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
Master Python asyncio from the ground up. Learn coroutines, tasks, event loops, and async/await patterns essential for building high-throughput AI agent systems.
Learn to use Python context managers for reliable resource management in AI applications including API client lifecycles, database connections, and async session handling.
Compare Python dataclasses and Pydantic models for AI agent state management including performance benchmarks, validation capabilities, serialization, and practical use cases.
Python decorator for llm tool authorization: learn how to build reusable decorators for AI agent tools including retry logic, caching, authentication, and rate limiting using functools.wraps and parametrized patterns.
Master Python dependency management for AI projects using Poetry, uv, and virtual environments with reproducible builds, lock files, and Docker integration strategies.
Master Python generators and async iterators for building efficient streaming AI response pipelines with memory-efficient processing, backpressure handling, and real-time output.
Configure production-grade logging for AI applications using structlog and loguru with structured JSON output, context binding, correlation IDs, and cost-aware filtering.
Learn to identify and fix performance bottlenecks in AI applications using cProfile, py-spy, memory profiling, and optimization strategies for LLM pipelines and data processing.
Master Python type hints for AI engineering including generics, TypedDict, Protocol classes, and runtime validation to build maintainable agent applications with self-documenting interfaces.
