Attention Is All You Need

The Transformer architecture changed everything by enabling parallel processing and efficient scaling. This became the foundation for all modern LLMs.

BERT

Introduced bidirectional context and made pretrain → fine-tune the new standard for NLP tasks.

GPT-3 & Scaling Laws

Showed that simply scaling model size unlocks surprising emergent capabilities, such as few-shot learning.

T5 & Text-to-Text

Unified NLP tasks under one generalized framework — making model usage simpler and more flexible.

RAG, LoRA & Instruction Tuning

• RAG combined retrieval with generation for better factual grounding
• LoRA made fine-tuning large models cost-efficient
• Instruction tuning made models understand and follow natural language instructions

Chain of Thought & Self-Consistency

Prompting techniques enabled step-by-step reasoning and improved reliability on complex tasks.

DeepSeek-R1 & Structured Reasoning

A shift toward models that reason, not just autocomplete — paving the way for AI that can analyze, plan, and validate its own answers.