Docassemble interview logic determines how users interact with legal automation systems, guiding them through complex processes while collecting accurate information. Effective interview logic creates intuitive, efficient experiences that produce reliable results. Poor interview logic, however, can confuse users, produce errors, or fail to collect necessary information, undermining the value of automation.
This guide explains how to design effective Docassemble interview logic, covering design principles, conditional branching, question flow, error handling, and best practices. Whether you’re building new Docassemble interviews, improving existing ones, or evaluating interview design approaches, this guide provides the practical guidance you need to create effective interview logic.
You’ll learn what interview logic is, how it works in Docassemble, how to design effective conditional branching, how to structure question flow, how to handle errors and edge cases, and what best practices ensure maintainable, reliable interviews. This guide is written for developers building Docassemble systems, legal teams implementing interviews, and organizations designing legal automation workflows who need implementation-level guidance, not just high-level concepts.
Understanding Interview Logic
Interview logic is the system of rules and conditions that determines how Docassemble interviews behave, what questions appear, and how user responses are processed.
What Interview Logic Is
Interview logic encompasses the conditional rules, question sequencing, validation, and data processing that make interviews work effectively.
Conditional Rules: Logic determines which questions appear based on previous answers, ensuring users only see relevant questions for their situation.
Question Sequencing: Logic determines the order in which questions appear, creating a logical flow that matches how users think about information.
Data Processing: Logic processes user responses, performing calculations, validations, and transformations needed for document generation.
Workflow Control: Logic controls interview flow, determining when to move to the next section, when to validate, and when to generate documents.
How It Works in Docassemble
Docassemble implements interview logic through YAML question definitions and Python code that processes responses and controls flow.
YAML Question Definitions: Questions are defined in YAML files, specifying question text, variable names, validation rules, and conditional logic.
Python Code: Python modules process responses, perform calculations, implement complex logic, and control interview flow programmatically.
Variable System: Docassemble uses a variable system where questions collect values into variables, and logic uses these variables to determine subsequent behavior.
Conditional Blocks: Conditional blocks in YAML determine which questions appear based on variable values, creating branching interview paths.
Role in Legal Automation
Interview logic is central to legal automation because it determines how users interact with systems and how accurate information is collected.
User Experience: Interview logic directly affects user experience, determining whether interviews are intuitive, efficient, and easy to complete.
Data Quality: Effective logic ensures accurate data collection through validation, conditional questions, and clear guidance.
Process Efficiency: Well-designed logic makes processes efficient by asking only relevant questions and guiding users through complex requirements.
Compliance: Logic ensures compliance by collecting all required information and validating that information meets legal requirements.
Design Principles
Effective interview logic follows design principles that ensure interviews work well for users and produce reliable results.
Logical Flow
Interviews should flow logically, matching how users think about information and legal processes.
Natural Progression: Questions should progress naturally from general to specific, from simple to complex, matching how users understand their situations.
Contextual Grouping: Related questions should be grouped together, maintaining context and reducing cognitive load.
Clear Purpose: Each section should have a clear purpose that users understand, helping them see progress and understand what’s being collected.
Progressive Disclosure: Information should be disclosed progressively, showing users what they need when they need it, rather than overwhelming them with all questions at once.
User Experience
Interview logic should prioritize user experience, making interviews easy to complete and understand.
Clarity: Questions should be clear and understandable, using plain language and avoiding unnecessary legal jargon.
Guidance: Interviews should provide guidance throughout, explaining why information is needed and how to provide it correctly.
Feedback: Users should receive feedback on their progress, understanding where they are in the process and what remains.
Error Recovery: When errors occur, interviews should help users recover easily, providing clear error messages and allowing corrections.
Conditional Branching
Conditional branching adapts interviews to user situations, but must be designed carefully to avoid complexity.
Appropriate Branching: Use branching when it significantly improves user experience or ensures accuracy, not for minor variations.
Clear Conditions: Branching conditions should be clear and testable, avoiding complex nested conditions that are difficult to understand or maintain.
Tested Paths: All branching paths should be tested to ensure they work correctly and collect necessary information.
Documented Logic: Complex branching logic should be documented so future maintainers can understand and update it.
Error Handling
Robust error handling ensures interviews continue working even when unexpected situations occur.
Input Validation: Validate user input at the point of entry, checking data types, formats, ranges, and required fields.
Clear Error Messages: Provide clear, actionable error messages that explain what went wrong and how to fix it.
Graceful Recovery: Allow users to correct errors and continue, rather than forcing them to restart interviews.
Error Logging: Log errors for debugging and monitoring, but don’t expose technical details to users.
