Why it helps
A method suggestion is not an experiment result.
Discussions about missing values, data leakage, and evaluation choices accumulate alongside the actual analysis. When the write-up begins, it can be difficult to recover why you chose an approach. Give each question a clear conversation and retain the checked conclusions with your project, while keeping datasets, execution results, and environment details in your analysis tools.
Distinguish discussion from execution
Keep suggested approaches separate from results you have actually measured in a notebook or experiment system.
Find a previous methodological question
Search synced messages by metric or function name when you need to revisit an assumption rather than asking it again.
Make reviews more systematic
Reuse a prompt that asks about leakage, validation, baselines, and missing evidence before discussing a model's performance.
Your everyday tools
Group discussions by analysis project
Create Library returns model with Feature timing and Validation design as subfolders. Include the prediction question and dataset version in conversation titles. These labels organize your discussions; they do not version data or track experiments.
See the feature demoRetrieve the reasoning by a technical term
After archive synchronization, search for a metric, transformation, or function in message content. Use the matching conversation to understand the earlier reasoning and check whether it still applies to the current dataset.
See the feature demoRead analytical snippets more clearly
Choose a code block theme and adjust chat width for Python, R, or SQL examples. Presentation controls help you inspect the answer; execute and test every snippet in the appropriate analysis environment.
See the feature demoKeep the discussion with your analysis notes
Export all messages as Markdown so both the original assumptions and suggested methods remain visible. Add verified results and references in your notebook or report. An export does not capture the dataset, random seed, or runtime environment.
See the feature demoTool availability and allowances depend on your plan. Smart Search requires Power access and Chat archive synchronization; Prompt Guard also requires Power access and must be enabled. Check the current feature allowances.
Example workflow
Check what the model could know at prediction time.
A model predicting overdue library returns might accidentally use renewal information recorded after checkout. Inspect feature timing before trusting an evaluation score.
Define the prediction moment
Create Library returns model / Feature timing. Start Renewal event — availability check and supply an approved schema, the prediction moment, and how each field is recorded, using synthetic examples without borrower records.
Review the proposed features
Insert your feature-timing prompt. Ask which fields are known at checkout, which arrive later, and which cannot be classified without checking the data pipeline.
Verify against the real pipeline
Inspect event timestamps and feature construction in your analysis environment. Run the appropriate validation checks there and record the actual dataset version, split, baseline, and measured results.
Keep the reasoning beside the experiment
Return with verified findings and export as Feature timing review.md. Store it beside the experiment record so a reviewer can see why a feature was excluded without treating the chat as proof that the code implements the decision.
A feature-timing prompt to save
Save these instructions in Prompt Manager. Replace the bracketed text with your own context before sending.
Review this overdue-return model’s proposed features relative to the stated prediction moment. Classify each as Available then, Available later, or Timing unclear using only the supplied field definitions. Explain the evidence for each classification and identify pipeline checks needed to resolve uncertainty. Do not invent validation scores or assume timestamps are reliable. Prediction moment: [definition] Approved schema and feature logic: [details] Synthetic event sequence: [example]
Questions about this workflow
Does StylerGPT track experiments or run notebooks?
No. It organizes your ChatGPT conversations and their presentation. Keep execution, datasets, model artifacts, and experiment tracking in your existing analysis tools.
Can I search for a function or metric in old messages?
Yes, Smart Search searches the titles and message text of synced conversations. Check the surrounding context and dataset assumptions before reusing the earlier advice.
Will an export make the analysis reproducible?
No. A transcript records the discussion. Reproducibility also needs the actual code, data version, environment, seeds where relevant, and measured results.