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Learn intent classification for agent routing in a detailed, easy-to-understand way. This guide explains LLM-based routing, embedding similarity, hybrid classifiers, confidence thresholds, fallback logic, and multi-intent detection with a practical example.
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Production guardrails for LLM applications — input/output filtering, structured output enforcement with Pydantic and JSON mode, content moderation pipelines, PII detection and redaction, hallucination detection, and integration patterns with Guardrails AI and NeMo Guardrails.
Build production-grade LLM evaluation from scratch: async JudgeClient, position-bias-corrected pairwise comparison, rubric scoring with normalization, judge calibration, meta-evaluation, human eval with SQLite and Cohen's kappa, pytest CI/CD integration, eval dataset construction, bootstrap confidence intervals, and online monitoring.
Production-ready prompt engineering patterns with runnable Python code: chain-of-thought, few-shot learning, self-consistency, prompt chaining, structured output, system prompt design, and advanced techniques including A/B testing and regression frameworks.
Production state management for multi-agent workflows — Redis for ephemeral coordination, PostgreSQL for durable records, LangGraph for typed state graphs with conditional routing, and checkpoint/resume patterns that actually survive crashes.
Learn intent classification for agent routing in a detailed, easy-to-understand way. This guide explains LLM-based routing, embedding similarity, hybrid classifiers, confidence thresholds, fallback logic, and multi-intent detection with a practical example.
Build production orchestration for multi-agent systems — supervisor routing with LLM classification, parallel fan-out with error recovery, event-driven coordination, and hierarchical delegation. Includes comparison matrix and combined architecture example.