Resource
Bug Triage Assistant
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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.
How to use this resource
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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 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
FAQ
What does the research synthesis prompt produce?
It runs two passes. Pass 1 analyzes each source on its own — key claims, supporting evidence, limitations, and methodology — and Pass 2 synthesizes across them: points of agreement, contradictions with the specific sources named, evidence gaps none of them address, and the most defensible conclusion from the available evidence. Speculative conclusions are tagged "[speculative]".
How is this different from just asking AI to summarize my sources?
A plain summary blends everything into one averaged view where the loudest source wins and disagreements vanish. This processes each source separately before comparing them, so contradictions get surfaced with the sources named instead of smoothed over, and every claim stays attributed to where it came from rather than becoming an anonymous statement. The disagreements and gaps are the point.
Does this check whether my sources are accurate?
No — it organizes and compares what the sources say, but it doesn't fact-check them or judge which is right. It attributes every claim, flags gaps none of the sources cover, and won't fill those gaps with outside knowledge, yet a well-attributed claim can still be wrong. Run it in your own AI tool and verify the underlying sources yourself.
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AI Research Synthesis Workflow
Pull a single coherent view out of a stack of sources — package them together, summarize each faithfully, then have AI synthesize across them instead of one at a time.