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.
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.
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.
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.
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.
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.
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.
Use research synthesis when you have several sources and need one reconciled view; use single-document analysis for one oversized document. This workflow keeps many sources separate and ends with a synthesis — agreements, conflicts, weight of evidence — not a per-document summary.
One coherent, source-attributed synthesis across all your sources — where they agree, where they conflict, and what the balance of evidence supports, with each point traced to a source. Not a stack of disconnected summaries you still have to reconcile yourself. Steps package, summarize, then synthesize.
Run three steps in your own AI tool: package the sources kept separate and labeled (long-input-formatter), summarize each faithfully (structured-summary-prompt), then prompt the model to synthesize across the set (system-prompt-generator). NewPrompt supplies the prompts and order; you run each prompt yourself.
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.
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.
Trace each synthesized claim back to its labeled source and check it against that source's faithful summary — attribution and per-claim tracing make this possible. Accuracy is not guaranteed and the AI can misread; human review is required, so you confirm agreements and conflicts before relying on the synthesis.
Complete build journeys that include this workflow as a stage.
The full path to an AI research assistant — define its scope, organize the source corpus, ground responses in references, extract key facts, synthesize findings, check groundedness, then validate it for use.
The full path to a content operation that runs, not a pile of posts — set the editorial strategy, research the topics, build a reusable template, then produce and QA structured pieces on repeat.
Give AI several sources and it pours them into one bowl — attributing Source A's claim to Source B, averaging a real conflict into a smooth "customers generally think," and calling it consensus. Here's how to keep each source separate and attributed so the facts stay traceable to where they came from.
Ask AI to "summarize these sources and list the findings" and one interview becomes "customers," a real disagreement disappears, and no claim shows its source. Build an evidence table instead: one row per claim, with its source, exact quote, and how strong the support really is.
Three sources disagree, you ask AI which is right, and it hands you a confident average that folds a sales objection into a churn analysis. Resolve conflicting sources with AI: map the conflict, compare scope and method, and keep "unresolved, needs review" a real outcome.
Get AI to actually read a document that's too big for one prompt — fit it to the model, split it cleanly, package the parts, and analyze them without losing the thread.
Turn messy text into structured data you can trust enough to feed another system — bound the source, extract the fields, force clean JSON, and validate before it flows downstream.
Turn a meeting transcript into notes people actually use — a faithful summary, the action items pulled out and assigned, and a clean shareable format.