Large Language Model and Agentic AI Explainability
Black-box AI decisions are becoming liability nightmares for enterprises deploying LLMs at scale. This focused training demystifies explainability techniques for large language models and agentic AI systems, giving you the frameworks to make AI decisions transparent and auditable.
AIU.ac Verdict: Essential for anyone deploying AI in regulated environments or client-facing applications where decision transparency matters. However, the 28-minute format means you’ll get frameworks rather than deep implementation details.
What This Course Covers
You’ll explore core explainability concepts for LLMs, including attention visualisation, prompt engineering for transparency, and interpretability techniques for transformer architectures. The course covers practical methods for explaining model outputs, understanding decision pathways, and implementing explainability frameworks in production environments.
The agentic AI section focuses on multi-step reasoning transparency, agent decision trees, and chain-of-thought explanations. You’ll learn to implement explainability dashboards, create audit trails for AI decisions, and establish governance frameworks that satisfy regulatory requirements whilst maintaining system performance.
Who Is This Course For?
Ideal for:
- AI Product Managers: Need to explain AI decisions to stakeholders and ensure compliance with emerging AI regulations
- ML Engineers: Building production LLM systems that require transparency and auditability for enterprise deployment
- AI Ethics Officers: Responsible for implementing explainable AI practices and ensuring algorithmic accountability across organisations
May not suit:
- Complete AI Beginners: Assumes familiarity with LLM architectures and transformer models – you’ll need foundational knowledge first
- Deep Research Focus: The brief format covers practical frameworks rather than cutting-edge explainability research or novel techniques
Frequently Asked Questions
How long does Large Language Model and Agentic AI Explainability take?
The course runs for 28 minutes of focused video content, designed for busy professionals who need practical explainability frameworks quickly.
Do I need prior experience with LLMs?
Yes, you should understand transformer architectures and have worked with large language models. This isn’t an introduction to LLMs themselves.
Will this cover regulatory compliance?
The course provides frameworks that support compliance efforts, but you’ll need to adapt techniques to specific regulatory requirements in your jurisdiction.
Is there hands-on practice included?
As a Pluralsight course, you’ll have access to their hands-on labs and sandbox environments to practice explainability techniques with real models.
Course by Daniel Stern on Pluralsight. Duration: 0h 28m. Last verified by AIU.ac: March 2026.
