AIScholar.cc
Honest comparison

Guided LLM studies vs custom notebooks

Write code for maximum control or use an integrated workflow for supported single-turn studies.

What notebooks and scripts do well

Notebooks and custom code provide maximum control and flexibility: local or private model access, arbitrary APIs, bespoke randomization, statistical methods, and complete control over the analysis. Researchers can publish code and environment details as open methods artifacts. They are often the best fit for specialized pipelines, multi-turn interaction, tool use, or models outside AIScholar's supported provider scope.

Custom code can be just as reproducible—or more reproducible—than a platform when the protocol, dependencies, data, and execution details are documented and preserved.

What an integrated workflow offers

  • Guided study structure

    For supported work, AIScholar connects discrete factors and conditions to single-turn text trials, response coding, analysis, and exports in one workflow.

  • Less bespoke assembly

    The integrated workflow can reduce the amount of custom orchestration researchers need to assemble, while still requiring researchers to inspect designs and outputs.

  • Portable data

    Study data and artifacts can be exported for further inspection or analysis in R, Python, or other tools.

  • Reproducibility is a property of the method

    Recorded prompts and trial details can help inspection, but neither a platform nor a notebook guarantees reproducibility; provider changes and missing protocol details matter.

Trade-offs and portability

Use custom code when you need local/private models, arbitrary APIs, multimodal or multi-turn designs, tool use, specialized methods, or maximal customization. Consider AIScholar for a guided workflow when its supported single-turn, text-only OpenRouter execution and built-in study stages match your question. It does not remove the need for research expertise or assure publication outcomes.

For a study walkthrough, see the prompt-sensitivity example; that import path is the bring-your-own-data walkthrough; the full pipeline is on the features overview; review scope limitations.

All comparisons

Study LLM behavior with a guided research workflow.

Design controlled text studies, collect model responses, and explore what changes across conditions. You decide what the evidence means.

Start your first study