An AI Research Workflow From First Question to Reviewable Output
A repeatable AI research workflow for framing questions, gathering evidence, synthesizing findings, and reviewing outputs.
A reliable AI research workflow has seven stages: define the decision, decompose the question, plan the evidence, retrieve sources, extract claims, synthesize with uncertainty, and review the deliverable. AI can assist at every stage, but the researcher remains responsible for scope, source selection, interpretation, and final use.
1. Define the decision before searching
‘Research electric vehicles’ is a topic, not a research question. A workable brief names the decision and constraints: ‘Compare the total five-year ownership considerations for two vehicle classes in India for a city driver, using current official tax, charging, warranty, and efficiency information.’ The answer can now be judged against a purpose.
Audience and decision
Geography and time range
Terms that need definitions
Required source types
Output format and deadline
What would count as insufficient evidence
2. Break the question into evidence-sized parts
Complex questions usually hide several subquestions. Separate definitions, current facts, mechanisms, comparisons, counterarguments, and unknowns. This prevents one broad search from returning a convenient but incomplete narrative. It also makes parallel research easier without pretending that a real team or measured productivity gain exists.
3. Write an evidence plan
Choose sources before conclusions. For a policy comparison, the plan may prioritize enacted text, regulator guidance, implementation data, and then reputable analysis. For scientific research, it may include reviews, primary studies, methods, datasets, corrections, and replication evidence. PRISMA is specifically a reporting guideline for systematic reviews, not a universal template, but its emphasis on transparent reasons, methods, and results is instructive.
4. Retrieve broadly, then narrow deliberately
Run distinct searches for official sources, data, critical views, and recent updates.
Record why each source is included rather than saving everything.
Prefer stable identifiers such as DOIs for scholarly work.
Capture dates, versions, geographic scope, definitions, and access limits.
Stop when new sources repeat known evidence and remaining gaps are explicit.
Crossref’s public REST API exposes deposited scholarly metadata, including identifiers and post-publication updates where supplied. Metadata is useful for discovery and identity checks, but it is not a substitute for reading the work.
5. Extract claims, not just summaries
For every important source, capture the claim, supporting passage or table, source type, scope, date, limitations, and your interpretation. Keep those fields separate. A summary collapses information; a claim ledger preserves the pieces needed to audit a synthesis.
A compact claim-ledger format
Claim: the narrow statement the source supports.
Evidence: quotation, statistic, table, or method result.
Locator: URL, DOI, page, section, or row.
Scope: population, place, period, and definition.
Status: supported, disputed, inferred, stale, or unresolved.
Use: where the claim belongs in the final output.
6. Synthesize across sources
Synthesis is not concatenation. Group evidence by question, explain agreement, and investigate disagreement. Sources can conflict because they use different dates, denominators, samples, definitions, or incentives. State which explanation is supported and which remains a hypothesis. The National Academies describes scientific knowledge as durable yet mutable and emphasizes communicating uncertainty, a sound principle for research beyond science as well.
7. Build and review the output
Choose the smallest format that answers the decision: a brief, comparison list, chart, report, or annotated source set. In Rixx, web research and supported documents can continue into charts, reports, writing blocks, generated files, or saved Insights where available. Review the output against the brief rather than judging fluency.
Does the opening answer the actual question?
Can every consequential factual claim be traced?
Are dates, units, and definitions consistent?
Are inference and evidence visibly different?
Did the output retain disagreements and limitations?
Could another person reproduce the source path?
Is expert review required before use?
Example: a competitive product investigation
Assume the decision is whether a small organization should switch from one service to another. Decompose the question into required capabilities, migration constraints, total cost, security documentation, data export, support, and operational risk. Use current vendor documentation for product facts, direct testing only where it actually occurred, and independent evidence for outages or user-reported limitations. Do not turn anecdotal reports into prevalence claims.
The claim ledger might show that both products document the required capability, one restricts it to a higher plan, and the other lacks a needed export format. A recommendation follows from the organization’s criteria, not from the volume of positive coverage. The final brief should say which facts were verified, which depend on vendor claims, and which need a trial.
Workflow failure modes
The decision is never stated, so research expands without a stopping rule.
Subquestions are chosen after a preferred conclusion emerges.
Searches use only confirming language.
Notes blend quotation, paraphrase, and inference.
Sources are saved without dates or locators.
A chart is created before definitions and units are reconciled.
The final draft adds claims that never appeared in the ledger.
Review checks grammar but not evidence or reasoning.
When to iterate and when to stop
Iterate when a load-bearing claim has only secondary support, sources conflict for an unexplained reason, the decision criteria change, or a missing value could reverse the conclusion. Stop when each subquestion is answered to the required confidence, material gaps are explicit, and additional credible sources mainly repeat existing evidence. If the evidence cannot resolve the decision, say what new data or test would.
Handoff package
Final brief and scope
Search and inclusion notes
Source register
Claim ledger
Calculations and chart data
Draft output
Unresolved questions
Review status and as-of date
The workflow is complete when the output can be questioned without the research disappearing behind it.
Final decision test
Before using this guidance, return to the actual decision and test it against AI research workflow, AI research process, research with AI, and source verification workflow. Record which evidence is direct, which conclusion is inferred, which facts can change, and who will review the result. Check the strongest counterexample, preserve source dates and definitions, and stop when missing evidence could reverse the decision. A useful output should remain understandable without hidden chat context and correctable when a source changes. Do not convert an unavailable fact into an estimate, an example into a testimonial, or a product direction into a promise.
