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- Attention Is All You NeedThe paper that introduced the Transformer — the architecture underneath GPT, BERT, Claude, and Gemini. Self-attention, Query/Key/Value, multi-head attention, positional encoding, and the real BLEU results, explained in depth with analogies and animated visuals, not just equations.Read →
- BERTThe paper that made "pre-train once, fine-tune anywhere" the default for NLP. Bidirectional context, masked language modeling, next-sentence prediction, the [CLS]/[SEP] tokens, and the GLUE sweep — explained with analogies, not just equations.Read →
- Retrieval-Augmented GenerationThe paper behind every "chat with your docs" product. Pair a generator with a neural retriever so the model looks knowledge up instead of memorizing it — dense retrieval, top-k documents, RAG-Sequence vs RAG-Token, and why retrieval cuts hallucination.Read →
- Chain-of-Thought PromptingShow a model a few worked examples with their reasoning and it starts solving problems it used to fail. Why reasoning emerges only at scale, the GSM8K jump, zero-shot "Let's think step by step", and how CoT seeded modern reasoning models.Read →
- LoRAThe paper that made fine-tuning affordable. Freeze the giant pretrained model and train two tiny low-rank matrices instead — up to 10,000x fewer trainable parameters, no extra inference latency, and swappable per-task adapters. The road to QLoRA and PEFT.Read →
- ReActThe blueprint for AI agents. Interleave reasoning with actions so a model can think, use a tool, observe the result, and think again — the Thought–Action–Observation loop that grounds reasoning, cuts hallucination, and underpins every tool-using agent.Read →