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Prompt / Context Engineering (Cross-Author Thematic Synthesis)

A fusion of Prompt Engineering from 4 perspectives. newtype Lao Huang (186 posts + 4 production-grade templates + 5-element task decomposition + Thinking-Claude performative thinking + Ultra think three tiers + Karpathy Context Engineering) / Wang Kai (6000+ words on PUA-ing Claude + using AI as a ruler for quantification + risk-scoring prompts + decision interrogation SOP) / Lao D (three-layer prompt architecture: meta/system/user + Markdown imitation writing SOP + 4-step blogger imitation) / Anthropic internal (Cal Rueb CLAUDE.md anti-patterns). Covers the counter-consensus view on "whether Prompt Engineering needs to be learned", Skill-ification as the end state, Prompt scheduling Prompt, and Sequential Thinking scheduling.

Source collection：Cross-source synthesis · Published here：2026-09-26 · Note updated：2026-05-18

AI EngineeringAgent WorkflowsModels and Evaluation

When to open: You want to learn Prompt Engineering / build production-grade Prompt scheduling / understand the Prompt → Context → Skill evolution path / see high-quality prompt templates. This wiki fuses 4 schools of perspective and explicitly marks 3 core disagreements.

One-sentence core:Lao Huang's counter-consensus stance — "Prompt Engineering doesn't need to be learned; studying logic is enough" vs Wang Kai's practical approach — "PUA Claude through repeated feedback until the prompt reaches 6000+ words" vs Lao D's domestic teaching — "Three-layer prompt architecture (meta/system/user) + imitation writing SOP". The root of the disagreements lies in different user identities (product manager / investor / ordinary self-media creator).

0. The four-perspective view of "Prompt / Context Engineering"

Perspective Source Stance One-sentence core
Counter-consensus "no need to learn" school newtype · Prompt / Context Engineering in Practice(Huang Yihe) "For ordinary users, there is absolutely no need to specifically learn prompt engineering" "Prompt is essentially logic / methodology; studying the Pyramid Principle / communication studies is enough"
PUA Claude practical school Wang Kai's Decision-Making System in the AI Era / Wang Kai RSI Investment Practical Case Collection + AI Value Investing Philosophy(Wang Kai) Through repeated analysis → feedback → improvement, prompts can eventually be polished to over 6000 words "Use AI as a ruler to quantify fuzzy variables (risk/value/emotion)"
Structured teaching school dontbesilent AI Self-Media Course / 13-lesson systematic methodology(Lao D) Three-layer prompt architecture: meta prompt → system prompt → agent → user prompt "Domestically, Gemini 2.5 Pro is the first choice for writing meta prompts, and DeepSeek R1 drives the agent"
CLAUDE.md anti-pattern school Claude Code Best Practices (Cal Rueb · Anthropic internal talk)(Cal Rueb) Don't use imperative phrasing like "You must X"; use "We prefer X because Y" LLMs follow instructions with reasons better

Key observation:The four schools' stances seem conflicting, but they actually stem from different user identities:

  • Lao Huang has a marketing background → "I already know how to express logically" → opposes learning Prompt
  • Wang Kai is a product manager → "Investment decisions must be rigorous" → PUA Claude to 6000 words
  • Lao D is a teacher → "Domestic ordinary creators can't express logically" → must teach the three-layer architecture
  • Cal is internal at Anthropic → "Collaborating with engineer colleagues" → use reasons rather than commands

1. Cross-author consensus (6 points)

1.1 The essence of Prompt = task decomposition + Brief writing (5 elements)

Lao Huang newtype · Prompt / Context Engineering in Practice §2 (2025-05 Post 1223) presents the "5 elements of classic prompts":

Element Content
Who are you Role setting (You are a financial analyst / translation expert /...)
What background Context (what skills you have, what resources you have, what domains you understand)
What to do Goal (what task you need to complete)
How to do it Implementation path (how many steps, what method to use, what the constraints are)
Success image Reference cases (what similar good works look like, or few-shot examples)

Why this set covers 90% of scenarios: It is exactly the SOP for "onboarding a new hire".

