Session 1: How LLMs Work
Aug 26, 2025
Demystify how LLMs really work—watch Session 1 of The Road to Agentic AI and level up your prompt game.
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Session 3: Thinking in Latent Space
The Road to Agentic AI
What you'll learn
This session unpacks the concept of latent space—the invisible multi-dimensional geometry behind how large language models (LLMs) like ChatGPT and Copilot function. You’ll explore how seemingly intelligent behaviors like translation, reasoning, and planning emerge not from comprehension, but from probabilistic pattern-matching—and what that means for how you prompt AI tools effectively.
Who should watch
This session is ideal for:
- Business and tech leaders integrating AI tools
- Enterprise users frustrated with “hit-or-miss” results
- Anyone serious about developing prompt engineering skills
- Teams tasked with AI implementation or strategy
Key takeaways
- LLMs mimic patterns—not meaning: Translation, reasoning, and even logic are not “features” of the model but emergent behaviors based on probabilistic pattern completion in latent space.
- Latent space is where the magic happens: This abstract multi-dimensional space organizes tokens based on statistical relationships, enabling the model to generate coherent output without “understanding” anything.
- Prompting is positioning: A prompt isn’t a command—it’s a way to position the model within a specific region of latent space to steer it toward desired outputs.
- Chain of Thought (CoT) = Simulated reasoning: CoT prompting, using phrases like “think it through step by step,” invokes structured, interpretable output. It’s one of the most powerful tools for managing complexity and ambiguity in AI interactions.
- All AI design is probability design: Whether you’re using Copilot or building agentic systems, success comes down to intentionally shaping probabilistic trajectories in latent space.
About the speaker
Julian Lancaster
As Chief Information Security Officer at Prowess Consulting, Julian Lancaster brings a grounded, refreshing, and practical perspective to the Agentic AI series. A passionate advocate for responsible AI adoption, Julian focuses on building foundational understanding of how large language models (LLMs) work, and how teams, individuals, and organizations can leverage them to increase efficiency, scale capacity, and drive smarter decision-making. With a strong background in cybersecurity and enterprise operations, Julian helps demystify AI technologies so they can be used effectively and securely across the business.
Related Videos
Session 2: Prompt Design as System Design
Oct 8, 2025
Learn how prompt design shapes AI behavior—and how to get more reliable, creative, and targeted results from LLMs.
Session 3: Thinking in Latent Space
Jul 15, 2025
Explore how LLMs use latent space and probability—not understanding—to simulate reasoning, translation, and more through prompt-driven behavior.
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