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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Master the fundamentals of prompt engineering — learn to write clear system and user messages, format instructions for consistency, and avoid common pitfalls that cause unreliable LLM outputs.
Understand the key differences between zero-shot, one-shot, and few-shot prompting. Learn when each technique works best and how to select high-quality examples for reliable LLM outputs.
Learn how chain-of-thought (CoT) prompting dramatically improves LLM accuracy on reasoning tasks. Covers manual CoT, auto-CoT, tree-of-thought, and when step-by-step reasoning helps most.
Learn proven patterns for writing effective system prompts — from persona design and behavioral constraints to output formatting instructions that produce consistent, high-quality LLM responses.
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.
Master techniques for extracting structured data from LLMs — JSON mode, schema enforcement, parsing strategies, and robust handling of malformed responses in production systems.
Learn how assigning specific roles and expertise to LLMs dramatically improves response quality. Covers proven persona patterns, role combinations, and techniques to minimize hallucination in role-based prompts.
Master the art of negative prompting — learn how to set boundaries, use guardrails, and constrain LLM behavior by specifying what to avoid. Includes real patterns for production prompt safety.
