LeetLLM
Tracks
Beginner23 lessons

AI Engineer Foundations

Start from software and math basics, then build toward first LLM applications without guessing the prerequisite order.

Software engineers, students, and career switchers new to ML systems.

You can read model diagrams, run Python experiments, call LLM APIs, and explain the first full AI app path.

  1. 1Git, Shell, Linux for AIBuild a reproducible AI repo with safe Git defaults, a clean-clone eval gate, shell diagnostics, and Linux process habits.Computing FoundationsEasy11 min
  2. 2Docker for Reproducible AIPackage the access-request scorer in a portable Docker image with a small build context, runtime data and secrets, a non-root user, and a reproducible gate.Computing FoundationsEasy11 min
  3. 3Python for AI EngineeringLearn Python as the first AI engineering loop: read JSONL rows, validate fields, compute exact-match accuracy, and harden the scorer with pytest, prompt snapshots, leakage checks, seeded runs, and a CI gate.Computing FoundationsEasy14 min
  4. 4NumPy and Tensor ShapesLearn NumPy shape reasoning from first principles: name axes, predict indexing and broadcasting, reduce safely, distinguish reshape from transpose, and add shape guards.Computing FoundationsEasy15 min
  5. 5Data Structures for AIChoose lists, inverted indexes, heaps, queues, and caches by the operations an AI system must serve.Computing FoundationsEasy12 min
  6. 6SQL and Data ModelingTurn an in-memory support retriever into durable SQL tables. Create rows and keys, query with parameters and joins, enforce permissions, roll back failed work, deduplicate retries, inspect indexes, and see where pgvector fits.Computing FoundationsEasy13 min
  7. 7Dataset Pipelines and Data QualityBuild versioned AI datasets with schema gates, grouped splits, contamination checks, and auditable receipts.ML Algorithms & EvaluationMedium16 min
  8. 8Gradients and BackpropLearn why training works by nudging one response-latency weight, tracing and summing chain-rule paths, checking gradients, and confirming them with PyTorch.Math & StatisticsEasy15 min
  9. 9Vectors, Matrices & TensorsTurn one gradient vector into batches of model inputs while learning dot products, matrix transforms, tensor axes, and shape debugging.Math & StatisticsEasy11 min
  10. 10Probability for Machine LearningUse one API abuse-risk detector to learn events, priors, conditional probability, independence, Bayes rule, and base-rate mistakes.Math & StatisticsEasy15 min
  11. 11Statistics and UncertaintyEstimate abuse risk in a flagged review queue from finite labels, using bootstrap intuition, score intervals, sampling bias checks, and calibrated reporting.Math & StatisticsEasy11 min
  12. 12Neural Networks from ScratchTrace an incident-risk network from one neuron to a batched NumPy forward pass, then diagnose activation, shape, scale, and numerical-stability failures.Preparation & PrerequisitesEasy12 min
  13. 13Training & BackpropagationFollow a decode-latency model through prediction, loss, gradients, parameter updates, scalar autograd, mini-batches, validation checks, and PyTorch.Preparation & PrerequisitesEasy13 min
  14. 14PyTorch Training LoopsBuild a PyTorch classifier from raw logits through autograd, validation, and reloadable checkpoints.ML Algorithms & EvaluationMedium17 min
  15. 15The Transformer Architecture End-to-EndTrace an incident note through masked attention, a decoder block, and next-token logits with readable NumPy and PyTorch code.Preparation & PrerequisitesEasy13 min
  16. 16Language Modeling & Next TokensLearn how next-token prediction becomes a trainable language model, from bigram counts and neural n-grams to causal Transformer generation and KV-cache serving.Preparation & PrerequisitesEasy21 min
  17. 17From GPT to Modern LLMsTrace how decoder-only models grew into modern LLMs, then inspect scaling, instruction tuning, open weights, MoE, and serving tradeoffs with runnable examples.Preparation & PrerequisitesEasy22 min
  18. 18BPE, WordPiece, and SentencePieceBuild a small subword tokenizer, compare BPE, WordPiece, and SentencePiece, then audit token cost and Unicode behavior.Core LLM FoundationsMedium19 min
  19. 19Static to Contextual EmbeddingsTurn token IDs into vectors, learn what nearby usage captures, and see why a word such as charge needs sentence-dependent representations.Core LLM FoundationsMedium15 min
  20. 20Prompt Engineering FundamentalsBuild and test grounded prompts with clear roles, few-shot examples, structured outputs, evidence checks, and failure-focused evaluation.Preparation & PrerequisitesEasy18 min
  21. 21Calling LLM APIs in ProductionTurn a grounded prompt into a reliable API boundary with server-side secrets, typed results, bounded retries, safe actions, and useful telemetry.Preparation & PrerequisitesEasy17 min
  22. 22First AI App End-to-EndShip one traceable rotation-decision workflow: validated input, model boundary, stored status, clear UI states, failure tests, and deploy checks.Preparation & PrerequisitesEasy15 min
  23. 23LLM Benchmarks & LimitationsBuild an evaluation suite for a policy-answering LLM: score evidence use, understand public benchmark contracts, control judge bias, and make release decisions from private tests.Core LLM FoundationsMedium21 min