With a small labeled dataset and an LLM with billions of parameters, CMU 10-423 offers two routes: supervised fine-tuning, or putting the examples in the prompt for in-context learning. L10 first notes that the 2023 consensus was that fine-tuning usually wins, then covers four ways to tune only a few parameters: the top layers only, adapters, prefix tuning, and LoRA. The first half of L11 returns to in-context learning: how sensitive it is to example order and label balance, how to pick a prompt, and what chain-of-thought is. HW3's written questions and its LoRA programming task both draw on these two lectures.
The first half of L06 is about ethics. Yen-Lung Tsai quotes Karpathy's line that hallucination is a feature of LLMs, then works through plagiarism, whether your data gets used for training, and DeepSeek's censorship and corpus skew, and closes with seven principles of responsible use. The second half is about applications: give the model the right information and clear instructions, and one system prompt becomes a Lucky Vicky positivity generator, a social-media copywriter, or a biased college-major counselor. The week-6 assignment moves that prompt into an OpenAI-compatible API with a Gradio front end: a chatbot with a persona.
L09 defines an AI agent in one line: the AI finishes the work you would otherwise do yourself. Yen-Lung Tsai follows Andrew Ng's four design patterns (Reflection, Tool Use, Planning, Multiagent Collaboration) but builds only the two easiest. Demo07a hands a draft between a "writer" and a "reviewer" LLM call. Demo07c splits the Lucky Vicky post generator into "think of five reasons, then write the post", a two-stage CoT. Both use AISuite with Groq and a Gradio front end. LangChain, AutoGen and CrewAI appear only on a further-learning list. The week 9 homework asks you to pick one of the two patterns.
This 34-slide TA session answers a practical question. Pasting data into the ChatGPT web page one row at a time is slow and hits hourly limits, so research and homework should use the API. The notebook runs one SemEval 2014 entailment example through Gemini, Claude, and OpenAI in turn: prompts live in prompts.yaml, output is forced into JSON, then few-shot and token counting. The material is from 2024. The slide cover says 2024/11/21, and the notebook uses gemini-1.5-pro, gpt-4o, and claude-3-5-sonnet-20241022. That Claude model was retired on 2025-10-28, and Google's old Gemini SDK reached end of support on 2025-11-30.
Lecture 6 of ADL Fall 2025 sorts pre-trained models into three families: encoders (the BERT family, bidirectional context), decoders (the GPT series, good at generation), and encoder-decoders (BART and T5, pre-trained with denoising). It then names two practical obstacles of the pre-trained-model era: downstream labeled data is scarce, and models keep growing until one copy per task no longer fits. The slides' answer is prompt learning. GPT-3's in-context learning shows a model can do a task without updating parameters; hand-written hard prompts (template plus verbalizer, LM-BFF) then give way to soft prompts optimized as vectors (P-Tuning, Prefix-Tuning, Prompt Tuning); and Liu et al.'s prompting typology closes the lecture.
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, and the prompting half disappears from the syllabus.
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?
An agent with a workspace_browse tool said 'file not found' instead of searching. Anthropic, OpenAI, and Google's official guides all point to the same fix: put trigger conditions and workflows in the tool description. A 2025 study found 97.1% of MCP tool descriptions have quality issues.
DeepSeek Harness (dsh) uses an everything-is-a-plugin architecture and hit 214K stars in 3 weeks; ponytail proves with real benchmarks that one skill can cut Claude Code's code output by 54%; Magnitude auto-picks and tunes local models for your coding agent; wigolo gives agents API-key-free web search, crawling, and research
Looplane's prompt is now `m3-exact-edit-v4`: the version persists into artifacts; core/tool/interaction/runtime/instructions/skills/workspace/memory are composed as stable or dynamic sections; and positive/negative examples cover replace_text, unified diffs, and direct replies. Unit tests pin the structure, while live-eval coverage still needs expansion.
Writer sees the first 8 candidates for a factual query or 12 for a recommendation by default. Citations must use an exact `source_url` from that set, and weak or empty retrieval triggers an instruction to abstain rather than fill gaps from model knowledge.
Looplane resolves user and root-to-leaf project instructions before rendering named prompt sections for runtime, skills, workspace state, and the latest 20 explicit memories. The pipeline is traceable and reloadable, but it is not semantic memory and repository text does not become system authority.
A complete study guide for Claude's official architect certification (CCAR-F): five domains weighted 27/18/20/20/15, four scenarios drawn from six, common anti-patterns, and hands-on preparation. Official specs are 60 items / 120 minutes / $125 / 12-month validity / 720 to pass; registration is limited to Claude Partner Network members, and on-time renewal is free and non-proctored.
CCAO-F is the cheapest of Anthropic's four exams ($99, 60 items, 120 minutes), aimed at people who work with Claude rather than build against it. The heaviest of its seven domains is Output Evaluation and Validation at 21% — spotting hallucinations, deciding when human review is required, and adapting outputs — with Governance, Risk, and Responsible Use at another 15%. Anthropic states plainly that it is not for developers building against APIs or designing agentic systems. One easily missed limitation: this credential does not count toward Claude Partner Network tier eligibility, while the other three do.