Lao D corroborates (dontbesilent AI Self-Media Course / 13-lesson systematic methodology §L02):Markdown structured prompts —— use # layering to prevent AI from interpreting multiple reference copy pieces as one.

1.2 Communicating with the model = communicating with a "smart new hire who lacks background"

Lao Huang newtype · Prompt / Context Engineering in Practice §1 (2024-12 Post 661) core metaphor:

"Treat the model as a new hire who writes well but doesn't know how to write Xiaohongshu posts. Just think: how would you teach it? Then experiment and revise repeatedly. "

Key operations:

  1. Treat the other party as a smart new hire who lacks background, not a search engine or encyclopedia
  2. Tell it what you want (goal + success image), not just keywords
  3. Provide necessary background (industry, style, boundaries, what to avoid)
  4. Experiment repeatedly: if it doesn't work once, revise the Prompt, don't call the model stupid

Wang Kai corroborates ("PUA Claude"Wang Kai Claude Code Terminal Workflow Methodology (Full Version) §2):"Through repeated analysis → feedback → improvement, prompts can eventually be polished to over 6000 words".

1.3 Skill-ification is the end state (Lao Huang + Lao D consensus)

Lao Huang newtype · Prompt / Context Engineering in Practice §Conclusion:Once any prompt + workflow is stable enough, turn it into a Skill.

Lao D dontbesilent AI Self-Media Course / 13-lesson systematic methodology §L13:

"Skill = cross-platform reusable prompt engineering product / SKILL.md + references/ + Git version control"

See Claude Skill Engineering (Cross-Author Thematic Synthesis).

1.4 Sequential Thinking + Ultra think are the hard skills of Prompt Engineering

Keyword Thinking Token
Ultra think / ultrathink 32K(HIGHEST)
think hard / think harder 10K(MIDDLE)
think 4K(BASIC)

Lao Huang's practical principle(2025-07 Post 1558): If Claude Code answers wrong once,first add "Ultra think" and retry; 90% of the time it's due to insufficient budget, not the model being bad.

Supporting Sequential Thinking MCP(default must-have for Lao Huang) —— see MCP Protocol Ecosystem (Cross-Author Thematic Synthesis) §1.3.

1.5 Prompt scheduling Prompt (Workflow mindset)

Lao Huang newtype · Prompt / Context Engineering in Practice §6 (2025-06 Post 1381 highlight):

任务开始
  ↓
模型从 Prompt House MCP 拿到 "深度研究" 提示词
  ↓
这份 Prompt 要求:用 Sequential Thinking + Tavily 至少 3 轮迭代
  ↓
完成后,模型从 Prompt House 拿 "总结报告" 提示词
  ↓
最终输出

Core:One prompt can elicit another prompt. Multiple Prompts + multiple MCPs combined = a lightweight Workflow, sufficient for complex tasks.

Why this is the ceiling of Prompt Engineering:

  • A single Prompt template can only handle single-step tasks
  • The "Prompt → Prompt" chain = a cross-step, cross-tool Workflow
  • More lightweight than complex frameworks like LangChain

1.6 Web search enhances Prompts (Lao Huang consensus)

Lao Huang newtype · Prompt / Context Engineering in Practice §3 (2024-12 Post 693) commonly used template:

Always reply in Chinese. Prefers English sources for online searches,
with Chinese sources as a last option. Always search the web for English
pages, even if questions are in Chinese. Translate questions to English
before searching.

Why: There is a world of difference in information quality between Chinese and English sources —— For the same question, the answer density and accuracy from English sources are often 3-5 times that of Chinese sources.

