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Turn AI confusion into clarity. We break down complex concepts into ideas you can grasp, build on, and actually use. Start anywhere, learn what matters to you.

Your journey from AI curious to AI confident.

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Academy: LLM Fundamentals

Understanding tokens and context — developed through AI-assisted research and multi-model validation

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Labs: AI System Analysis

Testing results and research findings from our ongoing experiments

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The ReasonPath Academy

The ReasonPath Academy

An educational resource for understanding the principles, applications, and critical limitations of modern AI.

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My Progress

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In-Progress Course

The Problem of Hallucination 75% complete
Explore Learning Paths

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.

🎓 Beginner ⏱️ ~30 mins
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How AI Learns: Training vs. Inference

Understand the fundamental difference between teaching an AI and using it to generate answers.

🎓 Beginner ⏱️ ~45 mins
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From Data to Decisions: The AI Pipeline

A high-level overview of the end-to-end process of building a functional AI application.

🎓 Beginner ⏱️ ~45 mins
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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.

🎓 Intermediate ⏱️ ~1 hour
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Unpacking Model Bias

An analysis of how biases in data and design lead to skewed and unfair AI outputs.

🎓 Intermediate ⏱️ ~1.5 hours
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The Black Box Challenge: Intro to Interpretability

A look at the ongoing research challenge of understanding why a model makes a specific decision.

🎓 Advanced ⏱️ ~2 hours
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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.

🎓 Intermediate ⏱️ ~1 hour
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The Alignment Problem

A conceptual overview of the challenge of ensuring advanced AI systems pursue intended goals.

🎓 Advanced ⏱️ ~1.5 hours
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ReasonPath Labs

Labs

Where we test AI systems, analyze research papers, and share findings

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AI Glossary

AI Glossary

AI terms explained in plain English - navigate AI terminology with confidence

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Glossary Terms

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ReasonPath Learning Digest

ReasonPath Learning Digest

AI learning insights from an AI-designed learning hub

The ReasonPath Learning Digest shares discoveries, experiments, and honest takes on AI development. Built by AI with human oversight, we explore what's working, what's not, and what we're learning along the way.

What You'll Get

🧪 Real Experiments

Weekly experiments with new AI tools and techniques, plus honest assessments of what actually works vs. hype.

📚 Learning Resources

Resource roundups of actually useful AI learning materials, curated by AI and tested by humans.

🎯 Behind-the-Scenes

Honest looks at building an AI-designed platform, including mistakes, discoveries, and lessons learned.

👥 Community Highlights

Discoveries from fellow AI learners sharing their experiments and insights with the community.

Latest Issue #12 - March 15, 2024

"What We Learned Building This Newsletter (Spoiler: AI Did Most of It)"

This Week's Experiment

Building a Dictionary with Claude: Success and Struggles

We created a 150-term AI dictionary using Claude 4. What worked: comprehensive definitions and great analogies. What didn't: keeping the scope reasonable (we originally planned 50 terms).

Key discovery: AI analogies are surprisingly effective for explaining complex concepts

Honest Assessment

When AI Suggestions Go Wrong: This week our AI suggested adding a "quantum computing module." We don't know quantum computing. Neither does the AI, really. Filed under "maybe later."

Community Spotlight

Sarah from Portland shared her experience using our dictionary to explain AI to her team. Apparently the analogies actually helped! Small wins count.

Read Full Issue →
Recent Issues
Issue #11 - March 8, 2024

"Claude Helped Design Our Website: Here's What Worked and What Didn't"

Issue #10 - March 1, 2024

"Learning in Public: Our First Month of Mistakes"

Issue #9 - February 23, 2024

"AI Tools That Actually Save Time (And Ones That Don't)"

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About ReasonPath™

Independent AI Education

ReasonPath™ is an independent educational platform focused on AI literacy and understanding.

Educational Methodology

Our content development process:

  • Independent Research: Using publicly available AI tools and documentation
  • Comparative Analysis: Evaluating multiple AI systems for educational purposes
  • Original Commentary: Adding educational context and synthesis
  • Legal Compliance: Regular review of all content for trademark and fair use compliance

Contact & Legal

General Inquiries: info@reasonpath.com

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Press & Media: press@reasonpath.com