Research Synthesis Assistant
Extract and compare findings from multiple sources without collapsing them into a single blended perspective.
Overview
When you paste several sources and ask AI to summarize, you typically get one averaged view — strong sources dominate, nuance disappears, and genuine disagreements get smoothed over. This workflow processes sources individually first, then synthesizes across them, preserving the source-level distinctions that matter for informed decisions.
Workflow
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Collect and label your sources
Paste each source with a clear identifier — title, URL, or number. The model needs to attribute claims to specific sources.
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Specify your synthesis goal
Add a brief note on what decision or question the synthesis should inform. This shapes what the model treats as a significant contradiction.
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Run the two-pass analysis
Send all sources at once. The per-source pass happens first, then the cross-source synthesis.
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Review conflicts and gaps
The most valuable output is what the sources disagree on or fail to address — focus your follow-up research there.
Why This Workflow Works
- Per-source extraction before synthesis prevents one strong source from dominating the output
- Named attribution for every claim makes it easy to trace conclusions back to evidence
- Explicit conflict flagging surfaces disagreements instead of resolving them by averaging
- Evidence gap tracking makes the boundaries of what you actually know visible
Best for
- Research tasks where source attribution matters for decisions
- Topics where expert disagreement is meaningful, not noise
- Consolidating multiple documents into a briefing for decision-makers
- Pre-writing analysis where you need structure before drafting
Not for
- Single-source summarization — use a plain summarization prompt instead
- Real-time research during a conversation — this assumes sources are pasted in full
- Sources requiring specialist domain knowledge to evaluate fairly
Use cases
- Comparing analyst reports or competing market assessments
- Synthesizing academic papers before writing a literature review
- Reviewing multiple vendor proposals with consistent criteria
- Consolidating stakeholder feedback from different research sessions