Thirteen deep, visual guides to the skills that ship real AI products - each with a clear what / why / how, detailed sequence diagrams, a step-by-step 0→100 flow, and real case studies. Built to be read, not skimmed.
Designing and building systems on top of large language models.
Planning loops, tool use, memory, and coordinating fleets of specialized agents.
Read → Live 🔧How models invoke functions - schemas, arguments, validation, and the call loop.
Read → Live 🔌The open standard that plugs any model into any tool, data source, or API.
Read → Live 📚Retrieval-augmented generation: grounding answers in your own live data.
Read → Live ✍️Structuring instructions, context, few-shot, and reasoning for reliable output.
Read → Live 🔀Sending each request to the right model to balance quality, speed, and spend.
Read → Live 📱Running quantized models locally for privacy, latency, and offline use.
Read → Live 🧭Turning text into vectors and searching them fast, the engine under RAG.
Read → Live 🎛️Adapting model weights to your task, and when to prefer it over RAG.
Read → Live 🧱JSON mode and schema-constrained decoding for reliable machine-readable output.
Read →Measuring, observing, and governing AI systems in production.
Measuring quality with datasets, scorers, and models grading models.
Read → Live 🔍Observability for LLM apps - spans, tokens, latency, and debugging chains.
Read → Live 🗂️Treating prompts as versioned artifacts: registries, rollouts, and rollback.
Read → Live 🛡️Input/output filters, schema enforcement, and safety checks around the model.
Read → Live 🔒Detecting, redacting, and containing sensitive data across the AI pipeline.
Read → Live 🔐OWASP LLM Top 10, injection and exfiltration defenses for AI apps.
Read → Live ⚖️Policies, model risk management, audits, and regulatory compliance.
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