El Sintetizador de Investigaciones: Descubre en qué coinciden las fuentes, qué disputan y qué dejan sin respuesta
Why this prompt matters
Most people read sources sequentially and end up anchored to whichever conclusion they read last. Real synthesis requires mapping the intellectual landscape: where genuine consensus exists, where disputes are empirical vs methodological vs definitional, and where evidence is simply absent. Without this, you can unknowingly cherry-pick support for any conclusion. A 45-minute manual synthesis that would normally take an analyst half a day costs seconds with this prompt.
What we use it for
You have pulled together 4–7 articles, papers, or reports on a topic before writing a brief, making a decision, or presenting to stakeholders — and you need to understand the actual state of evidence across them, not just what each article says on its own.
Prompt
Act as a systematic research analyst with expertise in synthesizing academic and professional literature. You will analyze the source summaries or abstracts I provide and produce a structured synthesis identifying: where sources converge, where they conflict, what each uniquely contributes, and what important questions remain unanswered. I am researching: [YOUR RESEARCH TOPIC OR QUESTION] Here are the source summaries to synthesize: SOURCE 1 — [TITLE / AUTHOR / DATE]: [PASTE SUMMARY OR ABSTRACT HERE] SOURCE 2 — [TITLE / AUTHOR / DATE]: [PASTE SUMMARY OR ABSTRACT HERE] SOURCE 3 — [TITLE / AUTHOR / DATE]: [PASTE SUMMARY OR ABSTRACT HERE] [Add more sources as needed — 3 to 8 sources works best] Constraints: - Work only from what I have provided. Do not introduce outside knowledge. - Flag when two sources use the same term differently — definitional conflicts matter. - Be specific: when noting agreement or disagreement, cite which sources and what exact claims. - Do not summarize each source individually — I have already read them. Synthesize across them. - If a source is too brief or vague to contribute meaningfully, say so rather than padding. - Distinguish empirical conflicts (different findings) from methodological conflicts (different measurement approaches) from definitional conflicts (different meaning of terms). Produce your synthesis in this exact format: ## Points of Strong Agreement [Claims supported by 3+ sources, with source citations in parentheses] ## Productive Tensions [Where sources meaningfully disagree — note whether the conflict is empirical, methodological, or definitional, and why it matters] ## Unique Contributions [One bullet per source: what this source adds that the others do not cover] ## Unanswered Questions [Important questions the sources collectively leave open: gaps, unstudied populations, unexplored mechanisms, missing timeframes] ## Synthesis Statement [One dense paragraph integrating the above into a coherent research-landscape assessment — suitable for quoting in your own writing]
Result
Research topic: Does remote work reduce productivity?
Sources provided: Bloom & Liang (2023), Microsoft Work Trend Index (2025), Stanford WFH Research (2024), McKinsey Future of Work (2025), Chen et al. (2024)
Points of Strong Agreement
All five sources agree that remote work productivity outcomes are highly heterogeneous — dependent on role type, individual autonomy preference, home environment quality, and management approach (Bloom & Liang; Microsoft; Stanford; McKinsey; Chen et al.). Three sources explicitly note that productivity metrics used in remote work studies are inconsistently defined, making cross-study comparisons unreliable (Bloom & Liang; Stanford; Chen et al.).
Productive Tensions
The central empirical conflict is between Bloom & Liang (2023), who find a 9% productivity gain for hybrid workers over full-remote, and the Microsoft Work Trend Index (2025), which reports 87% of full-remote knowledge workers self-assess as highly productive. This is a methodological conflict, not a factual one: Bloom & Liang use manager-rated output metrics for customer service roles; Microsoft uses self-reported assessments across knowledge workers. The populations and measurement instruments are fundamentally different.
McKinsey and Stanford diverge on whether collaboration quality declines in remote settings. McKinsey finds measurable degradation in cross-team innovation projects; Stanford finds no measurable difference when controlling for meeting frequency. This is an empirical conflict that likely reflects different industries sampled.
