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.
The problem
Reading five papers and asking an AI to summarize them gets you five summaries and no synthesis — no sense of where they agree, where they conflict, or what the weight of evidence actually says. Synthesis is the harder, more useful job, and it falls apart if the sources arrive as one undifferentiated blob or if each summary quietly distorts its source. The reliable path keeps the sources separated and attributed, summarizes each on its own terms, and only then asks the model to reconcile them — so the synthesis is built on faithful parts, not a soup.
Recommended workflow
Each step uses an existing NewPrompt tool, pre-filled by a matching resource. Open the resource to read it, or jump straight into the tool with the inputs ready.
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Package the sources, kept separate
Bundle the sources with clear boundaries and labels so each stays attributable — synthesis depends on knowing which claim came from where.
Goal Delimited, attributed sources the model won't blur together.
Open this step in Long Input Formatter -
Summarize each on its own terms
Summarize every source faithfully before comparing anything — capture its actual claims and method, not the conclusion you're hoping for.
Goal A faithful per-source summary, distortion-free.
Open this step in Structured Summary PromptResource Research Paper Summary Prompt -
Synthesize across the set
Now have the model act as a synthesizer: where do the sources agree, where do they conflict, and what does the balance of evidence support — with each point traced back to a source.
Goal A synthesis that reconciles the sources and cites where each claim came from.
Open this step in System Prompt GeneratorResource Research Synthesis Assistant
Expected outcome
One coherent, source-attributed synthesis — agreements, conflicts, and the weight of evidence — instead of a stack of disconnected summaries you still have to reconcile in your head.
Best for
- Synthesizing a set of papers or reports
- Reconciling sources that partly disagree
- Turning a reading pile into one evidence-weighted view
Not for
- Summarizing a single document — use the AI Long Document Analysis Workflow
- Pulling structured fields out of sources — use the AI Data Extraction Workflow
FAQ
How is this different from the AI Long Document Analysis Workflow?
That workflow analyzes one oversized document and ends with its summary. This one works across many separate sources and ends with a synthesis — where they agree, conflict, and what the evidence weighs to. Different input (many vs one), different output (synthesis vs summary).
Why summarize each source before synthesizing?
Because synthesis built on distorted summaries inherits the distortion. Faithful per-source summaries — kept attributable — are what let the model reconcile claims honestly instead of averaging them into mush.
Does it track which source said what?
Yes — that's why step 1 keeps the sources delimited and labeled. Attribution carries through so the final synthesis can cite where each point came from.