AI Academy

Pluralsight's AI Academy goes beyond a collection of courses, moving organizations from experimentation into actual execution. By combining expert-led learning, hands-on labs, live seminars, code-along sessions, and assessments, you get a structured, end-to-end journey built for clarity, alignment, and lasting impact.

Using a systematic, multi-tiered approach, your team starts with full alignment by building a common AI literacy. The journey is supported with a series of seminars to support each phase of the approach. These instructor-led sessions, part of the overall AI Academy package, will be uniquely tailored to your specific business needs, developing actual solutions as they learn. Each of the sessions is explained below.

As you review these seminars, please note:

THE SEMINARS ARE NOT ELIGIBLE FOR TRAINING BUNDLES OR TRAINING CREDITS.
THEY ARE ONLY AVAILABLE WITH THE PURCHASE OF THE PLURALSIGHT AI ACADEMY SOLUTION PACKAGE.

Contact your Pluralsight Account Executive for more information.    

SEMINARS

 

Seminar: Introduction to Generative AI

This two-hour seminar establishes a firm connection between Generative AI mechanics and practical organizational utility. Participants will explore how Large Language Models function, identifying both their high-value strengths and critical limitations to manage expectations effectively. The session provides clear guidance on utilizing participants’ company-approved AI tools and introduces foundational prompting best practices to ensure immediate, secure, and ethical interaction with AI systems in a professional environment.

Audience

This session is ideal for:

• Business professionals and stakeholders
• Individual contributors new to AI

Prerequisites

To get the most of this session, participants should have:

• Basic familiarity with standard productivity software
• No prior technical AI experience required

Learning objectives

After this session, participants will be able to:

• Understand what Generative AI is, how it works, and its core capabilities
• Recognize Generative AI’s key strengths and limitations
• Identify company-approved AI tools and when to use them
• Utilize the basic best practices for effective prompt engineering
• Apply Responsible AI principles for safe, secure, and ethical use

Duration

2 hours 

Outline

Generative AI Fundamentals & Capabilities

• Defining Gen AI and LLM evolution
• Model processing and content generation mechanics
• Core capabilities in text, summarization, and analysis

AI Strengths and Limitations

• High-value strengths in productivity and creative workflows
• Risks regarding hallucinations, bias, and technical hurdles
• Output evaluation and human-in-the-loop validation

Company-Approved AI Ecosystem

• Directory of approved enterprise AI tools
• Tool selection based on specific task requirements
• Boundaries between personal and professional accounts

Foundational Prompt Engineering

• Basic structures for clear and concise prompts
• Using personas, context, and constraints for quality
• Demonstrations of iterative prompt refinement

Responsible and Ethical AI Usage

• Principles of safe and secure AI interaction
• Alignment with data privacy and IP policies
• Ethical considerations and company-specific guardrails

 

Seminar: Prompting for Productivity

This two-hour seminar guides learners through the evolution of AI interaction, advancing from foundational prompt engineering skills to the strategic application of autonomous systems. Participants will gain insight into how prompt frameworks serve as the "instructions" for more complex Agentic AI architectures, exploring real-world use cases where AI moves from simple conversation to executing multi-step tasks using memory and specialized tools.

Audience

This session is ideal for:

• Business professionals seeking to optimize daily AI interactions
• Individual contributors looking to automate repetitive digital workflows

Prerequisites

To get the most of this session, participants should have:

• Experience using a chat-based AI interface
• Basic understanding of LLM capabilities

Learning objectives

After this session, participants will be able to:

• Apply advanced prompting frameworks to improve output quality
• Understand the transition from chat-based AI to agentic systems
• Recognize the role of memory and tool-use in AI agents
• Identify strategic use cases for autonomous AI agents

Duration

2 hours

Outline

Advanced Prompting Frameworks

• Persona, context, and constraint-based prompting
• Few-shot and chain-of-thought techniques
• Refining outputs through iterative cycles

Introduction to Agentic AI

• Differences between chatbots and autonomous agents
• Standard chat workflows vs agentic reasoning loops
• Demonstrations of agent-led task execution