Unique Contributions
- Chen et al.: Only source examining productivity variation by home office setup quality — finds ergonomic environment accounts for 23% of productivity variance
- Bloom & Liang: Only source with randomized control data rather than observational
- Microsoft Work Trend Index: Largest sample (31,000 workers, 31 countries), only source with multi-country data
- Stanford WFH: Only longitudinal data spanning 2020–2024 — tracks adaptation over time
- McKinsey: Only source distinguishing individual-task productivity from collaborative innovation productivity
Unanswered Questions
- How do productivity effects evolve after 3+ years of sustained remote work? (All studies capture ≤18 months post-adoption)
- Are findings consistent across labor markets outside the US, UK, and Australia?
- How does manager monitoring intensity interact with intrinsic motivation over time?
- What happens to long-term career progression for remote workers — absent from all five sources?
Synthesis Statement
The literature does not support a simple "remote work increases or decreases productivity" conclusion. The stronger finding, supported across all five sources, is that outcomes are highly contextual. Methodological choices — particularly whether to measure individual task output or collaborative outcomes, and whether to use self-report or manager ratings — largely predict the direction of findings. The most important unanswered question is longitudinal: nearly all existing data captures an adjustment period, not a steady state. Any organizational decision based on this literature must specify what type of productivity it cares about and for what worker population.
La mayoría de las personas abordan un montón de investigaciones de la misma manera: leen la fuente uno, luego la fuente dos, luego la fuente tres — y terminan sosteniendo lo que el último autor argumentó de manera más convincente. Eso no es síntesis. Eso es exposición secuencial.
La síntesis real requiere un tipo diferente de lectura: mapear dónde convergen las fuentes, aislar dónde realmente discrepan y por qué, identificar qué contribuye cada una de manera única y — lo más importante — reconocer qué deja sin respuesta todo el cuerpo de literatura. Este Prompt está diseñado para forzar exactamente ese análisis.
Por qué existe cada sección del Prompt
El formato de salida tiene cinco secciones, y cada una es deliberada.
Puntos de Fuerte Acuerdo requiere que la AI encuentre afirmaciones respaldadas por múltiples fuentes — no solo "la mayoría de las fuentes discuten X" sino "estas fuentes específicas hacen esta afirmación específica". Esto evita el falso equilibrio, donde dos artículos atípicos reciben el mismo peso que un consenso de seis.
Tensiones Productivas es la sección más importante. El Prompt pide a la AI clasificar los desacuerdos por tipo: empírico (hallazgos diferentes de datos diferentes), metodológico (enfoques de medición diferentes que llegan a conclusiones diferentes) o definicional (fuentes que usan la misma palabra para significar cosas diferentes). Esta distinción importa enormemente. Un conflicto empírico sugiere un fenómeno del mundo real que vale la pena investigar. Un conflicto metodológico a menudo significa que los estudios no están midiendo realmente lo mismo. Un conflicto definicional significa que estás comparando manzanas con grapadoras.
Contribuciones Únicas evita el error común de tratar todas las fuentes como intercambiables. Cada fuente en una buena revisión de literatura fue elegida por una razón — esta sección obliga a la AI a articular esa razón explícitamente.
Preguntas sin Respuesta es lo que separa la síntesis útil del mero resumen. Los vacíos en la literatura son a menudo más accionables que sus hallazgos: te dicen dónde debe intervenir tu propio juicio, dónde tu decisión conlleva un riesgo no cuantificado y dónde se justifica más investigación antes de comprometerse.
Declaración de Síntesis produce un solo párrafo denso adecuado para citar o adaptar directamente en tu propia escritura — una caracterización del panorama de investigación lista para usar.
Cómo aprovecharlo al máximo
El Prompt funciona mejor con 3 a 8 fuentes. Con menos de 3 no hay nada que sintetizar; con más de 8 incluso los mejores LLMs empiezan a perder la pista de qué afirmación vino de qué fuente.
Pega resúmenes o extractos reales — no solo títulos. Cuanto más específica sea la entrada, más específica será la síntesis.
La instrucción de distinguir entre conflictos empíricos, metodológicos y definicionales hace un trabajo real. Déjala. Sin ella, la mayoría de los LLMs notarán que las fuentes "discrepan" sin explicar por qué.
Modelos Compatibles
Este Prompt funciona bien con Claude Sonnet 4.6, GPT-5 y Gemini 3.5 Flash. Para textos fuente muy largos (artículos completos en lugar de resúmenes), Claude y GPT-5 manejan el contexto de manera más fiable.