How This Course Continues in Phase 2
You'll learn to
- -Know exactly what changes in Phase 2: real hands-on practice via the GenAI Lab
- -Feel ready to continue directly into Phase 2's first chapter
Congratulations. Finishing this module means you have finished all of Phase 1. Every concept from here forward in this course builds directly on the foundation you just built: vectors and distance, probability and loss, gradient descent and backpropagation, overfitting and evaluation, and the discriminative and generative distinction from the last two chapters.
What Changes Starting Now
The biggest practical change is that from this point on, nearly every module ends with a direct bridge into a real, hands-on level in the GenAI Lab. Phase 1 had no lab exercises of its own, because the lab is entirely about generative AI systems, but everything Phase 2 covers has a matching level waiting for you to actually build it, not just read about it.
- -Phase 2 starts with LLM foundations: tokenization, embeddings, and memory, the concrete mechanics behind the "how a transformer processes language" preview from the Neural Networks module.
- -From there: retrieval-augmented generation, agents and tool use, and finally production and LLMOps concerns.
- -The course closes with a capstone module of fully worked GenAI system design examples, mirroring the worked examples you may have seen in ScaleDojo's other Learn courses.
If any Phase 1 concept feels shaky, especially vectors and embeddings, loss functions, or the discriminative versus generative distinction from the last two chapters, this is a good moment to skim back over it. Phase 2 assumes this vocabulary rather than re-explaining it from scratch.
Ready when you are. Continue on to Welcome to Generative AI Engineering, the first chapter of Phase 2.
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