The ReasonPath Academy
An educational resource for understanding the principles, applications, and critical limitations of modern AI.
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Path Category 1: AI Fundamentals
Start here to build a solid foundation. These courses cover the essential, non-technical concepts behind how AI models work.
What is a Large Language Model?
A simple primer on what LLMs are, how they are trained, and their basic capabilities.
How AI Learns: Training vs. Inference
Understand the fundamental difference between teaching an AI and using it to generate answers.
From Data to Decisions: The AI Pipeline
A high-level overview of the end-to-end process of building a functional AI application.
Path Category 2: Understanding AI Limitations
Go beyond the hype. This path focuses on the inherent weaknesses and failure modes of current AI systems, aligning with ReasonPath's core mission.
The Problem of Hallucination
Learn why models confidently generate false information and explore the concept of grounding.
Unpacking Model Bias
An analysis of how biases in data and design lead to skewed and unfair AI outputs.
The Black Box Challenge: Intro to Interpretability
A look at the ongoing research challenge of understanding why a model makes a specific decision.
Path Category 3: AI Safety & Alignment
Explore the practical techniques and ethical frameworks used to make AI systems safer, more reliable, and aligned with human values.
AI Guardrails and Red Teaming
An introduction to the proactive security measures used to test and constrain model behavior.
The Alignment Problem
A conceptual overview of the challenge of ensuring advanced AI systems pursue intended goals.