5 papers every AI engineer should read 📚 Tools change. Ideas last. These five explain most of what you use today: 1️⃣ Attention Is All You Need (2017): the transformer arxiv.org/abs/1706.03762 2️⃣ Language Models are Few-Shot Learners (2020): GPT-3 and in-context learning arxiv.org/abs/2005.14165 3️⃣ Training language models to follow instructions with human feedback (2022): InstructGPT and RLHF arxiv.org/abs/2203.02155 4️⃣ Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (2022) arxiv.org/abs/2201.11903 5️⃣ DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning (2025) arxiv.org/abs/2501.12948 Tip: read the abstract, then the figures, then the method. Don’t start at page one. Save this for later 🔖 Which paper should I break down first? awomanindatascience #AI #MachineLearning #LLM #AIEngineering #DeepLearning Transformers ReasoningModels