Conditional Logic and Branching
Conditional logic and branching adapt interviews to user situations, but must be designed carefully.
When to Use Conditional Logic
Conditional logic should be used when it significantly improves user experience or ensures accuracy.
Relevant Questions Only: Use conditional logic to show only questions relevant to the user’s situation, reducing interview length and complexity.
Accuracy Improvement: Use conditional logic when it improves accuracy by ensuring users provide appropriate information for their situation.
Process Efficiency: Use conditional logic to make processes more efficient by skipping irrelevant sections and focusing on what matters.
Compliance Requirements: Use conditional logic to ensure compliance by collecting all required information based on case type or other factors.
Effective Branching Patterns
Effective branching patterns create clear, maintainable logic that adapts to user situations.
Simple Conditions: Use simple, clear conditions for branching, avoiding complex nested conditions that are difficult to understand or maintain.
Grouped Scenarios: Group similar scenarios together, using conditional questions to distinguish between them rather than creating separate paths for every variation.
Clear Decision Points: Make decision points clear to users, explaining why certain questions appear and what they determine.
Tested Branches: Ensure all branches are tested, including edge cases and rare scenarios, to prevent production failures.
Avoiding Complexity
Complex conditional logic can make interviews difficult to maintain and understand.
Simplicity First: Prefer simple logic over complex solutions. If a question applies to most users, ask it directly rather than creating complex conditional logic.
Avoid Over-Branching: Don’t create branches for every possible variation. Group similar scenarios and use clear conditional questions.
Clear Structure: Maintain clear structure in conditional logic, using comments and documentation to explain complex decisions.
Maintainability: Design logic for maintainability, ensuring future developers can understand and update it.
Testing Branches
Thorough testing ensures all branches work correctly and collect necessary information.
All Paths: Test all branching paths, including main paths, edge cases, and rare scenarios.
Edge Cases: Test edge cases and boundary conditions to ensure logic handles unusual situations correctly.
Integration Testing: Test how branches integrate with document generation and other system components.
User Testing: Have real users test interviews to identify usability issues and ensure logic works as intended.
Question Design and Flow
Effective question design and flow ensure users can complete interviews efficiently and accurately.
Question Sequencing
Questions should be sequenced logically, matching how users think about information.
Logical Order: Present questions in a logical order that matches how users understand their situations, starting with broad information and narrowing to specifics.
Contextual Grouping: Group related questions together, maintaining context and reducing the need to recall information from earlier sections.
Progressive Complexity: Start with simple questions and progress to more complex ones, building user confidence and understanding.
Natural Flow: Questions should flow naturally from one to the next, with each question building on previous information.
Progressive Disclosure
Progressive disclosure shows users only what they need when they need it, reducing cognitive load.
Relevant Questions Only: Show only questions relevant to the user’s situation, using conditional logic to skip irrelevant sections.
Contextual Information: Provide contextual information as users progress, helping them understand where they are and what’s needed.
Section-Based Flow: Organize interviews into clear sections, with each section focusing on a specific aspect of the process.
Progress Indicators: Show users their progress through the interview, helping them understand how much remains.
User Guidance
Effective guidance helps users complete interviews successfully and accurately.
Clear Questions: Write questions in plain language, avoiding legal jargon when possible. If legal terms are necessary, provide definitions.
Helpful Explanations: Provide explanations for why information is needed and how to provide it correctly.
Examples: Include examples where helpful, showing users what kind of information is expected.
Validation Feedback: Provide immediate feedback on input validation, helping users correct errors quickly.
Validation
Input validation ensures data quality and prevents errors before they cause problems.
Real-Time Validation: Validate input as users provide it, providing immediate feedback on errors.
Clear Validation Rules: Make validation rules clear to users, explaining what format or values are expected.
Helpful Error Messages: Provide helpful error messages that explain what went wrong and how to fix it.
Required Field Indication: Clearly indicate required fields, helping users understand what information is necessary.
Error Handling and Edge Cases
Robust error handling and edge case management ensure interviews work reliably in all situations.
Input Validation
Comprehensive input validation prevents errors and ensures data quality.
Data Type Validation: Validate that input matches expected data types, such as dates, numbers, or text formats.
Range Validation: Validate that values fall within acceptable ranges, such as dates within reasonable bounds or numbers within expected limits.
Format Validation: Validate that input matches required formats, such as phone numbers, email addresses, or identification numbers.
Required Field Validation: Ensure required fields are completed before allowing users to proceed.
Error Messages
Clear error messages help users understand and fix problems quickly.
Actionable Messages: Error messages should be actionable, explaining what went wrong and how to fix it.
