoffs, routing, delegation, and result consolidation
Coordination risks: conflicting outputs, duplication, loops, cost explosion
Governance: observability, permissions, and human review

Outcome: Learners understand how context protocols and multi-agent patterns support scalable AI workflows.

Instructor Discipline for Modules 8-10

Topic	Description
Area Coverage Required	Tool schemas, tool result handling, framework purpose, LangChain, LangGraph, LlamaIndex, OpenAI Agents SDK, Model Context Protocol (MCP), and multi-agent design patterns.
Must Avoid	Do not teach frameworks as brand names alone. Explain when to use a plain API, a RAG framework, an agent framework, or graph-based orchestration for different AI application scenarios.
Completion Check	Learners can choose the most appropriate framework pattern and explain how to design a secure, reliable, and tool-enabled AI workflow.

M11. AI Engineering & Deployment

Goal: Teach the engineering discipline required to move from a notebook/demo to a usable application.

Application structure: frontend, backend, services, configuration, secrets
Streamlit for quick AI application interfaces
FastAPI for API-based AI services
Packaging code, dependency management, and environment files
Docker basics for repeatable execution
Deployment thinking: local, cloud, authentication, logging, scaling basics

Outcome: Learners can explain the path from prototype to deployable AI application with clean engineering structure.

M12. LLMOps, GenAIOps & AIOps

Goal: Introduce operational discipline for evaluating, monitoring, improving, and governing AI systems.

LLMOps lifecycle: prompts, datasets, evaluations, releases, monitoring
Tracing model calls, tool calls, latency, cost, and errors
Evaluation types: exactness, relevance, faithfulness, safety, user satisfaction
Guardrails: input checks, output checks, policy checks, escalation
Feedback loops and continuous improvement
AIOps overview: using AI for operational detection, summarization, triage, and automation

Outcome: Learners can apply operational thinking to make AI systems measurable, reviewable, and improvable.

M13. No-Code AI Automation

Goal: Show how non-developers and business teams can use AI automation responsibly for productivity workflows.

No-code and low-code automation concepts
Trigger-action workflows and AI-assisted steps
Document, email, form, spreadsheet, and notification automation patterns
When no-code is appropriate vs when custom engineering is required
Controls: approval steps, audit trails, access, privacy, and rollback
Productivity use cases for operations, support, analytics, and training

Outcome: Learners can identify useful no-code AI automation opportunities and understand their limitations.

Instructor Discipline for Modules 11-13

Topic	Description
Area Coverage Required	Streamlit, FastAPI, Docker, application packaging, deployment patterns, evaluation, tracing, monitoring, guardrails, user feedback, and no-code automation tools.
Must Avoid	Do not present a notebook as a production application. Ensure learners understand the importance of evaluation, logging, security, monitoring, and operational risk management.
Completion Check	Learners can explain the key requirements for transforming an AI prototype into a deployable, scalable, secure, and monitorable production solution.

M14. Capstone Portfolio Project

Goal: Bring the full learning path together into one polished, explainable, career-ready solution.

Problem statement and business context
Architecture: UI, LLM, retrieval, tools, data, evaluation, deployment plan
Implementation plan: milestones, components, risks, and validation
Evaluation: test cases, expected outputs, quality checks, limitations
Portfolio packaging: README, architecture diagram, demo script, resume bullets
Final presentation: problem, solution, architecture, demo, lessons learned

Outcome: Learners can present a complete AI solution with architecture clarity, implementation confidence, and portfolio value.

Final Curriculum Checklist

Topic	Description
Core Concepts	AI, Generative AI, LLMs, prompts, tokens, hallucinations, embeddings, vector search, Retrieval-Augmented Generation (RAG), AI agents, tools, frameworks, Model Context Protocol (MCP), deployment, and AI operations.
Core Skills	Prompt design, API integration, Python fundamentals, structured outputs, retrieval design, agent design, framework selection, and evaluation thinking for real-world AI applications.
Engineering Mindset	Design before coding, validate before trusting, monitor before scaling, and explain solutions clearly before presenting them.
Career Outcome	Learners can confidently discuss AI systems, demonstrate implementation awareness, and present a complete AI solution with technical clarity.
Instructor Rule	Keep every session aligned with the module outcome. Avoid unnecessary tool overload, excessive projects, and theoretical concepts that do not directly support practical learning.

End State

At the end of the 14 modules, the learner should not merely know AI terminology. The learner should understand how modern AI systems are designed, built, retrieved, orchestrated, evaluated, deployed, monitored, and explained.

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Prerequisites

This program is designed for beginners as well as professionals who want to build practical AI engineering skills. While no prior AI experience is required, learners will benefit from having:

Basic computer and internet navigation skills
Familiarity with using web applications and online tools
Basic logical and problem-solving abilities
A willingness to learn Python fundamentals (covered during the course)
A laptop or desktop with a stable internet connection
Basic understanding of English technical terminology

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