Dive into LLM Building Blocks for Python, a concise 1.2-hour video course that equips you with everything you need to integrate large language models into your Python applications. You’ll learn to move beyond "text in → text out" by turning your prompts into structured data, orchestrating chat-style workflows, and building interactive prototypes. From rapid-fire notebook experiments to production-ready async pipelines and caching, this course gives you practical, code-first techniques for real-world LLM development.
By the end of this course, you’ll be able to:
- Set up and use Marimo for live, reactive notebook experiments
- Install and configure the llm library and its plugins for multiple vendors
- Craft prompts with Pydantic schemas to enforce structured JSON outputs
- Build and manage chat conversations programmatically in Python
- Orchestrate async LLM calls and understand concurrency limits
- Implement disk caching to save tokens, speed up development, and cut costs
- Measure classification accuracy and benchmark LLM vs. scikit-learn pipelines
- Conduct A/B tests on prompts to iteratively refine model outputs
- Explore higher-level tools like smartfunk, Mirascope, Ollama, and Instructor
- Design small "apps" inside Marimo to automate tasks such as YouTube transcript summarization
- Python developers curious about adding LLM features to scripts, tools, or web apps
- Data scientists wanting to prototype NLP workflows without deep ML expertise
- DevOps/automation engineers looking to integrate AI-driven tasks into pipelines
- Tech leads and architects evaluating LLM toolchains for production use
- Educators and researchers who need structured LLM interactions in their code
Visit Talk Python Training to take the course for just 19ドル USD.