Deep Research - Prompt de Análisis IA
Use this Li Lu rule prompt to apply “Deep Research” to a specific company. It turns a vague opinion into a repeatable checklist: what facts you must verify, which assumptions matter most, what would invalidate the thesis, and the common misreads that create false certainty. Expect a written output you can save: a thesis summary, key risks, and next-step questions for filings and earnings calls. If a claim matters, require primary-source citations before you act. Educational only — not investment advice.
Prompt completo
Eres un analista de inversiones entrenado en el principio de Li Lu: "Deep Research". Tu tarea es analizar {Nombre de la Empresa} a través de esta perspectiva específica.
## Contexto
Li Lu enseña: "Understand the business deeply before investing. Read everything available. Talk to customers and competitors."
## Marco de Análisis
### 1. Evaluación de Aplicación del Principio
- ¿Cómo se aplica específicamente este principio a {Nombre de la Empresa}?
- ¿Qué aspectos de la empresa son más relevantes para "Deep Research"?
- Califica la alineación: Fuerte / Moderada / Débil
- ¿En qué se enfocaría Li Lu primero?
### 2. Evidencia Cuantitativa
- Identifica 3-5 métricas financieras clave relevantes
- Analiza estas métricas durante los últimos 5-10 años
- Compara con competidores y benchmarks históricos
- ¿Los números están mejorando, estables o deteriorándose?
### 3. Análisis Cualitativo
- Evalúa factores no cuantificables que Li Lu examinaría
- Calidad de la gestión y alineación con este principio
- Dinámica de la industria y posición competitiva
- Sostenibilidad del modelo de negocio desde esta perspectiva
### 4. Evaluación de Riesgos
- ¿Qué riesgos destaca este principio para {Nombre de la Empresa}?
- ¿Qué señales de advertencia identificaría Li Lu?
- Prueba de estrés: ¿Cómo se desempeñaría bajo condiciones adversas?
- ¿Cuál es el peor escenario desde esta perspectiva?
### 5. Identificación de Oportunidades
- ¿Qué oportunidades revela este análisis?
- ¿Hay fortalezas ocultas que el mercado podría estar subvalorando?
- ¿Qué catalizadores podrían liberar valor?
### 6. Li Lu Verdict
- ¿{Nombre de la Empresa} pasa la prueba de "Deep Research"?
- Calificación: 1-10
- Recomendación clara: Comprar / Mantener / Evitar
- Resumen en un párrafo
## Formato de Salida
Presenta datos específicos en cada sección. Termina con un veredicto decisivo.Related reading (close the loop)
Pick one path below to turn the output into a checkable, repeatable decision policy.
- Read the matching principleDefinition, boundaries, pitfalls, and a minimal checklist.
- Master profileMethodology summary + common misreads for this framework.
- Practice in scenariosTranslate conclusions into “what I do under stress”.
- More prompts from this masterTriangulate with multiple rules instead of anchoring on one prompt.
Educational only. Verify facts with primary sources and apply your own constraints.
Este contenido solo está disponible en chino e inglés por el momento.
Basic Questions
What specific steps does Li Lu's first-hand research method include?
Core idea: conducting extremely thorough first-hand research on investment targets
✅ Using this AI prompt, you can systematically analyze any company or investment opportunity from this principle's perspective.
The prompt guides you to:
1. Assess whether the investment target meets this principle's core requirements
2. Identify key risks and blind spots
3. Provide a 1-10 comprehensive rating
Start by analyzing companies you know well for practice, then apply the framework to new investment decisions.
✅ Using this AI prompt, you can systematically analyze any company or investment opportunity from this principle's perspective.
The prompt guides you to:
1. Assess whether the investment target meets this principle's core requirements
2. Identify key risks and blind spots
3. Provide a 1-10 comprehensive rating
Start by analyzing companies you know well for practice, then apply the framework to new investment decisions.
Usage Tips
Is the AI's 1-10 rating reliable?
⚠️ The rating is just the starting point of a deep research journey, not the destination.
The rating's value:
- Li Lu's deep research often takes months or years — AI scoring covers only the most superficial part
- The score helps determine research direction and priorities — which dimensions need deeper investigation
- Weaknesses exposed in the rating point to where you need further deep research
Key limitations:
- Li Lu's research depth includes face-to-face management meetings, site visits, supply chain research — none of which AI can do
- The core of deep research is forming 'unique insights,' not summarizing public information — AI excels at the latter
- True deep research requires significant time and effort investment — AI scoring only saves initial screening time
✅ Right approach: Use the AI score to quickly screen companies worth investigating deeply, then do the real work — reading annual reports, analyzing competitors, understanding industry dynamics. AI is a research assistant, not a research replacement.
The rating's value:
- Li Lu's deep research often takes months or years — AI scoring covers only the most superficial part
- The score helps determine research direction and priorities — which dimensions need deeper investigation
- Weaknesses exposed in the rating point to where you need further deep research
Key limitations:
- Li Lu's research depth includes face-to-face management meetings, site visits, supply chain research — none of which AI can do
- The core of deep research is forming 'unique insights,' not summarizing public information — AI excels at the latter
- True deep research requires significant time and effort investment — AI scoring only saves initial screening time
✅ Right approach: Use the AI score to quickly screen companies worth investigating deeply, then do the real work — reading annual reports, analyzing competitors, understanding industry dynamics. AI is a research assistant, not a research replacement.
Getting started
Does this prompt give investment advice or buy/sell calls?
No. It is a research helper that turns your thinking into checkable inputs and constraints: what evidence you must verify, what would prove the thesis wrong, and what common misreads to avoid. Treat the output as a draft, not a signal. Validate every material number against primary sources (filings, earnings releases, investor presentations, transcripts), and do not act unless you can write down (1) position-size limits and (2) explicit invalidation triggers.
What inputs should I provide for a reliable result?
At minimum: a 1-sentence business model summary, your current thesis (why it wins/loses), time horizon, and risk constraints; a valuation/price range; and the latest financial statements (profit quality, cash flow, debt/liquidity). Add context that reduces hallucinations: the exact filing period, known one-offs, key competitors, and what you do NOT know yet. If an input is missing, label it as missing evidence instead of letting the model guess.
Validation and boundaries
How do I validate the output?
Validate falsifiable claims one by one. Rewrite each key statement into something you can check: the metric, the period, and the source. Numbers must match filings; management claims must be traceable to transcripts/guidance; and “moat” claims need observable evidence (pricing power, retention, switching costs, cost structure). Anything you cannot verify becomes a follow-up task, not a decision trigger. If the model cites dates, confirm they are not beyond its knowledge cutoff.
When should I NOT act on the output?
If you cannot write down invalidation triggers, a position-size cap, or primary-source evidence for the key claims behind “Deep Research”, do not act. The safer move is usually to reduce size, slow down, and schedule the next review.
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