Clawdemy Lessons

Brief overview of the LLM-as-a-Judge lesson: what LaaJ is, pointwise vs pairwise, why it works, and the three biases plus prerequisites and time.

Brief for the reasoning-models lesson: what they are, how they differ from standard LLMs, the benchmarks (AIME, GSM8K, HumanEval, SWE-bench), and Pass@K.

Phase 1 brief on why models need word order added explicitly, plus the learned and sinusoidal position schemes the 2017 transformer paper proposed.

A brief on function calling: how it gives an LLM structured data and actions, the three-stage mechanism, what the model sees, and how it is trained.

Course brief for the agent loops lesson: scope, prerequisites, learning outcomes, and how it fits as the Phase 6 closer in AI Foundations.

Brief for the Phase 5 closer on chain-of-thought prompting: what you'll learn, the prerequisites, learning outcomes, and where CoT fits before Phase 6.

A brief for the reward-model lesson: how preference pairs fill the gap SFT leaves, where this fits in the RLHF arc, prerequisites, and learning outcomes.

Brief for the scaling-laws lesson: how the Kaplan and Chinchilla results give the 'pretraining works because of scale' claim its empirical foundation.

Brief for the Phase 3 closer on precision: what floating-point bits mean, why lower precision saves memory and compute, plus quantization and mixed precision.

Brief for the parallelism and Flash Attention lesson: prerequisites, learning outcomes, and how the four memory tricks fit into Phase 3 pretraining.

Brief for the pretraining lesson, Phase 3 opener: next-token prediction at internet scale, the data sources, one worked training step, and scale figures.

Brief for the lesson on how modern LLMs inject position into attention: the input-to-attention shift, T5 relative bias, ALiBi, and the RoPE intuition.

Brief for the normalization lesson: what LayerNorm does, why transformers prefer it to BatchNorm, the post-norm to pre-norm shift, and the move to RMSNorm.

Brief for the RAG lesson: what retrieval-augmented generation is, the pipeline, bi-encoder and cross-encoder retrieval, HyDE, and indirect prompt injection.

Brief for the prompting lesson: what a prompt is at the token level, what system prompts do, prompt injection, and where it sits in Phase 5 of Track 5.

Brief for the encoder-decoder and T5 lesson: the T5 family (T5, mT5, byT5), span corruption pretraining, and why decoder-only models eventually won out.

Brief for the lesson on BERT's two derivatives: DistilBERT shrinks BERT via distillation; RoBERTa retrains it without NSP for better quality.

Brief for the attention efficiency lesson: prerequisites, learning outcomes, and how sliding window attention and the MHA, MQA, GQA progression fit Phase 2.

How supervised fine-tuning (SFT) bridges a base model to an assistant: what it changes (response shape) versus keeps (knowledge), plus LoRA and its limit.

Brief for the transformer block lesson: what you'll learn, where it fits in Phase 2, prerequisites, and the read and practice time.

Brief for the multi-head attention lesson: why one head must choose, the split-run-concatenate pattern, the 12-head 768-dim flow, plus MQA, GQA, MLA variants.

Brief for the text generation lesson: what it covers (autoregressive prediction loop, decoding strategies, KV caching), prerequisites, outcomes, and timing.

Overview of the embeddings lesson: how token IDs become meaning-carrying vectors, where it fits in Phase 1, prerequisites, and read and practice time.

Overview of the tokenization lesson: why models read tokens not words or letters, what byte-pair encoding does, where it fits in Track 5, and learning goals.

Overview of the self-attention lesson: what it covers, where it fits in Track 5 Phase 2, the prerequisites, and the learning outcomes before you start.