Join 50,000+ learners from 130+ countries in Ready Tensor’s self-paced, beginner-friendly certification focused on the foundations of agentic AI. In this program, you’ll design and build your first RAG-powered AI assistant, learning how modern AI systems retrieve knowledge, reason with prompts, and maintain memory. Complete the project to earn the RAG Systems Expert micro-certificate and a shareable digital badge.
Program Overview
Most people stop at prompting. Real AI systems go further.
Agentic AI for Beginners is designed to help you cross the gap from chatting with LLMs to building real AI applications. You’ll learn how modern assistants actually work under the hood — combining prompts, retrieval, memory, and structured pipelines — using the same patterns employed in production systems.
This program gives you a strong technical foundation you can build on, whether your goal is advanced agent architectures, multi-agent systems, or production deployment later.
Your proof isn’t just theory — it’s a working RAG application you can show publicly.
What You’ll Learn
This program focuses on the foundations of agentic AI, giving you the core concepts and practical skills needed to build real AI assistants powered by retrieval, reasoning, and memory.
Topics Covered
You’ll learn the fundamental building blocks behind modern AI assistants. The program covers how agentic AI works, how prompts and reasoning shape behavior, and how retrieval and memory enable AI systems to use external knowledge.
Topics include agentic AI fundamentals, modular prompting, reasoning techniques, embeddings, vector databases, semantic search, memory management, and retrieval-augmented generation (RAG).
Tools & Frameworks
You’ll work with modern tools used to build practical LLM applications and AI assistants.
These include frameworks for making LLM calls, managing prompts and memory, implementing retrieval pipelines, and building interactive chat-based AI systems.
Learning Outcomes
By the end of this program, you’ll be able to:
✅ Understand agentic AI fundamentals
✅ Design modular prompts
✅ Apply reasoning techniques such as Chain-of-Thought and ReAct
✅ Implement and manage memory in LLM conversations
✅ Understand word and document embeddings
✅ Implement vector databases and semantic search
✅ Build RAG pipelines
✅ Build chatbots and AI assistants
What You’ll Build
This program centers on a single, hands-on project designed to solidify core concepts.
Project: Your First RAG-Powered AI Assistant
You’ll build a question-answering assistant that can respond based on a custom document set rather than just its training data.
Your system will include:
- Document ingestion and chunking
- Embedding generation and vector storage (e.g., FAISS or Chroma)
- Semantic retrieval based on user queries
- Prompt construction that injects retrieved context
- LLM-generated responses grounded in your data
- Optional enhancements like session memory or basic observability
The final result is a working AI application, not a demo notebook.
What You’ll Earn
Completing this program earns you verified, shareable credentials that demonstrate real skills.
RAG Systems Expert Micro-Certificate
A credential recognizing your ability to design and build retrieval-augmented AI systems.
Digital Badge
A verifiable badge you can showcase on LinkedIn and professional platforms.
Portfolio Project
Your completed RAG assistant published on Ready Tensor with documentation and linked code — visible to employers and peers.
Who This Program Is For
This program is ideal if you’re new to agentic AI but ready to move beyond surface-level usage.
Ideal for:
- Software engineers new to LLM-based systems
- Data scientists transitioning into AI application development
- Students exploring applied AI and modern NLP systems
- Product-minded builders who want to understand how AI assistants really work
- Developers preparing for advanced agentic or multi-agent systems
⚠️ Note: This is a code-driven program.
Program Prerequisites
You don’t need prior experience with agent frameworks — but you should be comfortable coding.
Required Skills:
- Intermediate Python (functions, classes, basic modules)
- Familiarity with the command line
- Basic experience using GitHub
- Understanding of APIs and HTTP requests
- Comfort interacting with chat-based AI tools
Recommended (but not required):
- Basic AI/ML concepts (embeddings, inference)
- Exposure to LangChain or similar libraries
- Familiarity with vector databases
- Prior experience building small Python applications
How the Program Works
This is a project-based, self-paced certification program designed to take you from foundational concepts to a working agentic AI application.
You’ll progress through structured lessons that introduce core ideas step by step, paired with hands-on code examples and a guided project that brings everything together.
What the Program Includes
-
Lessons
Clear, beginner-friendly lessons covering the foundations of agentic AI — including prompts, reasoning, memory, retrieval, and RAG system design. Concepts are introduced incrementally, with an emphasis on why things work, not just how. -
Code Repositories
End-to-end reference implementations written in clean, readable Python. These examples show how real agentic AI components are wired together and serve as a starting point for your own project. -
Videos
Walkthroughs and explanations that reinforce key ideas, demonstrate implementation details, and help you connect concepts to practical system design. -
Quizzes
Short, optional quizzes to help you check your understanding as you progress.
These are self-assessments only and do not affect certification. -
Capstone Project
The core of the program. You’ll build a RAG-powered AI assistant that uses custom documents, vector retrieval, and structured prompts.- Submit your project as a Ready Tensor publication
- Receive rubric-based expert reviewer feedback
- Revise and resubmit if needed
- A minimum score of 70% is required to earn certification
Pace and Timeline
The program is fully self-paced. You can move through lessons, code, and project work according to your own schedule and submit your project whenever you’re ready.
Learn From Industry Experts
This program is designed and taught by practitioners who build and evaluate real agentic AI systems, not just demos.
Abhyuday (Abu) Desai, Ph.D. — Founder & CEO, Ready Tensor
With over 20 years of experience in AI, data science, and analytics, Abu has led AI initiatives across Fortune 500 companies and now focuses on building and researching agentic AI systems at Ready Tensor.
The foundations taught in this program reflect how modern AI systems are actually built — including real design tradeoffs around prompting, retrieval, memory, and system reliability.
Program Team
The program is supported by a team of AI engineers and technical educators who contribute to lesson design, tools and templates, project assessments, and learner support.
Together, the instructors and team ensure the material is:
- Technically sound
- Beginner-accessible
- Grounded in real-world AI engineering practice
Our goal is to set you up for success when conducting real-world agentic AI projects in industry.
Start Building Real AI Systems
Modern AI applications are no longer just single prompts or chatbots.
They are systems — combining prompts, reasoning, memory, tools, and retrieval to solve real problems.
This program gives you:
- A clear mental model of how agentic AI systems work
- Hands-on experience building a RAG-powered AI assistant
- Practical skills you can build on in advanced agent and production programs
- A portfolio-ready project that proves you can go beyond prompt tinkering
Enroll today and start your journey into building real agentic AI systems — from the ground up.