El Sintetizador de Investigación: Convierte Fuentes Dispersas en un Mapa de Acuerdos y Contradicciones
Por qué importa este prompt
Without a structured synthesis step, people default to citing whichever single source already matched what they expected to find, and the gap surfaces at the worst possible moment — when a stakeholder points out mid-meeting that another source in the same pile actually argues the opposite. That credibility hit costs far more time and trust to recover from than the twenty minutes a proper synthesis would have taken upfront.
Para qué lo usamos
A product manager has four analyst reports and a dozen saved articles on whether on-device AI or cloud AI will dominate enterprise deployment by 2027, needs to present a one-page synthesis to the leadership team by Friday afternoon, and does not have time to read everything closely enough to defend a recommendation under questioning in the room.
Prompt
Role: Act as a research synthesis analyst who specializes in finding genuine points of agreement, disagreement, and unaddressed gaps across multiple sources covering the same question — not a summarizer who blends everything into one smooth narrative. Context: I am researching [YOUR RESEARCH QUESTION]. Here are the sources, each clearly labeled: Source A: [PASTE SUMMARY, ABSTRACT, OR FULL TEXT] Source B: [PASTE SUMMARY, ABSTRACT, OR FULL TEXT] Source C: [PASTE SUMMARY, ABSTRACT, OR FULL TEXT] [ADD MORE SOURCES AS NEEDED, LABELED SOURCE D, E, ETC.] Task: Produce a structured synthesis that: 1. Identifies the 3 to 5 core claims being made across all sources combined 2. For each core claim, states which sources agree, which disagree, and quotes or closely paraphrases the specific language showing the agreement or disagreement 3. Distinguishes disagreements that are factual (verifiable, at least one source is likely wrong) from disagreements that are interpretive (both sources can be reasonably right, they emphasize different things) 4. Identifies at least [NUMBER] unaddressed gaps — specific sub-questions none of the sources cover, that would need separate research 5. Flags any source that appears to have a conflict of interest or vested stake in a particular conclusion, such as a vendor-sponsored study or an advocacy group Constraints: - Do not average disagreements into a vague middle-ground conclusion. State disagreements explicitly, with attribution to the specific source. - Do not treat source volume as a proxy for correctness. Three sources repeating the same unverified claim is not stronger evidence than one source citing primary data — check whether sources are independently reporting or just citing each other before counting them as separate confirmations. - If two sources make the same claim but one cites a primary source and the other cites the first source, note this explicitly as one underlying data point, not two. - Keep the total output under [WORD LIMIT] words. Output Format: 1. A short summary paragraph (3 to 4 sentences) stating the overall state of consensus or lack of it 2. "Where Sources Agree" — list of claims with supporting sources 3. "Where Sources Disagree" — list of claims with which sources say what, and whether the disagreement is factual or interpretive 4. "Unaddressed Gaps" — list of the open sub-questions 5. "Caveats" — short section flagging any source credibility concerns
Resultado
Overall State of Consensus: Of the three sources reviewed, there is partial agreement that on-device inference will handle a growing share of routine enterprise AI tasks by 2027, but the sources sharply disagree on whether this displaces cloud AI for core workloads or simply adds a new tier alongside it. Source C's central claim appears to rest heavily on Source A's original survey data without independent verification.
Where Sources Agree: - On-device inference costs have dropped enough to handle routine classification and short-context tasks locally (Sources A, B, C) - Enterprise IT buyers cite data residency, not raw cost savings, as the top driver for on-device adoption (Sources A, B)
Where Sources Disagree: - Claim: "On-device AI will capture the majority of enterprise AI spend by 2027." Source A projects 35% share by 2027 based on a survey of 200 IT buyers; Source B projects under 15% share, arguing complex reasoning tasks still require cloud-scale models regardless of hardware improvements. This is a factual disagreement — the two sources make incompatible quantitative predictions, and only one survey methodology can be sounder. Worth checking sample composition before trusting either figure. - Claim: "Cloud AI providers are cutting prices industry-wide to defend market share." Source C asserts this is already happening broadly; Source A documents only one vendor doing this. This is an interpretive disagreement — Source C may be generalizing from a single data point rather than an industry pattern.
Unaddressed Gaps: - No source addresses how data-residency requirements outside the US and EU affect this split - No source quantifies the latency threshold at which enterprises actually migrate workloads from cloud to on-device, versus simply running new workloads on-device from the start
Caveats: Source C is published by a chipmaker with a direct commercial interest in on-device AI adoption and should be weighted accordingly — its 35%-by-2027 figure traces back to Source A's survey rather than independent data.
La forma habitual en que la gente maneja fuentes contradictorias es elegir la que ya coincide con lo que querían creer, o pedirle a una herramienta de IA un resumen rápido que mezcla todo en un párrafo seguro de sí mismo. Ambos enfoques borran silenciosamente la información más útil del conjunto: los puntos específicos donde fuentes creíbles realmente discrepan, y por qué.
Por qué importa el Role aquí
El Role enmarca al modelo como un analista de síntesis en lugar de un resumidor, porque son trabajos distintos. El trabajo de un resumidor es comprimir; el de un analista de síntesis es mapear el desacuerdo y atribuirlo correctamente. Pedirle a un asistente general que “resuma estos cuatro informes” casi siempre produce la versión suavizada, porque suavizar es para lo que la síntesis se optimiza por defecto.
Qué hace realmente la sección Task
Los cinco requisitos numerados fuerzan una secuencia específica: encontrar primero las afirmaciones centrales, luego revisar el acuerdo por afirmación, luego clasificar los desacuerdos en fácticos frente a interpretativos. Esa última distinción es la que casi todos omiten, y es la que importa más. Un desacuerdo fáctico significa que alguien probablemente está equivocado y vale la pena investigar la metodología. Un desacuerdo interpretativo significa que ambas fuentes pueden tener razón mientras enfatizan cosas distintas — tratarlo como una disputa fáctica desperdicia tiempo buscando una respuesta “correcta” que no existe.
La Restricción que hace el trabajo real
“No promedies los desacuerdos en una conclusión intermedia vaga” es la instrucción que protege todo el propósito del ejercicio. Sin restricción, la mayoría de los modelos resolverán la tensión encontrando un lenguaje suave y cauteloso que técnicamente acomode a cada fuente — exactamente la salida que deja a alguien desprevenido cuando un colega señala que las fuentes nunca estuvieron realmente de acuerdo. La restricción de conteo de citas importa igual: tres entradas de blog que citan todas el mismo estudio original son un solo dato con tres disfraces, no tres confirmaciones independientes.
Por qué el formato de salida se divide en cuatro secciones
Separar “Dónde las Fuentes Coinciden” de “Dónde las Fuentes Discrepan” obliga a que los desacuerdos permanezcan visibles en lugar de desaparecer en una narrativa combinada. La sección “Vacíos No Abordados” existe porque el error de investigación más costoso no es malinterpretar una fuente — es asumir que una pregunta fue cubierta cuando nunca se planteó. La sección “Advertencias” detecta las fuentes que tienen una razón para preferir una conclusión determinada, lo cual importa más que el número de fuentes al decidir cuánto peso darle a una afirmación.
Ejecuta esto antes de cualquier reunión, memo o decisión que dependa de reconciliar varias fuentes que discrepan entre sí — no después, cuando los vacíos ya se han convertido en la objeción de otra persona.