A deep dive introduction to model capabilities, context design and engineering and experience evaluation.
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Let's do some context design. The model's context window is the key to creating useful and helpful output.
So we did some context design - now how does that become context engineering?
And the final missing piece: evals! But wait, do we even need them here?
What does it mean for models to be stateless? Let's build some intuition around that.
And what does it mean for models to be Stocastic? Why do they hallucinate? Can we ever get beyond that?
Some common misunderstandings about AI and Large Language Models can easily lead us astray.
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?
How are capabilities trained into models? How can we build intuition around these capabilities and best use them?
What is model character, how is it trained, and how can we learn to understand and use this beyond "Claude feels friendlier"?
“This is opening up all sorts of new neural pathways for me to see under the hood more of how the sausage is made! 🙏”
“Very timely at my enterprise software company as evaluation of AI features scales.”
“Everything I know about evals is from Peter's talk, which is why I'm back to find out more!”