Deep-dive guides and courses for software engineers who need to build production AI systems — not just mess around with prompts.
Each PDF covers one engineering topic in depth. Think of it as a technical book — but focused on what actually works in production. No padding, no theory for its own sake.
Full access to the complete library — all current guides, all courses, and every new release the moment it drops. One subscription, no friction.
Most AI courses are built by educators. We build by engineers who've run production AI systems at scale — and know exactly what breaks, what costs too much, and what actually matters.
No toy examples. Every technique is one that's been used in a system with real users, real costs, and real consequences when it fails.
RAG vs. fine-tuning. Open source vs. hosted. Single-agent vs. multi-agent. We explain the actual costs, latency, and maintenance burden of each choice.
Built for engineers who already know how to code. We skip the "what is an LLM" and go straight to how to integrate it, evaluate it, and operate it at scale.
AI moves fast. When a tool changes, a technique gets deprecated, or a new pattern emerges — the guide gets updated. Subscribers get the new version automatically.
Guides and courses in the works. Each one is a definitive resource on its topic — not a summary of what's already been written elsewhere.
Engineering patterns, cost controls, evaluation strategies, and team practices for deploying AI agents in production systems. Not prompting tips — the real architecture decisions.
Build a production-grade retrieval-augmented generation system. Chunking strategies, embedding models, vector DB selection, re-ranking, and latency optimization.
How to add LLM capabilities to a SaaS product: API design, cost management, rate limiting, fallback strategies, and how to avoid the common pitfalls that sink first attempts.
不再是黑箱。构建评估框架：基础指标、LLM-as-judge、人类反馈循环、A/B测试，以及如何在生产中监控模型漂移。
When to fine-tune vs. prompt engineer vs. RAG. Practical guide to dataset curation, training infrastructure, evaluation, and deployment — with real cost estimates.
We've seen it at Livly, in every company we've built, and in every engineering team we've worked with. The resources to actually learn this — written for engineers, by engineers — are sparse. That's why PilotStack exists.