Most people assume GenAI certifications are built around prompt writing. CCAO-F gives Prompting 14% while Output Evaluation gets 21%; CCDV-F gives Prompt and Context Engineering 11.0%. What actually gets tested is structured output, injection-resistant prompting, dynamic context injection, context compression and caching, prompt lifecycle governance, and proving a prompt change helped — closer to context engineering and software engineering than to writing craft. None of the ten asks you to write a prompt on the spot; they are all multiple choice, so explaining why beats having a feel for it. The single most useful line comes from CCAR-F: when business logic must be guaranteed, 'change the prompt first' is usually the wrong answer.
Fall 2026 compresses a full week of prompting into one bullet here and adds RePPIT (Research, Propose, Plan, Implement, Test) and MCP. Two RePPIT rules are worth stealing outright: always ask for exactly two proposals, and never let the instance that wrote the code review it. On the MCP side, Anthropic measured turning tools into code calls dropping 150,000 tokens to 2,000.
A BCG experiment found a jagged frontier: inside it, AI substantially improved consultants' work; outside it, AI made results worse — and people fell asleep at the wheel. The lecture also takes a strong position: avoid fine-tuning wherever possible, because by the time you're done tuning, the next model already beats your fine-tuned version.
CS230's first lecture is a course overview, but Andrew Ng spends most of it on three things: why scaling works, when prompting stops being enough, and why he thinks 'don't learn to code' is one of the worst pieces of career advice ever given.
In November 2025 three frontier labs jointly broke all 12 previously proposed prompt-injection defenses. EchoLeak's payload passed Microsoft's own dedicated classifier. So the goal is not blocking every attack — it is surviving the ones that land, and that is harness work.
Every official course platform from OpenAI, Anthropic, and Google, plus Stanford CS146S/CS336, Elements of AI, Hugging Face, MIT 6.S191 and more — scraped page by page, then re-sorted into four tiers: AI-curious, vibe coding, shipping to production, and how models actually work. Also covers self-study repos still being updated in 2026 and browser-based platforms that need no local setup, filtered by last-commit date rather than star count. The conclusion: nearly all of it is free. What is scarce is not courses, it is the judgment to pick one. And tier four will not fix your tier three problem.
Loop Engineering is the practice of designing systems that automatically prompt AI agents, rather than prompting them manually. Boris Cherny runs hundreds of agents, Addy Osmani coined the term, and Blake Crosley identified verification cost as the real bottleneck — this article covers primary sources, the five building blocks, applicability boundaries, and criticisms.
Automatic prompt optimization (APO) has evolved from APE/OPRO to GEPA: replacing sparse rewards with linguistic reflection, winning over GRPO by ~6pp with 4-35x fewer rollouts. Meanwhile, tool descriptions are the overlooked prompt -- small wording changes can shift tool selection rates by 10x, and Anthropic's experiments show Claude self-rewriting tool descriptions outperforms human experts. These two lines are converging: eval-driven automatic optimization is eating hand-tuned prompts.
asgeirtj/system_prompts_leaks collects the raw system prompts of 40+ AI assistants, from GPT-5.5 and Claude Opus 4.7 to Gemini 3.1 Pro, with 40.3k stars, 461 commits, and an MIT license. The value isn't in obtaining secrets -- it's in turning vendors' implicit policies into comparable engineering material. What you should study is the design decisions, not the text itself.
Rewriting tool descriptions from soft suggestions to hard rules (whitelist + consequence explanation) eliminated the LLM's incorrect tool selection; adding skip_signal=True fixed vector store double-indexing.
A Skill is a folder with a SKILL.md. Three-layer progressive disclosure lets Claude load details only when needed, eliminating the need to re-explain preferences every conversation.
Every one of Claude Code's 45 tools uses a prompt() method that dynamically adjusts based on user type, feature flags, and system capabilities. Applying this pattern to a ReAct Agent, tool descriptions are dynamically generated along three dimensions: orchestrator model capability, locale, and available tools. Small models automatically get few-shot examples; large models save tokens.
AI engineering has gone through three phases: Prompt Engineering (write better instructions) → Context Engineering (feed the right information) → Harness Engineering (design the entire working environment). Each evolution doesn't replace the previous one — it operates at a higher level of abstraction.
Context Engineering is the core concept that replaced Prompt Engineering in 2025: the focus shifted from 'how to ask' to 'what information to provide.' Delivering the right information at the right time into the context window is more effective than upgrading to a stronger model. This post covers the definition, four key strategies, practical techniques, and common failure modes.
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.
Search found the right documents, but the LLM's answers are still poor — often the problem lies in prompt design. System prompt structure, context formatting, and instruction placement all affect output quality.