AI Research Report Workflow

# AI Research Report Workflow ## Article Summary A high-quality research report is not a one-shot generation. It begins with a decision, decomposes questions, maps sources, cross-validates evidence, and separates fact, inference, and recommendation. --- ## 1. The real objective Use AI to accelerate research while preserving evidence quality, analytical boundaries, and human accountability. Projects usually fail not because the model is completely incapable, but because input, execution, validation, human responsibility, and feedback are not connected into a controlled loop. ## 2. Target architecture 1. **Decision objective and scope**: define inputs, outputs, ownership, and failure handling. 2. **Question tree and hypotheses**: define inputs, outputs, ownership, and failure handling. 3. **Source hierarchy and search plan**: define inputs, outputs, ownership, and failure handling. 4. **Fact cards and evidence library**: define inputs, outputs, ownership, and failure handling. 5. **Conflicts and counterevidence**: define inputs, outputs, ownership, and failure handling. 6. **Calculations and charts**: define inputs, outputs, ownership, and failure handling. 7. **Conclusions, recommendations, and uncertainty**: define inputs, outputs, ownership, and failure handling. 8. **Peer review and version archive**: define inputs, outputs, ownership, and failure handling. ## 3. Implementation steps ### Step 1: State the decision the report supports Retain execution records and critical parameters. Before launch, test normal, abnormal, boundary, and unauthorized paths rather than only the happy path. ### Step 2: Decompose broad questions into testable subquestions Retain execution records and critical parameters. Before launch, test normal, abnormal, boundary, and unauthorized paths rather than only the happy path. ### Step 3: Prioritize official, academic, and primary data Retain execution records and critical parameters. Before launch, test normal, abnormal, boundary, and unauthorized paths rather than only the happy path. ### Step 4: Store source, date, and applicability for critical facts Retain execution records and critical parameters. Before launch, test normal, abnormal, boundary, and unauthorized paths rather than only the happy path. ### Step 5: Actively search for counterexamples Retain execution records and critical parameters. Before launch, test normal, abnormal, boundary, and unauthorized paths rather than only the happy path. ### Step 6: Calculate numbers separately Retain execution records and critical parameters. Before launch, test normal, abnormal, boundary, and unauthorized paths rather than only the happy path. ### Step 7: Label facts, inferences, and recommendations Retain execution records and critical parameters. Before launch, test normal, abnormal, boundary, and unauthorized paths rather than only the happy path. ### Step 8: Have another reviewer audit critical evidence Retain execution records and critical parameters. Before launch, test normal, abnormal, boundary, and unauthorized paths rather than only the happy path. ## 4. Quality and operating metrics - **Critical facts with sources**: define a baseline, target, and alert threshold. - **Primary-source ratio**: define a baseline, target, and alert threshold. - **Citation support rate**: define a baseline, target, and alert threshold. - **Conflict-resolution rate**: define a baseline, target, and alert threshold. - **Material numeric errors**: define a baseline, target, and alert threshold. - **Human verification time**: define a baseline, target, and alert threshold. - **Recommendation adoption**: define a baseline, target, and alert threshold. ## 5. Common failure modes - Writing the conclusion before research. - Relying on secondary repetition. - Using search snippets instead of sources. - Hiding uncertainty. - Omitting the research cutoff date. ## 6. Implementation recommendations - Separate the evidence library from report prose. - Use independent evidence types for critical conclusions. - Mark unverifiable information explicitly. ## 7. Launch checklist - Are input data, permissions, and retention defined? - Are model, prompt, tool, and rule versions recorded? - Are deterministic checks and human review points present? - Can the workflow retry and roll back without duplicate execution? - Can quality, cost, latency, and business outcomes be measured? - Are alerting, disablement, and incident procedures available? ## Conclusion The correct approach is not to maximize one isolated capability. Build evaluation criteria, permission boundaries, and a continuous improvement loop around real work. Validate on a narrow production-like scope before expanding. For more practical AI product comparisons and production engineering guidance, visit **Zyentor Picks**: https://www.zyentorpicks.com/.