Prompts as versioned, auditable building blocks.
Templates plus a reusable snippet library turn each experimental condition into exact prompt text — reviewable in a variation grid before a single trial runs, and versioned so every result traces back to the words the model actually saw.
Templates and snippets: the separation that keeps studies clean
In AIScholar, a prompt is never a loose blob of text. It is a template with placeholders, filled by named snippets — project-scoped text blocks that each carry multiple indexed versions (v0, v1, v2…). A template references a snippet by name, like {{framing}}-style tags, and each snippet version is bound to one or more levels of a design factor.
At execution time the platform resolves each condition by injecting the snippet version bound to that condition's factor levels. This is the only channel through which a factor level becomes concrete wording — which means the mapping from design to text is explicit, inspectable, and impossible to fudge silently. Single-version snippets act as fixed, shared text across all conditions, so boilerplate stays consistent everywhere.
How prompt construction works
Write or generate snippets
Create named text blocks in a card-based snippet manager, or bulk-edit versions in a spreadsheet-style grid. Each version of a multi-version snippet realizes one level of a factor.
Bind versions to factor levels
Attach each snippet version to the (factor, level) pairs it realizes. The platform validates coverage: every level of every factor a template varies must have a bound version.
Compose templates
Build one or more prompt templates that reference snippets by name. Templates that reference a factor name directly are flagged invalid and blocked from save and run — text must flow through snippets.
Preview the variation matrix
A combinatorial matrix shows every fully-resolved prompt your design will produce, with snippet text highlighted so you can see exactly what varies between conditions. Select or deselect instantiations before running anything.
Version on every edit
Editing a saved template creates a new version rather than overwriting. Trials record which template version they used, so results never point at text that has since changed.
Tools that do the heavy lifting
Snippet library
Card-based manager for all project snippets: names, descriptions, versions, and the factor levels each version realizes.
Variation grid
Spreadsheet-style bulk editing of snippet versions across factors and levels — the fastest way to author a full design's worth of wording.
Template versioning
Every template edit is a new version; every trial records the version it ran. Nothing is lost, nothing is ambiguous.
Live preview with highlighting
Template previews highlight snippet insertions in blue and surface invalid factor-name placeholders in red, so structural errors are visible at a glance.
AI prompt wizard
Generates a complete template-and-snippet set from your design using a creator→verifier loop that catches unbound snippets, uncovered factors, missing level versions, and unknown variables before you see the result. Residual issues are surfaced as explicit warnings.
Prompt variants
Auto-generate semantically equivalent phrasings along six perturbation dimensions — paraphrasing, structure, verbosity, output format, instruction specificity, and persona — so wording becomes a measurable random effect rather than a hidden confound.
Wizard scope, stated plainly
The AI prompt wizard runs only against an empty design slate — re-running it requires explicitly clearing existing templates first, so it can never silently overwrite hand-tuned work. And like every AI wizard in AIScholar, it drafts; you review.
With templates validated and the variation matrix reviewed, the study is ready to run — see experiment execution for how trials are dispatched at scale.
Explore more of the pipeline
- Hypotheses & experimental designFrame testable predictions, then build factorial, fractional, matched-pair, or custom designs with an AI design wizard.
- Experiment executionDispatch trials to any OpenRouter model with concurrency, retries, pilot runs, prompt variants, and cost estimates.
- Response codingScore responses with LLM-as-a-judge, check agreement across judges, and keep humans in the loop with gold-standard review.
Turn a question about LLMs into published research.
Start your first study today. No human participants required.