Agentic Components: Memory and Tools

• Role of state and context through memory
• Interaction with external APIs and search tools
• Impact of tool-integration on task accuracy

Strategic Application and Best Practices

• Mapping prompts to specific business workflows
• Identifying workflows for agentic automation
• Best practices for prompt versioning and evaluation
   

Code-Along: Agentic AI Code-Along

This four-hour hands-on code-along session focuses on building autonomous agents using low-code/no-code platforms. Participants learn to configure architectural components including grounded knowledge bases, persona-based instructions, and functional skills. Utilizing the organization’s specific AI provider, learners iteratively develop a functional agent. The code-along guides participants through the "Plan-Execute-Observe-Reflect" workflow to create an agent capable of performing real-world tasks with minimal manual intervention.

Audience

This session is ideal for:

• Business professionals seeking to automate daily digital processes
• Individual contributors transitioning into agentic system design
• Innovation leads prototyping AI solutions without custom coding

Prerequisites

To get the most of this session, participants should have:

• Basic understanding of organizational data sources and common business workflows
• Access to the client-specified low-code AI development environment (ex: Google Workspace Studio or Microsoft Copilot Studio)

Learning objectives

After this session, participants will be able to:

• Identify the core architectural components of a functional AI agent
• Understand the iterative agentic workflow of planning, execution, and reflection
• Connect an AI agent to external knowledge sources and functional tools
• Build a functional low-code AI agent to automate a specific business task

Duration

2 hours

Outline

Anatomy of an AI Agent

• LLM, orchestrator, and user experience layers
• Active task-oriented agents vs passive chatbots
• Mapping reasoning loops to the agent orchestrator
• Configuring the agentic persona and role-playing instructions

Grounding with Knowledge and Memory

• Internal data sources vs public internet grounding
• Interaction memory vs long-term knowledge bases
• Secure connections to document libraries or databases
• Building a retrieval-based agent grounded in enterprise data

Defining Agency: Skills and Tools

• Assigning tools for external system actions
• Boundaries and guardrails for autonomous tool usage
• Integrating data analysis and code interpretation capabilities
• Configuring agent skills to perform automated system actions

Practical Application and Review

• Safety, privacy, and cost-control guardrails
• Performance validation through starter prompts and test queries
• Iterative refinement based on reflection and feedback
• Finalizing the end-to-end agentic workflow for the target use case

Seminar: AI Strategy for Leaders

This two-hour seminar targets the executive layer, bridging the gap between high-level Gen AI opportunity assessment and concrete organizational strategy. By examining real-world case studies of successful AI leadership, participants will learn to prioritize AI initiatives based on ROI and complexity while establishing the necessary governance frameworks to manage technical, ethical, and workforce-related risks.

Audience

This session is ideal for:

• Senior Leaders and Executives responsible for department-wide technology adoption
• Operations and Strategy Directors focused on ROI and process optimization
• HR and Talent Leaders managing the transition to an AI-enabled workforce

Prerequisites

To get the most of this session, participants should have:

• General understanding of company business objectives
• Awareness of the current Generative AI landscape

Learning objectives

After this session, participants will be able to:

• Evaluate Generative AI opportunities through a strategic lens
• Describe successful AI implementation strategies from real-world case studies
• Understand executive-level best practices for managing AI risk and governance
• Identify cultural and workforce readiness factors for AI adoption

Duration

2 hours

Outline

Assessing the AI Opportunity

• Categorizing AI initiatives by ROI and complexity
• Project prioritization aligned with organizational goals
• Scaling from experimental pilots to enterprise solutions

Case Studies in AI Leadership

• Enterprise-wide AI transformation successes
• Pitfalls and lessons from early adopters
• AI impact on workforce structure and culture

Governance, Risk, and Management

• Frameworks for AI ethics and transparency
• Managing technical debt and long-term ownership costs
• Post-learning evaluations to measure success

Leading Organizational Change

• Identifying skills gaps within the existing workforce
• Developing a culture of continuous learning and experimentation
• Communicating AI vision to reduce friction and gain buy-in