// 16 playbooks · updated 2026-09

What are you trying
to decide?

Pick the question you are stuck on. Every route ends in a defensible decision, a checklist, or a passed exam — not a reading list. Nothing to install, nothing to sign up for.

Built by Tarek Atwan — twenty years in data and AI, four books, four-time Pluralsight Elite instructor, Fortune 500 engagements across eight countries. These are the calls that came up often enough to be worth writing down.

// Start from a question Or press / to search every decision inside every playbook
02 Decide if it is worth building Is this a real AI problem, and should you build, buy, or kill it? 04 Pick the model and the method Which algorithm, which features, which data foundation. 02 Measure whether it works The right metric for classical models and for LLMs. 04 Build with LLMs RAG, fine-tuning, LoRA, quantization — and when not to. 05 Pass a certification AWS AIF-C01, AIP-C01 and Microsoft DP-700.
// The index — all 16
IDPlaybookTypeTopicTime
00 GenAI Foundations Navigator Beginner-friendly tour of GenAI basics: grounding, RAG components, prompt engineering and core parameters. Decision tool GenAI basics 10–15 min
01 Algorithm Selector Interactive decision tree for finding the right ML algorithm for your problem and data. Decision tool Modeling 15 min
02 Feature Engineering Playbook Encoding, scaling, outliers, feature creation and selection across tabular, text, time-series and image data. Decision tool Features 20 min
03 Metric Decision Tree Choose the right evaluation metric for classification, regression and beyond. Decision tool Evaluation 20 min
04 Data Engineering Foundations Vendor-neutral course through modern data engineering, from source systems to trusted decisions. Course Data platform 10 modules
05 AWS AI Practitioner Guide Study guide with flashcards and quizzes for the AWS Certified AI Practitioner exam. Exam prep AWS AIF-C01 30–45 min
06 AIF-C01 Practice Exam Timed practice exams, 114 questions across all 5 AIF-C01 domains. Exam prep AWS AIF-C01 30–60 min
07 Visual References Diagrams for confusion matrices, RAG pipelines and the LLM lifecycle. Exam prep Diagrams 10 min
08 AIP-C01 Prep Hub 6 interactive tools for the AWS AI Practitioner (Generative AI) exam. Exam prep AWS AIP-C01 6 tools
09 DP-700 Study Guide Study guide for the Microsoft Fabric Data Engineering Associate exam. Exam prep Microsoft Fabric 45 min
10 AI Opportunity Scorer Evaluate AI opportunities across 6 value dimensions for a Build, Buy, Experiment or Kill verdict. Decision tool Product strategy 10 min
11 AI Solution Navigator Navigate from business problem to solution across rules, ML, deep learning, GenAI, agents and OR. Decision tool AI landscape 10 min
12 GenAI Technique Selector Choose the right LLM optimization: RAG, fine-tuning, LoRA, quantization and more. Decision tool LLM optimization 25 min
13 FM Evaluation Metrics Choose LLM evaluation metrics: perplexity, ROUGE, BERTScore, RAGAS, LLM-as-judge. Decision tool LLM metrics 20 min
14 RAG Academy Interactive course on Retrieval-Augmented Generation concepts, architectures and deployment. Course RAG Course
15 Applied NLP — CSE 8803 Weekly study notes on NLP fundamentals from Georgia Tech OMSA, with live demos. Course NLP 7 weeks
// Suggested paths Three routes that pair the playbooks into a sequence

New to ML

Build the vocabulary, then make your first real modelling decisions.

  1. GenAI Foundations Navigator
  2. Algorithm Selector
  3. Feature Engineering Playbook
  4. Metric Decision Tree

Building with LLMs

Work out what to optimize, then how to know whether it worked.

  1. GenAI Foundations Navigator
  2. GenAI Technique Selector
  3. RAG Academy
  4. FM Evaluation Metrics

Sitting a certification

Study, then sit timed practice under real exam distribution.

  1. AWS AI Practitioner Guide
  2. AIF-C01 Practice Exam
  3. Visual References
  4. AIP-C01 Prep Hub
// About this lab

Every playbook here started as a question someone asked me in a workshop, a design review, or a consulting engagement — the kind where the honest answer is “it depends,” and the useful answer is knowing exactly what it depends on. Each one is a guided decision flow that makes those dependencies explicit and hands you back a recommendation with its reasoning attached.

They are deliberately small. Drop in for ten to thirty minutes, answer a few focused questions, and leave with something you can put in a design note, a study plan, or a team decision record.

I write and teach at tarekatwan.com and consult through Ensemble Methods. The source for everything here is on GitHub.

16
Playbooks
500+
Decision nodes
100%
Browser based
$0
No sign-up
// What’s new
Site-wide search across every decision node · Data Engineering Foundations · RAG Academy · CSE 8803 Applied NLP study notes · DP-700 study guide · AI Opportunity Scorer · AI Solution Navigator