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 a comprehensive evaluation methodology for fine-tuned LLMs, combining automated benchmarks, human evaluation, and production A/B testing to measure real-world improvement with statistical rigor.
Build a complete, working AI agent from scratch using only the OpenAI API and Python standard library — with a tool loop, conversation management, structured tool calling, and error handling.
Learn how to write effective JSON Schema tool definitions that help LLMs understand parameters, constraints, and expected inputs. Covers parameter types, descriptions, required vs optional fields, nested objects, and enums.
Walk through building a complete calculator tool for an AI agent from scratch. Covers schema definition, safe expression evaluation, result handling, and integration with the agent loop.
Learn how to build safe database query tools that let AI agents retrieve data from PostgreSQL and SQLite. Covers parameterized queries, read-only enforcement, result formatting, and guardrails against destructive operations.
Build HTTP-based tools that let AI agents fetch web pages, parse content, and extract structured data. Covers request handling, HTML parsing, rate limiting, error handling, and responsible scraping practices.
Build generic API integration tools that let AI agents call REST and GraphQL endpoints with proper authentication, error handling, retry logic, and response mapping. Learn patterns for building flexible yet safe API tools.
Learn how to execute multiple tool calls in parallel to dramatically speed up AI agent workflows. Covers async execution with asyncio.gather, handling partial failures, result aggregation, and timeout management.
