LLM Study Plan

What are LLMs, history of language models, key architectures, scaling laws, applications, and limitations.

Byte-pair encoding, WordPiece and SentencePiece tokenizers, token vocabularies, word embeddings, and positional encodings.

Self-attention mechanism, multi-head attention, feed-forward networks, layer normalization, residual connections, and full transformer blocks.

Language modeling objectives, causal vs masked language modeling, training data curation, infrastructure, and scaling strategies.

Transfer learning for LLMs, full fine-tuning, parameter-efficient methods like LoRA and QLoRA, instruction tuning, and data preparation.

Reward modeling, PPO for language models, Direct Preference Optimization, Constitutional AI, and preference data collection.

Zero-shot and few-shot prompting, chain-of-thought reasoning, system prompts, and structured output generation.

Vector databases, embedding models, chunking strategies, RAG pipelines, and advanced retrieval techniques.

Tool use patterns, ReAct framework, multi-agent systems, memory architectures, and agent frameworks.

Perplexity, BLEU and ROUGE metrics, human evaluation, popular benchmarks, and red teaming strategies.

Vision-language models, audio integration, cross-modal attention, CLIP and its variants, and multimodal applications.

Hallucination detection and mitigation, bias in language models, toxicity filtering, guardrails, and responsible AI practices.

Model quantization, knowledge distillation, serving infrastructure, inference optimization, and cost management.