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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Build production-grade entity extraction with LLMs. Learn schema design for names, dates, addresses, and custom entity types, plus batch extraction techniques and accuracy optimization strategies.
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 leverage prompt caching features from OpenAI and Anthropic to dramatically reduce latency and cost by reusing cached prompt prefixes across requests.
Implement robust security for MCP servers with OAuth2 integration, API key validation, permission scopes, and token management to ensure AI agents only access tools they are authorized to use.
Build function-calling agents with Claude's native tool use. Learn how to define tools with JSON schemas, handle tool_use and tool_result blocks, and orchestrate multi-step tool conversations.
Build a comprehensive chat analytics system with conversation metrics collection, conversion tracking, satisfaction scoring, session analysis, and A/B testing frameworks to continuously improve your chat agents.
Learn how to use knowledge distillation to transfer capabilities from large teacher models to smaller, cheaper student models suitable for production deployment, with concrete examples of cost savings and quality tradeoffs.
Learn how to incrementally update fine-tuned models with new data while preserving existing capabilities, using replay buffers, evaluation gates, elastic weight consolidation, and model versioning strategies.
