Step-by-step tutorials on building voice and chat AI agents using OpenAI Agents SDK, Realtime API, function calling, multi-agent orchestration, and production deployment patterns.
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Learn how to implement semantic versioning, generate changelogs automatically, publish to PyPI and npm, and set up CI/CD release pipelines for AI agent SDK distribution.
Build a complete resume parsing pipeline from PDF to structured data. Covers PDF text extraction, schema design for work experience and education, LLM extraction, validation, and output formatting.
Build resilient structured output systems that handle LLM failures gracefully. Learn retry strategies with exponential backoff, fallback schemas, partial result recovery, and graceful degradation patterns.
Learn how to stream structured outputs from LLMs for real-time UI updates. Covers partial JSON parsing, streaming with Instructor and Pydantic, progressive UI rendering, and handling incomplete data.
Build comprehensive validation layers for LLM outputs using Pydantic validators, cross-field validation, domain-specific constraints, and data quality scoring. Catch hallucinations before they reach your database.
Master the core building blocks of LangChain including chains, prompt templates, language model wrappers, and the LangChain Expression Language for composing AI applications.
Learn how to build LangChain agents that use tools to solve problems, understand the ReAct reasoning loop, and configure AgentExecutor for reliable agent behavior.
Explore LangChain's memory types for building conversational AI — from simple buffer memory to summarization and vector-store-backed long-term memory with persistence strategies.
