AI INTEGRATED COURSE

Agentic AI Training

From Prompting to Enterprise AI Automation

Course Overview
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This course teaches how businesses use modern LLM tools such as ChatGPT from OpenAI, Gemini from Google, and Claude from Anthropic for real-world work in Telecom and Manufacturing.

Students start with LLM basics and professional prompting methods. Then they learn about AI software connections, which use APIs to create automated workflow systems. The course teaches enterprise knowledge retrieval, spreadsheet automation, system integration using n8n, and privacy and AI monitoring practices.

After completing the course, students will be knowledgeable about AI for task automation, operational improvement, and business decision-making support.

Skills you’ll learn

Large Language Models in Business (ChatGPT, Gemini, Claude) : Understand how modern AI models are used in real business environments and daily workflows.
Prompt Writing for Professional AI Use : Learn how to write clear, structured prompts to get reliable and useful results from AI tools.
AI Integration with APIs & Automation : Connect AI tools with applications using APIs and simple automation workflows.
Enterprise AI Applications : Use AI for tasks like document analysis, data processing, and workflow automation.

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Lesson 1: The Foundations (LLMs & Professional Prompting)

  • Introduction to enterprise LLM use
  • Overview of ChatGPT by OpenAI
  • Overview of Gemini by Google
  • Overview of Claude by Anthropic
  • Model personality differences (structured, analytical, cautious)
  • Context window comparison and document limits
  • Matching models to Telecom tasks
  • Matching models to Manufacturing tasks

  • How LLMs predict the next word?
  • What tokens are and why they affect cost?
  • Input vs output tokens
  • Context window and memory limits
  • Difference between Web UI and API usage
  • When to use UI vs API in business settings?

  • Why basic prompts give weak results?
  • Moving from simple questions to structured instructions
    • C – Character: Define the AI’s role
    • R – Request: Clearly state the task
    • E – Examples: Provide sample outputs
    • A – Audience: Specify who will read it
    • T – Tone: Set communication style
    • E – Extras: Add constraints and formatting rules

  • What does few-shot prompting mean? (Using 3–5 real examples to guide output)
  • Maintaining brand voice in customer support
  • Standardizing parts-inventory logs
  • Reducing variability in AI responses
  • Hands-on practice with sample datasets

  • What does step-by-step reasoning means
  • When CoT improves accuracy
  • Calculating roaming charges (Telecom case)
  • Diagnosing mechanical faults (Manufacturing case)
  • Structured reasoning vs direct answers
  • Limitations of forcing reasoning

  • Why guardrails are needed in enterprise use
  • Preventing mention of competitors
  • Blocking unapproved discounts or policy violations
  • Controlling tone and compliance language
  • Reducing hallucinations with constraints
  • Building safe, policy-aligned prompts

  • What an API is and how it works as a bridge
  • Connecting company software to AI systems
  • Overview of ChatGPT API by OpenAI
  • Overview of the Gemini API by Google
  • Difference between chat interface and API integration

  • Understanding Temperature (creativity vs accuracy)
  • Understanding Max Tokens and response length
  • Controlling response consistency
  • Managing API costs and efficiency

  • Limits of traditional chatbots
  • Multi-step task automation with AI workflows
  • How do AI agents plan and execute tasks?
  • Examples in Telecom and Manufacturing operations

  • Converting company handbooks into AI instructions
  • What are System Prompts?
  • Creating consistent agent behavior
  • Using system instructions for 24/7 automated operations

  • What is Retrieval-Augmented Generation (RAG)?
  • Connecting AI to internal document libraries
  • Using PDFs like service manuals or telecom tariffs
  • Improving answer accuracy with internal data

  • What does no-code automation mean?
  • Using n8n to connect AI with business tools
  • Integrating with CRM systems like Salesforce and Zendesk
  • Connecting to ERP systems without coding

  • What does Human-in-the-Loop mean?
  • Adding human approval for critical actions
  • Reviewing customer emails before sending
  • Approving inventory or operational decisions

  • Protecting sensitive company data
  • Data sanitization before AI processing
  • Running AI locally using Ollama
  • Use cases for HR and legal confidentiality

  • Tracking token usage and operational costs
  • Measuring response accuracy
  • Monitoring agent performance
  • Calculating time and cost savings for the business
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