Every result traces back to the exact words that produced it.
Versioned prompts and coding schemes, complete trial metadata, an auto-generated Method Specification Prompt, and a one-click reproducibility package — so anyone can re-run your study, including you, six months later.
Why LLM research has a reproducibility problem
Model outputs are stochastic, providers update models behind stable-looking names, and a single word changed in a prompt can move results. A study that reports "we asked GPT-4" is unreproducible on its face. AIScholar's answer is to record everything that could matter, automatically, at the moment it happens — not to reconstruct it afterward from memory and scattered notebooks.
The platform's discipline is simple: nothing is overwritten, everything is versioned, and every artifact knows its lineage. A coded value points to the coding-scheme version that produced it; a trial points to the template version, snippet versions, model string, and parameters it ran with; an analysis points to the coded data it consumed.
What gets captured
Versioned prompts & schemes
Editing a prompt template or coding scheme creates a new version rather than mutating the old one. Past results keep pointing at exactly the text they were produced with.
Full trial metadata
Every trial stores its resolved prompt, model version string, sampling parameters, seed (and whether the provider honored it), latency, token usage, cost, and validity classification.
Method Specification Prompt
An auto-generated, self-contained specification of your entire method — design, prompts, parameters, coding procedure — precise enough for an independent researcher (or another AI system) to re-implement the study.
Deterministic condition resolution
The design-to-prompt mapping flows through explicitly bound snippet versions, so the transformation from factor levels to final text is mechanical and auditable — never a hidden f-string.
Reproducibility package
One click exports a complete archive: design, all prompt template and snippet versions, per-trial data, coding schemes and coded values, analyses, and the Method Specification Prompt.
Data export
Raw and coded data export as CSV or XLSX at any point — your data is never locked in.
What this looks like in practice
During the study
You work normally. Versioning and metadata capture happen automatically on every save, run, and coding pass — reproducibility is a side effect of using the platform, not extra work.
At write-up time
The methods section is generated from recorded facts: exact model versions, replication counts, reliability statistics. No archaeology through old chat logs.
At publication
Attach the reproducibility package as supplementary material. Reviewers and replicators get the full lineage — including the exact prompts, which most LLM papers still omit.
Honest about the limits
Perfect bit-level reproducibility is not in any platform's gift: providers deprecate models and may not honor seeds. AIScholar records what was true at run time — exact model strings and seed honor status included — so replications can distinguish "the method changed" from "the model changed."
The same audit-trail philosophy extends to compliance paperwork in R&D documentation.
Explore more of the pipeline
- Write-up assistanceDraft manuscript sections with AI, manage citations in five styles, verify references, and export to Word, Markdown, or LaTeX.
- R&D documentationCapture uncertainty statements, a contemporaneous activity log, and effort timesheets as you work — then export the evidence package.
- Hypotheses & experimental designFrame testable predictions, then build factorial, fractional, matched-pair, or custom designs with an AI design wizard.
Turn a question about LLMs into published research.
Start your first study today. No human participants required.