A language model does one thing: given the preceding text, output a distribution over the next word. At large enough scale, the way to control it becomes changing the input: a few examples (in-context learning), or asking it to write its reasoning first (chain-of-thought; the example cited in class is one sentence, Let's think step by step, taking an arithmetic task from 18% to 80%). But following instructions and saying 'I don't know' do not come from next-word prediction alone; instruction tuning and RLHF add them.
Lecture 10 of Harvard CS 2881R (Fall 2025) brought in four researchers from OpenAI, Anthropic, and Google DeepMind to cover two ways of catching a model misbehaving: read the chain of thought it writes, or read its activations. CoT monitoring catches reward hacking far better than watching actions alone, but put the monitor into the training reward and the model learns to hide its intent. On the activation side, persona vectors track personality drift, and the Sonnet 4.5 audit showed that suppressing the 'I am being tested' direction makes bad behavior more frequent. Neel Nanda's takeaway was the most practical: simple steering vectors often beat SAEs, so always compare against baselines.
In week 14 of Fall 2025, Hung-Yu Kao closed the course with two slide decks. Course_summary sorts the semester into three columns (NLP Fundamentals, NLP Models, NLP Advances), adds two columns for the TA labs, and lists five directions for further study. The second deck is his notes on Denny Zhou's (Google DeepMind) April 2025 Stanford talk. Its claim: pretrained models can already reason, and decoding is what brings it out. CoT decoding, self-consistency and retrieval + reasoning add up to four inequalities. The Fall 2026 W16 'Reasoning / Agent' unit has not been released yet.
With pre-training data close to exhausted, models now improve by thinking more before they answer. Lecture 12 of NTU ADL covers this in two halves. The first is test-time scaling: the model stays fixed and inference spends more compute, through short CoT, parallel sampling followed by selection (majority voting, answer confidence, an outcome reward model), step-level scoring with a process reward model combined with search, and sequential revision. The second is learning to reason: the weights are updated, with reasoning data written by people, generated by the model and filtered, or produced by a stronger teacher model, or with reinforcement learning that only tells the model whether its answer was right, as in DeepSeek-R1-Zero.
CME295 Lecture 3 defines an LLM as a decoder-only next-token predictor, uses MoE to explain why a huge model only touches part of its weights per token, and spends most of its time on the knobs you can turn at generation time: greedy, beam search, top-k, top-p, temperature, guided decoding, plus three prompting techniques (few-shot, chain of thought, self-consistency). The 2026 edition folds this lecture into Lecture 2, prompting is no longer a separate syllabus item but still gets a brief segment in the recording.
The Spring 2023 edition of CS224U defines in-context learning as a frozen language model performing a task only by conditioning on the prompt, and warns that the second condition of few-shot learning (no examples of the behavior seen in training) is almost impossible to verify. Potts's 38-page deck runs from GPT-2's TL;DR trick through choosing demonstrations, chain of thought, self-consistency, and DSP, and ends with four recommendations: build dev/test sets first, learn your target model's instruction format, and treat prompt writing as AI system design. Mina Lee's guest lecture asks the reverse question: who should learn to read prompts, people or models?
2022 was the year diffusion models took center stage, Chinchilla scaling laws rewrote large-model training, and Chain-of-Thought turned reasoning into an ability that prompts could elicit. NeurIPS passed 10,000 submissions; three of its 13 Outstanding Papers directly concerned diffusion; and Chinchilla and data pruning both challenged the belief that bigger was always better. On the eve of ChatGPT’s release, every required piece fell into place at that year’s conferences.
2022 marked NLP’s shift from demonstrating model capabilities toward aligning and controlling them. InstructGPT brought RLHF into the mainstream, Chain-of-Thought showed that prompts could unlock reasoning, and Flan 2022 matured instruction-tuning methodology. ACL and NAACL adopted ARR as their sole review path, exposing infrastructure and reviewer-load problems. ChatGPT launched at year-end and rewrote the rules of NLP research.
2022 was a turning point at major AI conferences. Diffusion models moved from emerging to mainstream, with two NeurIPS Outstanding Papers; Chinchilla rewrote scaling laws; Chain-of-Thought showed that large models could reason; and InstructGPT used RLHF to teach language models to follow instructions. When ChatGPT launched at year-end, these academic topics instantly became global news.
Good prompts aren't written in one go — they're iterated into existence. Start with the simplest prompt, test with real cases, classify error types, and make targeted fixes. This article covers the three-part System Prompt structure, reasoning framework selection, few-shot optimization, token budget management, and six common mistakes.