Cetrification Courses

Build real, tool-using AI agents with the OpenAI Agents SDK — from design to triage, handoffs, guardrails, and eval.

This course teaches you how to design, build, and evaluate real AI agents using the OpenAI Agents SDK. You’ll learn how to give models tools, memory, policy boundaries, and the ability to route work — so they can solve real problems like customer support, operations, and workflow automation.

Instead of slideware, we build a full end-to-end system together: an airline customer support assistant that can route requests, answer policy questions, change seats, handle cancellations and reschedules, respect business rules, and escalate when needed. You’ll also learn how to test it, monitor it, and plug it into a simple UI.

What you’ll learn

• The difference between traditional automation, LLM “assistants,” and true agentic systems — and when you should use which.
• How to decide if your use case actually needs an agent (or if a single LLM call is enough).
• How the Agent Loop works (Observe → Think/Plan → Act → Reflect) and how to force the model to operate step-by-step instead of hallucinating.
• How to expose real tools (Python functions / APIs) to the model so it can look up data, take actions, and update systems — safely.
• How to build multi-agent systems with triage and handoffs (for example: FAQ agent, seat booking agent, cancellation and rebooking agent).
• How to attach guardrails for policy, confirmation, and safety.
• How to persist context and memory across turns.
• How to evaluate the agent’s behavior automatically — routing, tool usage, escalation, and confirmation gates — using a lightweight JSONL eval harness.
• How to run your agent against both OpenAI models and OpenAI-compatible local models (e.g. self-hosted / vLLM / Qwen-style endpoints).
• How to wrap it in a front end (Gradio) so stakeholders can try it like a real support assistant.

Unlock the full potential of generative AI with our comprehensive course on Prompt Engineering—a pivotal skill in the modern landscape of language models. Whether you’re a developer, data scientist, or simply curious about harnessing AI's capabilities, this course is designed to equip you with the strategies and techniques needed to master the art of prompting.

What You’ll Learn:

• What is Prompt Engineering & Why we need Prompt Engineering
• Prompt Engineering Strategy for Large Language Models
• Prompt Engineering Techniques for Large Language Models
• Build Chatbot using Prompt Engineering
• Build Prompt Enginnering based application using OpenAI models
• In context learning using Prompt Engineering
• Zero-Shot , One-Shot, Few-Shot Prompt Engineering Techniques
• Understanding Chain-Of-Thought
• How to enable reasoning capability of Language Models

Unlock the Power of Generative AI with Retrieval-Augmented Generation (RAG)!

In today’s rapidly evolving AI landscape, traditional language models—no matter how large—face a common limitation: they are bound by the static nature of their training data. As the world changes and new knowledge is created every day, relying solely on pre-trained models can lead to outdated or incomplete answers.

That’s where Retrieval-Augmented Generation (RAG) comes in.

• "Why": Why do we even need RAG, and what unique challenges does it address in the world of Generative AI?
• "What": What exactly is Retrieval-Augmented Generation? I’ll break down its key components and functionality.
• "How": How to implement RAG in real-world applications.
• Hands-on: Implement Real World Use Cases using RAG

We’ll guide you through two real-world RAG implementations that you can apply and extend in your own projects:

• LiveStockIQ – A stock market assistant that integrates with real-time financial APIs to provide current stock data, company info, and market trends. You’ll see how retrieval connects to APIs and how LLMs generate insights on top of it.
• SmartRecruit – An AI-powered recruitment assistant for HR teams that intelligently analyzes resumes and matches them to job descriptions using contextual document retrieval and summarization.