AI 101
A deep dive introduction to model capabilities, context design and engineering and experience evaluation.
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1. Introduction
2. Getting our hands dirty
Context Design Exercise
Let's do some context design. The model's context window is the key to creating useful and helpful output.
Context Engineering Exercise
So we did some context design - now how does that become context engineering?
Evals Exercise
And the final missing piece: evals! But wait, do we even need them here?
3. Model basics
Models are Stateless
What does it mean for models to be stateless? Let's build some intuition around that.
Models are Stochastic
And what does it mean for models to be Stocastic? Why do they hallucinate? Can we ever get beyond that?
A model warning
Some common misunderstandings about AI and Large Language Models can easily lead us astray.
4. Model Capabilities
Introduction to Model Capabilities
What can LLMs do? How do we know what the capabilities of these models are? How are they trained? And how does that influence our product design decisions?
Model Post Training and RLHF
How are capabilities trained into models? How can we build intuition around these capabilities and best use them?
Why do models have different personalities?
What is model character, how is it trained, and how can we learn to understand and use this beyond "Claude feels friendlier"?
What participants say
"This training has provided valuable insights into AI product development methodologies and practical implementation strategies."
— Course Participant
"Highly relevant for our enterprise software organization as we scale AI feature evaluation processes."
— Course Participant
"The evaluation framework training has been instrumental in establishing our AI quality processes."
— Course Participant