Course Overview
This one day program provides a structured and technically grounded introduction to modern Artificial Intelligence for corporate professionals in data‑driven and technology‑focused roles. Unlike general generative AI programs, this course focuses on how AI models actually work, how outputs are generated and how systems can be controlled, evaluated and integrated into real business workflows. The program emphasizes understanding AI as a controllable system rather than just a tool, introducing concepts such as model behavior, context limitations, output variability and reliability frameworks. Participants also gain exposure to structured prompting techniques, system-level workflows and light programmatic interaction, enabling them to confidently design, control and apply AI within enterprise environments.
What you will learn
Understand how modern AI models process inputs, generate outputs and behave under different conditions.
Evaluate AI outputs using logical validation, completeness and consistency frameworks.
Design structured prompts to control output format, reasoning style and consistency.
Design and control AI‑driven workflows and systems using structured logic and prompting techniques.
Apply output control techniques to improve accuracy, structure and reliability.
Understand basic integration and automation concepts for embedding AI into business processes.
Identify risks such as hallucinations, bias and variability and implement appropriate safeguards.
Course Curriculum
9
Modules
Modules
Module 1 : Introduction to AI - Concepts, Working & Types
● Core AI Concepts : Understand AI, machine learning and generative AI from a
corporate systems perspective.
● How AI Systems Work : Input, tokenization, model inference to output generation
explained clearly.
● Types of AI : Predictive, generative and rule-based AI systems in enterprise
applications.
● Enterprise Use Cases : How AI supports analytics, automation, operations and
decision-making.
Module 2 : LLM Architecture & Behavior
● Tokenization & Embeddings : How data is broken down and represented internally by
AI systems.
● Probabilistic Output Generation : How models predict and generate responses.
● Context Windows : Input limits and how they affect output completeness and
accuracy.
● Output Variability : Why small changes in prompts can significantly alter outputs.
Module 3 : Prompting for Controlled & Structured Outputs
● Prompt Engineering as Control : Using prompts as a mechanism to guide system
behavior.
● Structured Prompt Framework : Task, Context, Constraints, Format and Output
definition.
● Advanced Prompting Techniques : Role prompting, step-wise prompting and
structured output formats.
● Hands‑On Lab : Design prompts for structured reports and analytical workflows.
Module 4 : Output Control & Structured Generation
● Forcing Output Structure : Generating tables, lists, structured formats and
standardized outputs.
● Controlling Output Style : Managing tone, reasoning depth and detail levels.
● Consistency Techniques : Reducing variation and maintaining repeatability.
● Practical Exercise : Convert unstructured outputs into controlled, production-ready
formats.
Module 5 : Output Evaluation & Reliability Frameworks
● Evaluation Criteria : Accuracy, completeness, consistency and logical validity.
● Failure Modes : Hallucinations, ambiguity, missing assumptions and inconsistency.
● Validation Techniques : Iterative prompting, cross-checking and structured
verification.
● Hands‑On Lab : Refine and improve unreliable outputs using evaluation frameworks.
Module 6 : AI Integration Concepts - Light Code & System Thinking
● AI as a System Component : Understanding how AI fits into enterprise architecture.
● Input–Process–Output Pipelines : Designing structured workflows using AI steps.
● Prompt as Interface : Treating prompts as programmable inputs.
● Light Coding Concepts : Functions, structured logic and simple automation ideas.
● Hands‑On Lab : Walk through a simple AI-driven workflow using structured logic.
Module 7 : Workflow Design, Automation & Scaling AI
● Workflow Patterns : Single-step vs multi-step AI-driven workflows.
● Prompt Chaining : Linking multiple prompts into structured processes.
● Automation Thinking : Reducing manual work using repeatable logic.
● Hands‑On Lab : Design a scalable AI workflow for a business use case.
Module 8 : Responsible AI, Governance & Enterprise Risk
● Risk Identification : Bias, hallucinations, misuse and operational risks.
● Data Privacy & Security : Handling sensitive and confidential information safely.
● Governance Frameworks : Human-in-the-loop, approvals and accountability.
● Enterprise Readiness : Compliance, auditability and responsible system deployment.
Module 9 : Capstone - AI System Design for Business Use (Optional)
● Use Case Selection : Identify scalable and high-value applications of AI.
