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Please see the EDSL documentation page for more details on each of the object types and methods for looping questions and piping questions and answers that are used below.
We start by creating an initial question (with no content piped into it). EDSL comes with many common question types that we can choose from based on the form of the response that we want to get back from a model (e.g., free text, multiple choice, linear scale, etc.). Here we use a numerical question:
Next we create a question that we will “loop” (repeat) some number of times. We use double braces to create a {{ placeholder }} for content to be added to the question when we create copies of it Here we want to simultaneously set the names of the copies of the question and reference those names in the versions of the question text, so that content from one question and answer can be automatically piped into another cope of the question. To do this, we create placeholders for each question name ({{ num }}) (it must be unique) and question text ({{ text }}). Then in the next step we reference the question names in those texts.
Note:(Note that the names of the placeholders can be anything other than reserved names, and this example works with any other question types as well. We just use a numerical question to keep the responses brief and easy to check!)
Next we create a list of Scenario objects for the question name and question text inputs that we will pass to the loop method that we call on the question in order to create the copies (learn more about using scenarios):
The loop method creates a list of questions with the scenarios added in. Note that because we used single-braces for ease of referencing the piped question names we will see a warning that scenarios require double braces, in case we used the single braces inadvertently. We can ignore this message here, and confirm that our questions have been formatted as intended:
We pass the list of questions to a Survey object as usual in order to administer them together. Note that because we are piping answers into questions, the questions will automatically be administered in the order required by the piping. (If no piping or other survey rules are applied, questions are administered asychronously by default. Learn more about applying survey rules and logic.) We can re-inspect the questions that are now in a survey:
Next we select some models to generate responses (see our models pricing page for details on available models and documentation on specifying model parameters):
We run the survey by adding the models and then calling the run() method on it:
We can see a list of the columns of the dataset of Results that has been generated:
All of these components can be analyzed in a variety of built-in methods for working with results. Here we create a table of responses, together with the question prompts to verify that the piping worked:

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Adding question memory

Re: survey rules mentioned above–here we automatically add a memory of all prior questions to each new question, e.g., to see how this may impact responses:

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Posting to Expected Parrot

Expected Parrot is a platform for posting and sharing AI-based research. It is fully integrated with EDSL and free to use. Learn more about how it works or create an account: https://www.expectedparrot.com/login. In the examples above, results generated using remote inference (run at the Expected Parrot server) were automatically posted to the platform (see links to results). Here we show how to manually post any local content to Expected Parrot, such as this notebook:
Content posted to Expected Parrot can be modified from your workspace or at the web app at any time.