In July, we expanded our course catalog with new offerings. With our latest courses, you'll be able to develop production-ready RAG applications, adapt LLMs for specific tasks and behaviors using post-training techniques, and build reliable LLM applications with structured outputs and validated data using Pydantic.
Explore our most recent courses:
Retrieval Augmented Generation (RAG)
This course, taught by AI engineer and educator Zain Hasan, gives you the hands-on experience and conceptual understanding to design, build, and evaluate production-ready RAG systems. You’ll learn to choose the right architecture for your use case, work with vector databases like Weaviate, experiment with prompt and retrieval strategies, and monitor performance using tools like Phoenix from Arize.
In this course, you’ll learn three common post-training methods—Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Online Reinforcement Learning (RL)—and how to use each one effectively. With SFT, you train the model on input-output pairs with ideal output responses. With DPO, you provide both a preferred (‘chosen’) and a less preferred (‘rejected’) response, and train the model to favor the preferred output. With RL, the model generates an output, receives a reward score based on human or automated feedback, and updates the model to improve performance.
You’ll learn the basic concepts, common use-cases, and principles for curating high-quality data for effective training in each of these methods.
In this course, you’ll learn to bring structure, reliability, and validation to the data in your LLM-powered applications using Pydantic, a Python library for data validation.
You’ll begin by understanding what structured output is and why it matters when building applications that use LLMs. Through the example of a customer support assistant, you’ll learn different methods of using Pydantic to ensure an LLM gives you the expected data and format you need in your application. These methods ensure that the LLM’s responses are complete, correctly formatted, and ready to use, whether that means creating support tickets, triggering tools, or routing requests.
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