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Survey logic lets an interview respond to the answers it receives. You can screen out respondents who have not used a product, ask a follow-up about a problem they described, or repeat a question for each item they name. These patterns are useful whenever asking everyone the same fixed sequence would produce irrelevant questions. Three EDSL features work together here. Branching decides which question comes next. Piping inserts an earlier answer into a later question’s wording. Memory gives the model earlier questions and answers as context when it answers a later question. A survey can use any combination of these features.

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Build a follow-up interview

The example below asks whether a respondent has used an export feature. A “No” ends the interview. A “Yes” leads to a question about problems, followed by a question about how to resolve the reported problem. It demonstrates all three features in a three-question survey:

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Check the paths

add_stop_rule() evaluates the answer to used after that question is answered. If the answer is No, neither problem nor followup is asked. Otherwise, the survey continues in its original order. In the last question, {{ problem.answer }} is replaced by the respondent’s actual answer. If they answered “The export button is hidden,” the question becomes “What would resolve this problem: The export button is hidden?” add_targeted_memory(followup, problem) also supplies the preceding question and its answer as context. This illustrates both mechanisms; piping alone would be enough if you only wanted to insert the problem text. With model access configured, run the survey and view the answers together:
This returns one interview with an answer column for each question; skipped questions have no substantive answer. To ask the same survey of several profiles or about several products, attach agents or scenarios with survey.by(...) before running it. For a closing message on an early-exit path, see Survey messages.

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Choose which prior answers a model sees

Use memory when a later answer should take earlier exchanges into account without copying them into the question text. For example, a final recommendation can consider several earlier answers while keeping its wording simply “What should we improve first?” Choose how much context to include: These methods modify the survey and return it for chaining. Targeted memory is useful for a specific dependency, lagged memory for a moving conversation, and full memory for an interview in which every earlier exchange is relevant. More memory also means more text in later model prompts.

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Jump to another question

Ending the interview is only one routing choice. In a longer survey, you might want non-users to skip the feature questions but still answer a final summary question. If that survey contains a later question named summary, use survey.add_rule("used", "{{ used.answer }} == 'No'", next_question="summary") to jump to it. Use add_skip_rule() to skip a particular question before it is asked, add_rule() to choose a destination after an answer, and add_stop_rule() to end the interview. Conditions can refer to available scenario values or earlier answers; an answer from a question that has not been asked cannot supply the intended routing information. survey.clear_non_default_rules() removes custom routing and restores the default forward sequence. See Surveys for the complete routing methods.

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Ask follow-ups for items in a list answer

Sometimes you do not know the follow-up topics in advance. Survey.loop_over() can take a list answer and ask the same follow-up about each item. The example below first requests up to three features, then asks what would improve each named feature. The follow-up template must not already be in the survey:
{{ item }} receives one list item at a time. If the answer lists “exports” and “search,” the respondent gets a follow-up about each; the unused third position is skipped. EDSL builds the three possible follow-up questions in advance, so each answer has its own question name in the results. survey.loop_question_groups() reports which questions belong to each loop. For conditional follow-ups within the loop, attach detail.skip_when(expression) or detail.jump_when(expression, to="another_template_name") before calling loop_over(). For a fixed set of multiple-choice options, survey.add_followup_questions(reference_question, followup_template, answer_template_var="answer") creates a follow-up for each option and routes according to the selected answer. Use {{ answer }} in that template for the option text. If you already know the items before the interview, question.loop(scenarios) instead creates a question for each supplied scenario. To ask each item in a separate interview, use question.by(scenarios). See Experimental conditions for choosing between those designs.

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Compute scores within a survey

A follow-up can also use a calculated value. For a survey containing numeric questions named score_a and score_b, survey.add_summation_question(question_name="total", include=["score_a", "score_b"]) adds a question that computes their sum. add_weighted_linear_scale_sum() creates a score from linear-scale questions with specified weights. These calculations use the earlier answers directly and do not ask a model to do the arithmetic. Use them when the score belongs in the interview itself; use Results transformations when you only need it during analysis.