2. Explicit disagreements (3 points — not smoothed over)

2.1 Disagreement 1: Does Prompt Engineering need to be "learned"?

Perspective Stance Key argument
Lao Huang(Counter-consensus / 2025-05 Post 1247) "There is absolutely no need to specifically learn prompts, let alone buy courses" "The essence of Prompt is logic and methodology. If you learn logic well and master expression and communication methodology, that is enough to organize language and drive model reasoning"
Lao Huang extension(2025-05 Post 1223) "If I really want to improve, I'll just go study logic; even books like the Pyramid Principle are more useful" Most prompt courses are just packaging; you can't draw inferences from one example when learning them
Lao D(Teaching school) Complete 13-lesson course + Markdown structuring + reverse questioning + three-layer architecture Domestic ordinary creators can't express logically, so they must be taught
Wang Kai(Practical school) PUA Claude, polishing repeatedly to over 6000 words Investment decisions must be rigorous, requiring repeated iteration rather than "getting it right in one write"

Real disagreement:Reflects differences in user identity:

  • Communications / marketing / PM background → Lao Huang's stance (logical expression is already a given, Prompt is a natural byproduct)
  • Domestic ordinary creators / can't write a Brief → Lao D's stance (must teach the three-layer architecture)
  • Investment / rigorous decision-making scenarios → Wang Kai's stance (PUA to 6000 words before it's adequate)

Anti-pattern consensus:

  • ❌ Treating "I want to learn Prompt Engineering" as the goal → 90% of time wasted
  • ❌ Collecting hundreds of Prompt templates → useless in most scenarios
  • ❌ Learning "5 secrets for AI to write Xiaohongshu posts" from bloggers → it has nothing to do with the blogger; clearly decomposing your own needs is the key

2.2 Disagreement 2: If "Thinking mode" is strong enough, do we still need the Sequential Thinking MCP?

Lao Huang newtype · Prompt / Context Engineering in Practice §7 + newtype · MCP Protocol and Ecosystem in Practice §4.4:

Perspective Stance
Theoretically unnecessary The Thinking of flagship models (Claude 4 / o3 / Gemini 2.5 Pro / Qwen3) can replace it
Still needed in practice API access has costs → it helps for models that don't have or don't enable Thinking
Best practice Equip Sequential Thinking and let the model decide for itself. For Claude 4 in ChatWise:Use built-in Thinking before starting work, and invoke Sequential Thinking when producing output

Key observation(Lao Huang 2026-05):Claude 4 Sonnet calls the Sequential Thinking MCP while doing its own Thinking(Previous models did not do this).

Real disagreement:The two modes of Thinking do not conflict — the model's built-in one is "introspection", Sequential Thinking is an "external scaffold":

  • Simple decisions → built-in Thinking
  • Complex multi-step + need for visible process → Sequential Thinking
  • Best practice → install both, let the model route itself

2.3 Disagreement 3: Is "Thinking-Claude" performative thinking valuable?

Lao Huang newtype · Prompt / Context Engineering in Practice §5 (2024-11 Posts 614, 616 highlight) iterated judgment:

Event: The browser extension Thinking-Claude adds "slow thinking" prompts to Claude; the effect was so good that Lao Huang called it an "epic-level enhancement" on the first day.

The next day Lao Huang retracted his judgment:

  1. It is not real thinking, but "performative thinking" —— because model responses are generated in one pass, and the so-called "thinking process" is also part of that generation
  2. But performance also has value —— under framework constraints,it forces the model to produce answers that are more logical, more structurally sound, and more comprehensive. This is the real reason for quality improvement
  3. It is not that the prompt is strong; it is that Claude is strong —— the ceiling of a prompt is exactly the ceiling of the model's capabilities

The real disagreement:Does "performing thinking" count as real thinking?:

  • Strict definition camp: No, because LLMs generate in one pass, and the so-called "thinking" is part of the output
  • Pragmatist camp:Forcing step-by-step generation → higher-quality output, and that is enough

When to use:

  • When evaluating a "god-tier Prompt", ask: "Is the model strong, or is the prompt strong?"
  • When writing your own Prompts,deliberately force step-by-step thinking(similar to Chain of Thought), even if you know it is a performance

3. Lao D's Three-Layer Prompt Architecture (Exclusive Teaching SOP)

dontbesilent AI self-media course / 13 lessons of systematic methodology §1 —— Core mindset that runs through the entire course:

