Agentic AI, LLM engineering, and the models behind modern automation — multi-agent systems, LLM evaluation and comparisons, RAG, fine-tuning, AI infrastructure, security, and production AI engineering.
From the blog
Build automated benchmark suites for evaluating prompt performance across different models and versions. Learn to design test cases, detect regressions, and generate actionable performance reports.
Build maintainable prompt systems using Jinja2 templates, Python f-strings, and variable injection. Learn how to version control prompts and create dynamic instruction pipelines for production AI applications.
Apply rigorous testing methodology to prompt engineering — A/B test prompts, define evaluation metrics, version your prompts, and build regression test suites that prevent quality regressions in production.
Master prompt templating techniques using Jinja2 and Python f-strings. Learn variable injection patterns, conditional blocks, loop constructs, custom filters, and safety practices for dynamic prompts.
Learn how to version control your AI prompts using Git. Covers file-based prompt storage, meaningful diffs, branch strategies for prompt experiments, and rollback techniques for production safety.
Explore PydanticAI's approach to building agents with typed tool parameters, structured result types, dependency injection, and the data validation guarantees that Pydantic is known for.
Learn how to use Pydantic BaseModel, Field validators, and nested models to parse and validate LLM responses into type-safe Python objects. Build reliable AI pipelines that never break on malformed output.
Learn how to use Pydantic v2 for robust data validation, settings management, and serialization in AI agent applications with BaseModel, Field validators, and model_config.
Learn to define robust agent interfaces using Python's abc module with abstract methods, interface contracts, plugin patterns, and registration hooks for extensible AI architectures.
