AI Engineer Notes

1 of 66 written

Notes

Every note in study order. Work through them top to bottom.

1

Deep learning foundations

Week 1
  1. What is a neural network? ready
  2. Loss functions: measuring how wrong we are to be written
  3. Gradient descent and backpropagation to be written
  4. Optimizers: SGD, Adam, AdamW and the learning rate to be written
  5. Logits, softmax and cross-entropy to be written
  6. PyTorch essentials: tensors, autograd, the training loop to be written
  7. Overfitting, regularization and data splits to be written
2

LLM internals: build GPT from scratch

Weeks 2–4
  1. What is an LLM? Pretraining, fine-tuning and the big picture to be written
  2. Tokenization and byte-pair encoding (BPE) to be written
  3. Token embeddings: turning IDs into vectors to be written
  4. Positional encoding: telling the model about word order to be written
  5. Preparing data: sliding windows and next-token prediction to be written
  6. Self-attention, the simple version to be written
  7. Scaled dot-product attention: queries, keys and values to be written
  8. Causal masking and attention dropout to be written
  9. Multi-head attention to be written
  10. LayerNorm, GELU, feed-forward layers and residual connections to be written
  11. The transformer block and the full GPT architecture to be written
  12. Counting parameters and weight tying to be written
  13. Pretraining: the training loop, cross-entropy and perplexity to be written
  14. Decoding: greedy, temperature, top-k and top-p to be written
  15. Loading pretrained GPT-2 weights to be written
  16. Fine-tuning for classification to be written
  17. Instruction fine-tuning to be written
  18. Encoder, decoder, encoder–decoder: BERT vs GPT vs T5 to be written
3

The modern LLM stack

Week 5
  1. From GPT-2 to modern LLMs: pre-norm, RMSNorm, SwiGLU, grouped-query attention to be written
  2. Rotary position embeddings (RoPE) to be written
  3. The KV cache: why generation is fast to be written
  4. Quantization: 16-bit, 8-bit, 4-bit and GGUF to be written
  5. LoRA, QLoRA and parameter-efficient fine-tuning to be written
  6. Teaching preferences: RLHF and DPO to be written
  7. Context windows and reasoning models to be written
  8. Prompting vs RAG vs fine-tuning: how to choose to be written
4

Retrieval-augmented generation (RAG)

Weeks 6–7
  1. Text embeddings and semantic similarity to be written
  2. Vector databases and approximate nearest neighbour search (HNSW, IVF) to be written
  3. Chunking strategies to be written
  4. Retrieval: BM25, dense, hybrid search and rank fusion to be written
  5. Reranking with cross-encoders to be written
  6. The RAG pipeline end to end to be written
  7. Advanced RAG: query rewriting, HyDE, multi-hop, GraphRAG, agentic RAG to be written
  8. Evaluating RAG: recall@k, MRR, nDCG, faithfulness to be written
  9. RAG failure modes and how to debug them to be written
5

Prompting, tools and agents

Weeks 8–9
  1. Prompt engineering that survives production to be written
  2. Context engineering: what goes in the window to be written
  3. Structured output and function (tool) calling to be written
  4. The agent loop and ReAct to be written
  5. Workflows vs agents: routing, orchestrator–workers, evaluator–optimizer to be written
  6. Model Context Protocol (MCP) to be written
  7. Agent memory: short-term, long-term, compaction to be written
  8. Multi-agent systems: when they help and when they hurt to be written
  9. Prompt injection and agent security to be written
6

Evals and production

Week 10
  1. Evals: golden sets, metrics and regression testing to be written
  2. LLM-as-judge: how to trust a model grading a model to be written
  3. Hallucination: causes, detection, mitigation to be written
  4. Guardrails and safety layers to be written
  5. Cost and latency: caching, batching, streaming, model routing to be written
  6. Serving LLMs: time to first token, throughput, continuous batching to be written
  7. Observability and tracing to be written
7

LLM system design and interview drill

Weeks 11–12
  1. A framework for LLM system design rounds to be written
  2. Design: a customer support assistant to be written
  3. Design: enterprise document search at scale to be written
  4. Design: a coding assistant to be written
  5. The ML coding round: attention, BPE and sampling from memory to be written
  6. Question bank: fundamentals to be written
  7. Question bank: applied AI engineering to be written
  8. Talking about your projects to be written