● Workflow & Prompt Design : Build structured systems for real-world tasks.
● Validation & Control Layers : Integrate reliability and governance checks.
● Final Output : Develop a complete AI-enabled workflow ready for implementation.
Module 1 : Introduction to AI - Concepts, Working & Types
Module 1 : Introduction to AI - Concepts, Working & Types
● Core AI Concepts : Understand AI, machine learning and generative AI from a
corporate systems perspective.
● How AI Systems Work : Input, tokenization, model inference to output generation
explained clearly.
● Types of AI : Predictive, generative and rule-based AI systems in enterprise
applications.
● Enterprise Use Cases : How AI supports analytics, automation, operations and
decision-making.
Module 2 : LLM Architecture & Behavior
● Tokenization & Embeddings : How data is broken down and represented internally by
AI systems.
● Probabilistic Output Generation : How models predict and generate responses.
● Context Windows : Input limits and how they affect output completeness and
accuracy.
● Output Variability : Why small changes in prompts can significantly alter outputs.
Module 3 : Prompting for Controlled & Structured Outputs
● Prompt Engineering as Control : Using prompts as a mechanism to guide system
behavior.
● Structured Prompt Framework : Task, Context, Constraints, Format and Output
definition.
● Advanced Prompting Techniques : Role prompting, step-wise prompting and
structured output formats.
● Hands‑On Lab : Design prompts for structured reports and analytical workflows.
Module 4 : Output Control & Structured Generation
● Forcing Output Structure : Generating tables, lists, structured formats and
standardized outputs.
● Controlling Output Style : Managing tone, reasoning depth and detail levels.
● Consistency Techniques : Reducing variation and maintaining repeatability.
● Practical Exercise : Convert unstructured outputs into controlled, production-ready
formats.
Module 5 : Output Evaluation & Reliability Frameworks
● Evaluation Criteria : Accuracy, completeness, consistency and logical validity.
● Failure Modes : Hallucinations, ambiguity, missing assumptions and inconsistency.
● Validation Techniques : Iterative prompting, cross-checking and structured
verification.
● Hands‑On Lab : Refine and improve unreliable outputs using evaluation frameworks.
Module 6 : AI Integration Concepts - Light Code & System Thinking
● AI as a System Component : Understanding how AI fits into enterprise architecture.
● Input–Process–Output Pipelines : Designing structured workflows using AI steps.
● Prompt as Interface : Treating prompts as programmable inputs.
● Light Coding Concepts : Functions, structured logic and simple automation ideas.
● Hands‑On Lab : Walk through a simple AI-driven workflow using structured logic.
Module 7 : Workflow Design, Automation & Scaling AI
● Workflow Patterns : Single-step vs multi-step AI-driven workflows.
● Prompt Chaining : Linking multiple prompts into structured processes.
● Automation Thinking : Reducing manual work using repeatable logic.
● Hands‑On Lab : Design a scalable AI workflow for a business use case.
Module 8 : Responsible AI, Governance & Enterprise Risk
● Risk Identification : Bias, hallucinations, misuse and operational risks.
● Data Privacy & Security : Handling sensitive and confidential information safely.
● Governance Frameworks : Human-in-the-loop, approvals and accountability.
● Enterprise Readiness : Compliance, auditability and responsible system deployment.
Module 9 : Capstone - AI System Design for Business Use (Optional)
● Use Case Selection : Identify scalable and high-value applications of AI.
● Workflow & Prompt Design : Build structured systems for real-world tasks.
● Validation & Control Layers : Integrate reliability and governance checks.
● Final Output : Develop a complete AI-enabled workflow ready for implementation.
Course Review
James
"Introduction to Data Science was a great course that helped me build a strong foundation in web development and programming. I especially appreciated how it covered both front-end technologies like HTML and JavaScript, and back-end tools like PHP and MySQL. The final project, a dynamic website with a login system, was a rewarding challenge. Highly recommend for anyone looking to start their journey in web development!"
Mark
I am in the middle of the course and it is fantastic, this course has helped me build an excellent foundation for interface designing.
$
10.99
$
20.99
48
%
off
37
Lessons
Approx
18 hours
to complete
Beginner
Level
English Language
Full Lifeime Access
Certificate of completion
Last Update 15 August, 2023
Instructor
Cameron Williamson
Data Scientist
Top Instructor
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Students
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