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 multi-modal prompting techniques that combine text, images, and code inputs in a single prompt to unlock more capable and context-rich LLM interactions.
Learn practical prompt compression techniques including LLMLingua, selective context pruning, and abstractive compression to cut token costs while preserving output quality.
Learn how to build robust input validation pipelines for AI agents using regex filters, content classifiers, blocklists, and input length limits to stop malicious input before it reaches your LLM.
Learn how to build evaluation frameworks with scoring rubrics, A/B testing, and regression testing to systematically improve prompt quality and catch regressions before production.
Learn how to build production-grade prompt libraries for regulated industries with domain-specific templates, terminology handling, and compliance-aware prompting patterns.
Learn how to use Pydantic BaseModel, Field validators, and nested models to parse and validate LLM responses into type-safe Python objects. Build reliable AI pipelines that never break on malformed output.
Master OpenAI's structured outputs feature with json_schema response format, strict mode, refusal handling, and complex schema definitions. Get guaranteed valid JSON from GPT models every time.
Design and implement multi-step data extraction pipelines that transform unstructured text into clean structured data using LLMs. Covers entity extraction, relation extraction, and pipeline orchestration.
