Your question is Evolution of LLM Methods. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
Explain the evolution of LLM algorithms and methods.
Cover the progression from statistical language models and word embeddings through RNNs, sequence-to-sequence models, attention, transformers, pretrained language models, instruction tuning, reinforcement learning from human feedback, retrieval-augmented generation, and current reasoning or agentic methods. For each stage, explain the core algorithm, the limitation it addressed, and the trade-offs it introduced. Include a small Python demonstration comparing a traditional language-model baseline with transformer fine-tuning, and describe how you would evaluate quality, factuality, robustness, and computational cost.