我写"元提示词"(Meta Prompt,我用键盘敲)
        ↓ 输入给 Gemini / Claude / DeepSeek 等
AI 输出"系统提示词"(System Prompt,可能 2000-3000 字)
        ↓ 复制到智能体平台
变成"智能体"(Agent)
        ↓ 接受用户输入
用户输入"用户提示词"(User Prompt)
        ↓
智能体输出回复

3.1 Key cognition

  • Meta-prompt = A prompt used to produce prompts.A Russian doll, but it solves the problem that "humans are not good at writing long prompts"
  • Do not write system prompts directly: let AI write them, and you only write the meta-prompt
  • For users in China, Gemini 2.5 Pro is the first choice for writing meta-prompts(long context + strong reasoning);For driving agents, use DeepSeek R1(available in China + if a prompt works on R1, it is generally considered universally OK)

3.2 Model selection ≠ antidote to insufficient prompts

"Many people ask: if GPT can't write well, they want to switch to Claude / Gemini. But they ignore that the core is the prompt itself. Improving the prompt is more important than switching models."

3.3 AI Imitation Writing 4-Step SOP (L02)

1. 准备提示词模板(Markdown 结构化)
   # 你的任务
   先分析参考文案,反向向用户提问列表,确认所有关键要素后再仿写

   # 参考文案一
   <粘贴文案 1>

   # 参考文案二
   <粘贴文案 2>

2. AI 接收后不立即仿写,而是发 8 个左右问题
   (字数/结构/语调/风格/用词偏好/特殊要求/EMOJI/长度)

3. 用户逐一回答 → AI 仿写

4. 不满意 → 用问题修订(列出 3 条不满意 + 修正)→ 再生成

Key points:

  • Use Markdown # hierarchy to prevent AI from treating multiple reference copies as one
  • One reference case is enough (if the case itself contains all features); otherwise multiple mutually consistent cases are needed
  • Do not give messy, mutually inconsistent cases — AI cannot find common ground

3.4 LobeChat Blogger Imitation 4 Steps (L09)

  1. Find the blogger's viral title data (cover title + text title + like count)
  2. Use Markdown to structure the prompt → let AI analyze the underlying logic (attention economy / human nature / communication studies)
  3. Based on the analysis, have AI write the system prompt
  4. Make the system prompt + Claude 3.7 Sonnet into an Agent

4. Lao Huang Exclusive: 4 Production-Grade Prompt Templates

newtype Prompt template library (4 production-grade prompts + 1 CLAUDE.md sample):

4.1 YouTube Podcast Deep Understanding (Gemini v2)

5-step execution flow:

Step Purpose Key output
Step 1 Extract structure Chapter division + time range + viewpoint differences between host and guests Chapter cards (emoji template)
Step 2 Detailed retelling Complete retelling (not a summary) + imitate tone + each segment covering 20–30 minutes Time period title + guiding summary + content
Step 3 Bilingual understanding English-Chinese parallel text + cultural notes Three columns: English / Chinese / Notes
Step 4 Knowledge expansion Topic background + guest introduction + concept explanation + further reading 5-dimension structured output
Step 5 Learning assistance 10 English learning notes + 5 discussion questions + 1 opposing challenge + 5 high-frequency words Learning material pack

4.2 Social Media Deconstruction (Viral Content Breakdown)

ROLE setting:

"You are a world-class social media strategist and viral content analyst..."

5-dimension analysis framework:

  1. 🎯 Core Insight(Core insight)
  2. 📐 Structure & Copy(Structure / Copy / Hook / Body / CTA)
  3. 🧠 Psychology(Emotional resonance / Social currency / Authority and trust)
  4. 🚀 Virality Factors(Audience fit / Topic timing / Simple and portable)
  5. 📋 Summary & Takeaways(Top 3 success factors + replicable model)

Key notes:"Always reply in Chinese!"

