@dkare1009: Most AI engineers learn from scattered blog posts and outdated tutorials. One guidebook just consolidated everything. T…

Most AI engineers learn from scattered blog posts and outdated tutorials. One guidebook just consolidated everything. The AI Engineering Guidebook covers the full stack of modern AI system design. I've shipped 50+ production agents. This is the reference I wish existed when I started. What's inside: 𝗟𝗟𝗠 𝗙𝗨𝗡𝗗𝗔𝗠𝗘𝗡𝗧𝗔𝗟𝗦 → Transformer and MoE architectures → Pre-training, Instruction tuning, RLHF, GRPO → Next-token prediction mechanics 𝗗𝗘𝗖𝗢𝗗𝗜𝗡𝗚 & 𝗣𝗥𝗢𝗠𝗣𝗧𝗜𝗡𝗚 → Temperature, Top-p, Top-k explained → Chain of Thought, Tree of Thoughts, ARQ → Beam Search, Contrastive Search, SLED 𝗙𝗜𝗡𝗘-𝗧𝗨𝗡𝗜𝗡𝗚 (𝗣𝗘𝗙𝗧) → LoRA, QLoRA, DoRA, VeRA, Delta-LoRA → Model distillation patterns → When to fine-tune vs. prompt 𝗥𝗔𝗚 𝗔𝗥𝗖𝗛𝗜𝗧𝗘𝗖𝗧𝗨𝗥𝗘𝗦 → HyDE, Corrective RAG, Graph RAG → Adaptive RAG, REFRAG → Cache-Augmented Generation 𝗔𝗚𝗘𝗡𝗧𝗜𝗖 𝗦𝗬𝗦𝗧𝗘𝗠𝗦 → ReAct pattern deep dive → MCP, Agent2Agent, AG-UI protocols → Memory types: semantic, episodic, procedural 𝗗𝗘𝗣𝗟𝗢𝗬𝗠𝗘𝗡𝗧 & 𝗘𝗩𝗔𝗟 → vLLM, PagedAttention, continuous batching → Quantization and pruning → DeepEval, Opik for observability The barrier to production AI knowledge used to be: → Piecing together 50 different sources → Outdated courses teaching last year's patterns → Trial and error on your own dime Now it's one guidebook. This is the curriculum for building AI systems that actually ship. Which section are you diving into first? Currently building at Persyn and few other fun AI first projects. http://Persyn.ai is a no-camera content studio that lets creators train an AI persona on a few photos and generate studio-quality TikTok UGC, Meta ads, and Instagram stories in 30 seconds flat. Follow for more production AI resources. Repost if someone in your network is building AI systems. Credit: Daily Dose of Data Science for putting this together.