AI Technical Practitioner Program Overview

Web Search: Internet Access Patterns and Best Practices The AI Technical Practitioner Program is designed for technical professionals who want to build practical skills in applying OpenAI technologies for real-world business and enterprise scenarios. The program covers core AI techniques, prompt engineering, RAG, embeddings, responsible AI, governance, and practical AI implementation considerations.

Participants will explore OpenAI product capabilities including ChatGPT, Deep Research, Advanced Data Analysis, Canvas, connectors, administration, OpenAI Platform fundamentals, API models, Responses API tools, web search, file search, vector stores, evals, and optimization strategies.

The course also includes Model Context Protocol, API solution patterns, and a hands-on lab for Building CustomGPT with Actions.

After completing this AI Technical Practitioner Program, students will be able to:

  • 1. Understand OpenAI technical ecosystem and its role in building AI-powered applications.
  • 2. Apply AI techniques including prompt engineering, RAG, embeddings, and responsible AI.
  • 3. Work with OpenAI Platform fundamentals, API models, and Responses API tools
  • 4. Use ChatGPT capabilities such as Deep Research, Advanced Data Analysis, Canvas, memory, and connectors.
  • 5. Understand Model Context Protocol
  • 6. Design AI workflows and integrations
  • 7. Evaluate solution patterns
  • 8. Build and configure a Custom GPT with Actions

Upcoming Batches

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Key Features of AI Technical Practitioner Course

  • AI Techniques & Prompt Engineering: Master modern AI techniques, prompting, RAG, embeddings, and vector databases

  • OpenAI Product Deep Dive: Explore ChatGPT, Deep Research, Advanced Data Analysis, Canvas, and Connectors

  • OpenAI API Platform:Learn API models, Responses API, retrieval patterns, evals, and optimization strategies

  • Model Context Protocol (MCP): Understand MCP architecture, integrations, security, and governance considerations

  • Enterprise AI Solution Patterns: Discover proven approaches for assistants, knowledge systems, content generation, and moderation solutions

  • Hands-On AI Implementation: Build a Custom GPT with Actions using real-world integration scenarios.

  • Responsible AI & Governance: Implement AI safely with governance, security, and responsible AI practices

  • Built for Technical Practitioners: Designed for architects, developers, AI engineers, and technical consultants.

Who Should Attend

  • Solution architects
  • AI engineers
  • Developers
  • Technical consultants
  • Software engineers

Prerequisites

  • Basic understanding of software development concepts
  • Familiarity with web applications and APIs
  • Basic programming knowledge is recommended
  • Interest in learning AI technologies
  • Access to ChatGPT and OpenAI Platform account

Why choose CloudThat as your training partner?

  • Authorized OpenAI SMB Training Partner: Learn through programs aligned with OpenAI technologies, best practices, and enterprise adoption frameworks
  • Industry-Experienced AI Experts: Gain insights from practitioners actively delivering AI solutions across customers, industries, and business functions
  • Hands-On Learning Approach: Build practical skills through guided labs, real-world scenarios, demos, and implementation-focused exercises
  • Enterprise-Focused Curriculum: Learn governance, security, architecture, and operational considerations required for successful AI deployments
  • Proven Technical Expertise: Benefit from CloudThat’s extensive experience in cloud, data, AI, and modern application technologies
  • Real-World Solution Patterns: Explore proven approaches for AI assistants, automation, integrations, knowledge systems, and business workflows
  • Learning Pathways: Progress from practitioner-level skills to advanced AI architecture, implementation, and enterprise solution design programs.

Course modules Download Course Outline

Module 1: Introduction to AI Part-1

  • Introduction to AI
  • AI Fundamentals
  • Machine Learning Basics
  • Deep Learning Overview
  • Generative AI Introduction
  • Foundation Models
  • Large Language Models (LLMs)
  • AI Applications and Use Cases

Module 2: Intro to AI Techniques – Part 2

  • Prompt Engineering Fundamentals
  • Prompt Design Techniques
  • Zero-Shot Prompting
  • One-Shot Prompting
  • Few-Shot Prompting
  • Chain-of-Thought Prompting
  • Prompt Evaluation
  • Prompt Optimization

Introduction to AI Techniques Pt-3

  • Retrieval-Augmented Generation (RAG)
  • Embeddings
  • Vector Databases
  • Fine-Tuning Concepts
  • AI Safety
  • Responsible AI
  • AI Governance
  • Practical AI Implementation Considerations

ChatGPT Deep Dive

  • ChatGPT Deep Dive
  • Deep Research
  • Advanced Data Analysis
  • Memory in ChatGPT
  • Canvas & Workflows
  • Administration
  • Positioning Use Cases
  • Connectors & Advanced Capabilities
  • Competitive Positioning
  • Running a Demo: Preset
  • Running a Demo: Present
  • Running a Demo: Putting it Together

API Platform – DeepDive Part 1

  • What is an API?
  • OpenAI Platform Fundamentals
  • API Models
  • Platform Core Concepts

API Platform DeepDive Part 2

  • Tools in the Responses API: What They Are and How They Run
  • Connectors and MCP: Extending Models with Remote Tools Safely
  • Web Search: Internet Access Patterns and Best Practices
  • File Search and Retrieval Patterns
  • Vector Stores and Knowledge Grounding
  • Vector Stores and Knowledge Grounding
  • Optimization Strategies
  • Prompting vs Retrieval vs Fine-Tuning

Model Context Protocol

  • Introduction to Model Context Protocol (MCP)
  • How it Works?
  • Core Primitives
  • Protocol-Level Primitives
  • MCP Architecture
  • Limitations and Risks
  • Security Considerations
  • Enterprise Governance

OpenAI Solution Part-1

  • The Value Framework
  • AI Maturity Curve
  • Adoption, Lifecycle Progression, Culture & Enablement
  • Technical Depth Considerations

OpenAI Solution Part-2

  • Customer Service Assistant
  • Knowledge Assistants
  • Digital Assistants
  • Content Generation Solutions
  • Moderation Solutions
  • Build vs Buy
  • Proven API Solution Patterns and Real-World Use Cases

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Course ID: 29985

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FAQs:

The AI Technical Practitioner Program is designed to help technical professionals build practical skills in AI techniques, OpenAI products, APIs, MCP, enterprise solution patterns, and AI-powered application development.

This program is ideal for solution architects, AI engineers, developers, software engineers, and technical consultants looking to implement AI solutions in real-world environments

Basic programming knowledge is recommended as the course covers APIs, integrations, RAG concepts, MCP, and hands-on implementation activities.

Yes. Participants learn OpenAI Platform fundamentals, API models, Responses API tools, retrieval patterns, vector stores, and optimization techniques.

Yes. The program covers RAG, embeddings, vector databases, file search, knowledge grounding, and practical implementation considerations.

Yes. The curriculum includes MCP architecture, core primitives, protocol-level concepts, security considerations, risks, and enterprise governance.

Yes. Participants will build a Custom GPT with Actions, configure OpenAPI schemas, implement integrations, and validate end-to-end workflows.

The skills gained in this program are relevant for AI Engineer, Solution Architect, AI Developer, Technical Consultant, Application Engineer, and AI Implementation Specialist roles.

Salaries vary by role, experience, geography, and organization. AI engineers, solution architects, and AI consultants are among the most sought-after professionals in the technology industry, with strong career growth opportunities globally.

Foundational courses focus on AI literacy and basic concepts, whereas this program emphasizes APIs, AI implementation, solution architecture, MCP, enterprise use cases, and hands-on development.

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