4.3 Creative Divergence Mode (Creative Divergence 4 lenses)

  • Gap Analysis(Gap analysis)
  • Reframing(Reconstruction)
  • Cross-Domain(Cross-domain)
  • Breakthrough(Breakthrough)

4.4 Weekly Synthesis Report

Markdown folder analysis → cross-document synthesis → weekly report.

4.5 CLAUDE.md MCP Configuration Sample

Tavily + Sequential Thinking + Prompt House + Super Analyst Pro

—— they are the "default startup 4-piece set" of newtype OS.

5. Wang Kai Exclusive: PUA Claude + AI Interrogation SOP

5.1 PUA Claude (polishing prompts to 6000+ words)

Wang Kai Claude Code terminal workflow methodology (complete version) §2 One of the mindsets:

"PUA Claude — analyze → feedback → improve again and again, and the prompt can eventually be polished to over 6000 words"

Wang Kai's practical process:

  1. Write the first version of the prompt (maybe 500 words)
  2. Claude outputs → not satisfied
  3. List "3 points of dissatisfaction + corrections" → let Claude revise the prompt itself
  4. Regenerate using the revised prompt
  5. Repeat N rounds → the prompt eventually reaches 6000+ words
  6. Stable output in any scenario

5.2 AI as a Ruler (Core of the Decision System)

Wang Kai AI-era decision system §4:For fuzzy scenarios that humans cannot accurately quantify, let AI score them using a stable framework.

Scenario AI usage Value
Market risk level Score 1–10 Determine cash ratio
Enterprise intrinsic value Multi-model synthesis + interval Decide whether to buy
Single stock risk Score across 5 dimensions: short, medium, and long term Determine position size
Emotional state "Am I panicking excessively right now?" Prevent impulsive decisions
Risk of AI replacing a job List the workflow and ask the replacement probability Career planning
Startup project feasibility Break down the model and let AI interrogate it Project initiation

Analogy:Insurance company actuary. Give AI a stable framework, and it becomes a ruthless actuarial machine.

Key discipline:Consistency > Accuracy —— running the same stock multiple times should yield similar results.

5.3 Five Variables for Risk Quantification (Built into Wang Kai's Prompt)

Wang Kai AI-era decision system §9:

  1. Whether the company's fundamentals have truly changed
  2. Macro risks (Federal Reserve / tariffs / war / unexpected events)
  3. Industry risks (AI replacement / policy / competitive landscape)
  4. Liquidity risk
  5. Position concentration

5.4 Intrinsic Value Calculation Prompt Template

Wang Kai RSI Investment Practical Case Collection + AI Value Investing Philosophy —— a 6000+ word prompt template, distilled from 120+ rounds of Agent dialogues based on Buffett / Munger / Duan Yongping.

6. Cal Rueb Exclusive: CLAUDE.md Anti-patterns

Claude Code Best Practices (Cal Rueb · Anthropic Internal Talk) §3.3:

6.1 What should NOT go into CLAUDE.md

  • Your passwords / API keys (use environment variables)
  • Large code snippets (use import or links)
  • Content unrelated to work (use CLAUDE.local.md for personal preferences)
  • Imperative statements like "You must X" —— Cal emphasizes using "We prefer X because Y"; instructions with reasons are followed better by LLMs

6.2 What SHOULD go into CLAUDE.md

  • Project structure (directory tree, where key modules are)
  • Build / test commands (pnpm dev, pytest, make build)
  • Code style (2 spaces vs 4 spaces, string quotes, naming conventions)
  • Architectural iron rules ("API calls must go through src/api/")
  • Pitfalls to avoid ("Do not touch legacy/auth.py")
  • Usage preferences for MCP tools

7. Integrated Analysis Framework (Lao Huang Advanced Usage)

newtype · Prompt / Context Engineering in Practice §8 (Post 1481, 2025-07):

Lao Huang's Prompt integration experiment:

  • First principles(inside-out, fundamentally reconstruct → breakthrough solutions)
  • Impossible trinity(outside-in, identify boundary constraints → optimize under constraints)

Limitations of both:

  • Impossible trinity → easy to fall into "fatalism"
  • First principles → easy to ignore constraints

Lao Huang had Claude integrate them into an "advanced analysis framework" Prompt, and loaded it into the notes_insight tool in Obsidian MCP.