Plain Language: Use plain language in error messages, avoiding technical jargon that confuses users.
Contextual Help: Provide contextual help with error messages, linking to explanations or examples when helpful.
Positive Tone: Frame error messages positively, focusing on solutions rather than problems.
Edge Case Handling
Edge cases should be handled gracefully, ensuring interviews work in all situations.
Identify Edge Cases: Think through rare scenarios, boundary conditions, and unusual user situations that might occur.
Plan Handling: Plan how to handle edge cases, either by asking for clarification, providing defaults, or explaining why cases cannot be handled.
Test Edge Cases: Test edge cases thoroughly to ensure they are handled correctly and don’t cause system failures.
Document Handling: Document how edge cases are handled so future maintainers understand the logic.
Graceful Degradation
Interviews should degrade gracefully when errors occur, allowing users to recover and continue.
Error Recovery: Allow users to correct errors and continue, rather than forcing them to restart interviews.
Partial Completion: Support partial completion, allowing users to save progress and return later if needed.
Fallback Values: Use fallback values for optional fields or when data is missing, ensuring interviews can complete even with incomplete information.
Error Logging: Log errors for debugging and monitoring, but don’t expose technical details to users.
Best Practices
Following best practices ensures interviews are maintainable, reliable, and effective.
Structuring Interviews
Well-structured interviews are easier to maintain and provide better user experience.
Clear Sections: Organize interviews into clear sections with descriptive headings that help users understand what information is being collected.
Logical Flow: Maintain logical flow throughout interviews, ensuring questions progress naturally and build on previous information.
Consistent Formatting: Use consistent formatting for questions, help text, and validation messages, creating a professional, cohesive experience.
Modular Design: Design interviews modularly, with reusable components that can be shared across multiple interviews.
Code Organization
Well-organized code makes interviews easier to maintain and update.
Modular YAML: Break interviews into logical modules using YAML blocks and includes, separating different sections or components.
Reusable Components: Create reusable question blocks, validation functions, and logic components that can be used across multiple interviews.
Clear Naming: Use clear, descriptive names for variables, functions, and blocks that indicate purpose and make code self-documenting.
Separation of Concerns: Separate interview logic (YAML) from business logic (Python), keeping YAML focused on questions and flow.
Maintainability
Maintainable interviews can be updated and improved over time without breaking existing functionality.
Documentation: Document complex logic, edge cases, and design decisions so future maintainers can understand and update interviews.
Version Control: Use version control to track changes, enabling rollback if updates cause problems.
Testing: Maintain comprehensive tests that verify interviews work correctly after changes.
Incremental Updates: Make updates incrementally, testing thoroughly after each change to ensure nothing breaks.
Performance
Interview performance affects user experience and system efficiency.
Efficient Logic: Use efficient conditional logic and calculations that don’t slow down interviews.
Optimized Questions: Optimize question flow to minimize unnecessary questions and reduce interview length.
Caching: Cache calculations and data lookups when appropriate to improve performance.
Resource Management: Manage system resources effectively to ensure interviews perform well under load.
Getting Started with Interview Logic
If you’re designing Docassemble interview logic, a structured approach helps ensure success.
Planning and Design
Start by planning interview structure and logic before implementation.
Requirements Analysis: Analyze requirements to understand what information must be collected and what logic is needed.
User Flow Design: Design user flow, mapping out how users will progress through interviews and what questions they’ll encounter.
Logic Design: Design conditional logic, identifying where branching is needed and how conditions should work.
Validation Planning: Plan validation requirements, identifying what data must be validated and how validation should work.
Implementation Support
Working with Docassemble specialists can help ensure effective implementation.
Expertise: Specialists with experience in Docassemble interview design can provide valuable expertise and guidance.
Best Practices: Specialists bring best practices from similar implementations, helping avoid common pitfalls and ensure success.
Technical Support: Specialists can provide technical support for complex logic, integration, and optimization.
Quality Assurance: Specialists can help ensure interviews meet quality standards and work correctly in production.
Working with Specialists
Docassemble interview logic benefits from working with specialists who understand both technology and legal workflows.
Domain Knowledge: Specialists with legal domain knowledge understand unique requirements and can design interviews that fit legal workflows.
Technical Expertise: Specialists provide technical expertise needed for effective interview implementation and optimization.
Partnership Approach: Working with specialists as partners ensures interviews meet needs and provide long-term value.
If you’re designing Docassemble interview logic or improving existing interviews, talk to a Docassemble expert about interview design, implementation approaches, and best practices. We help legal teams design and implement effective Docassemble interview logic that creates intuitive, efficient experiences and produces reliable results.
English