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 robust text classification systems for AI agent routing using intent detection, multi-label classification, and zero-shot approaches with practical Python implementations.
Compare extractive and abstractive summarization methods for AI agents, learn to control summary length and quality, and implement key point extraction pipelines in Python.
Learn keyword extraction with TF-IDF and KeyBERT, topic modeling with BERTopic and LDA, and how to build agent knowledge organization systems that automatically categorize and cluster documents.
Learn how coreference resolution enables AI agents to track pronouns and references across conversation turns, with practical implementations using spaCy, neural models, and LLM-based approaches.
Build multilingual AI agents with language detection, translation API integration, quality assessment, and fallback strategies that handle real-world linguistic diversity.
Build end-to-end information extraction pipelines for AI agents that convert unstructured text into structured data using extraction patterns, relation extraction, template filling, and validation.
Build robust text preprocessing pipelines for AI agents covering HTML stripping, Unicode normalization, tokenization, length management, and input sanitization with production-ready Python code.
Master the core principles of conversation design for AI agents including turn structure, progressive disclosure, error recovery, and building flows that feel natural to users.