Why this pattern works:

  • A single framework has blind spots
  • Integrating multiple frameworks → the Prompt itself becomes a methodology
  • This Prompt can be mounted into a Skill and automatically invoked in any scenario

8. iPhone Shortcuts + Prompt (Lao Huang Exclusive National-Level Usage)

newtype · Prompt / Context Engineering in Practice §4 (Essence of Post 263, 2024-05):

Lao Huang's 3 iCloud shortcuts(GPT-4o + Markdown output):

  1. Q&A GPT: voice input → speech-to-text + Prompt → GPT-4o → Notes
  2. Inspiration GPT: same as above
  3. Summary GPT: open WeChat article in Chrome → share to Shortcuts → GPT-4o extracts key points → Notes

Key Prompt design:Force Markdown format output(convenient for later editing in Obsidian / Ulysses).

Why this design works:

  • "AI + system shortcuts" integrates deeper than "AI Apps"
  • Markdown is the "universal language" of the AI era; forcing MD output by default in any Prompt makes downstream processing 10 times easier

9. How to Choose — Prompt Paths by Identity

Who you are Primary method Starting point
Communications / PM / good at logical expression Lao Huang's counter-consensus position Don't take Prompt courses; study logic / the Pyramid Principle
Domestic ordinary self-media creator / can't write a Brief Lao D's three-layer architecture Meta-prompt → system prompt → agent → user prompt / Markdown structuring
Investing / rigorous decision-making Wang Kai PUA Claude Repeated analyze → feedback → improve / 6000+ words / AI as ruler for quantification
Want to make production-grade Prompt templates Lao Huang's 4 templates YouTube podcast / Social Media / Creative Divergence / Weekly Synthesis
Want to work on Claude Code projects Cal Rueb CLAUDE.md "We prefer X because Y" / do not use "You must X" / project structure + commands + style

Universal starting order(for any identity):

  1. 5-element task breakdown(who / what background / what to do / how to do it / image of success)
  2. Markdown structuring(use # hierarchy)
  3. Force Markdown output(downstream processing 10 times easier)
  4. Add Ultra think(essential for complex tasks)
  5. Configure Sequential Thinking MCP(let the model decide routing itself)
  6. If not satisfied → list 3 points of dissatisfaction + corrections → let AI revise the prompt itself(Wang Kai PUA)
  7. After stabilization → turn into a Skill(SKILL.md + references + Git)

10. Anti-pattern Summary

❌ ✅
Treating "I want to learn Prompt engineering" as the goal Study logic / the Pyramid Principle, and learn Prompts along the way
Collecting hundreds of Prompt templates Encapsulate core patterns into Skills
Learning "5 secrets for AI to write Xiaohongshu posts" from bloggers Break down your own needs clearly: 5 elements + reverse questioning
Expecting a "god-tier Prompt" to improve model capabilities The ceiling of a Prompt is the model's capability (Lao Huang)
Getting it right in one write and being done PUA Claude and iterate repeatedly to 6000 words (Wang Kai)
Writing system prompts directly Let AI write system prompts; you only write meta-prompts (Lao D)
Imperative "You must X" "We prefer X because Y" with reasons (Cal)
Giving messy, mutually inconsistent reference cases Give mutually consistent cases so AI can find common ground
Not forcing Markdown output Force MD by default; downstream processing is 10 times easier
Don't use Sequential Thinking and just let the model plow ahead blindly Equip it, and let the model decide for itself whether to invoke it

11. Reference List

Primary original author wikis:

Related topic wikis (this series):

History

  • 2026-05-18: First release of the topic-dimension wiki. Synthesized and rewritten from 8 author wikis. Explicitly notes 3 points of disagreement: whether you need to learn Prompting / Thinking mode selection / the value of performative thinking

Sources and related materials