Build end-to-end observability for AI agents: traces, metrics, logs, and evals to debug, govern privacy, and scale quality, reliability, and cost.
Build a production-ready AI recipe generation API with JSON schema, prompting, validation, caching, and code in Node.js and Python.
A practical DSPy tutorial: build modular LLM programs with signatures, teleprompters, and RAG—plus evaluation and production tips.
A practical guide to designing, implementing, and governing AI chatbot personality customization—traits, prompts, memory, guardrails, and evaluation.
A practical, data-driven guide comparing prompting vs. fine-tuning for LLM apps, with decision checklists, trade-offs, and implementation tips.
Design a production-grade AI marketing copy generation API: architecture, prompts, guardrails, evaluation, and code examples.
