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AI Engineer’s Playbook

A pragmatic field guide for software engineers and IT professionals stepping into AI/ML.

No hype. No “journey” metaphors. No “democratizing” anything. Just facts, patterns, code that runs, and the occasional “here’s what everyone gets wrong.”

Table of Contents

Foundations

  1. Probability & Statistics for Engineers — Your Bayesian intuition is probably broken. Let’s fix it.
  2. Linear Algebra for Engineers — Matrices are just functions. Stop pretending they’re magic.
  3. Optimization & Calculus for ML — Gradient descent is just walking downhill. Your intuition fails at 100D, so here’s code instead.

LLM Fundamentals

  1. Transformers Demystified — The transformer is not a neural network. It’s a composition operator. Here’s why that distinction saves you three weeks of debugging.
  2. LLM Training Lifecycle — Pretraining is expensive. Fine-tuning is easy. Both are wrong. The actual pipeline: data curation → pretraining → SFT → RLHF/DPO → monitoring.
  3. Tokenization & Context Windows — Your 128K context window is a lie. Understanding tokenizers, subword fragmentation, and context economics.

Prompting (It’s Not Magic, But It’s Not Nothing)

  1. Prompt Engineering Patterns — Zero-shot, few-shot, CoT, ReAct — patterns that actually work and ones that don’t.
  2. Structured Outputs & Function Calling — JSON is a schema. Functions are APIs. Here’s how to make LLMs use them reliably.
  3. Guardrails & Safety — Your LLM will argue and lie. Here’s how to constrain it without making it useless.

RAG (Retrieval, Not Magic)

  1. RAG Architecture & Chunking — RAG is just Google with a neural network. Here’s what actually makes it work.
  2. Embedding Models & Vector Databases — Your embeddings are not vectors. They’re probability distributions in disguise.
  3. RAG Debugging & Evaluation — RAG isn’t broken. Your evaluation is. Here’s how to actually test it.

Fine-Tuning (When It’s Actually Worth It)

  1. Fine-Tuning Fundamentals — You probably don’t need fine-tuning. Here’s how to tell when you do.
  2. LoRA & QLoRA in Practice — Low-rank adaptation for engineers who don’t care about the math (but need the results).

AI Engineering Tools

  1. Orchestration Frameworks — LangChain is a dependency tree. LlamaIndex is RAG-specific. DSPy is the weird one. Here’s when to use which.
  2. Model Serving & Inference — Inference is not deployment. Here’s latency/throughput/cost without the marketing copy.
  3. Agent Frameworks — Agents are just programs with tool access. Most patterns are overcomplicated. Here’s what actually works.

MLOps & Production

  1. Training Pipeline Engineering — Training pipelines are infrastructure problems with a math hat.
  2. Monitoring & Drift Detection — Your model works. Until it doesn’t. Here’s how to catch it before your users do.
  3. Cost Engineering for AI Systems — AI is expensive. Here’s how to not go broke building with it.