PromptBuilder.constraint()`method`

Add a standard constraint to the prompt that will appear in the additional constraints section.

USAGE

PromptBuilder.constraint(constraint)

Standard constraints are important requirements and guidelines that shape the AI’s response but are not as critical as front-loaded constraints. These constraints appear in the ADDITIONAL CONSTRAINTS section after the main task context and structured sections, providing important guidance while maintaining the attention hierarchy of the prompt.

Parameters

constraint : str: Specific constraint, requirement, or guideline that should influence the AI’s response. Should be clear and actionable, using directive language when appropriate (e.g., "Use clear, concise language", "Include practical examples", "Avoid overly technical jargon", etc.).

Returns

PromptBuilder: Self for method chaining, allowing combination with other prompt building methods to create comprehensive, structured prompts.

Research Foundation

Positioning Strategy Theory. Standard constraints are positioned after critical constraints and core content to maintain optimal attention flow. This positioning ensures that essential task information receives primary focus while still communicating important requirements and preferences to the model.

Constraint Hierarchy Management. Standard constraints appear in the order they are added, after any critical constraints. This allows for logical grouping of related requirements and systematic constraint organization that respects cognitive processing priorities.

Use Case Classification. Standard constraints are ideal for quality preferences and style guidelines, secondary requirements that enhance output quality, behavioral preferences that improve response tone, technical preferences for implementation approaches, and context-specific guidelines that refine the response scope.

Differentiated Constraint Strategy. While critical_constraint() is used for non-negotiable requirements that must be front-loaded, constraint() is used for important but secondary requirements that guide response quality and style without overriding primary attention allocation.

Integration Notes

Attention Hierarchy: standard constraints appear after critical content to maintain focus
Quality Enhancement: these constraints refine and improve response quality without overriding priorities
Flexibility: supports diverse requirement types from technical to behavioral to domain specific
Systematic Organization: constraints are grouped logically in the final prompt structure
Complementary Function: works alongside critical constraints to create comprehensive requirement sets

The .constraint() method provides flexible, systematic way to communicate important requirements and preferences that enhance response quality while respecting the overall attention optimization strategy of the prompt building system.

Examples

Quality and style constraints

Add constraints that improve response quality and consistency:

import talk_box as tb

builder = (
    tb.PromptBuilder()
    .persona("technical writer", "API documentation")
    .task_context("create user guide for authentication API")
    .constraint("use clear, concise language appropriate for developers")
    .constraint("include practical code examples for each endpoint")
    .constraint("provide troubleshooting guidance for common issues")
    .core_analysis([
        "authentication flow and requirements",
        "error handling and status codes",
        "rate limiting and best practices"
    ])
)

print(builder)

You are a technical writer with expertise in API documentation.

CRITICAL REQUIREMENTS:
- use clear, concise language appropriate for developers

TASK: create user guide for authentication API

CORE ANALYSIS (Required):
- authentication flow and requirements
- error handling and status codes
- rate limiting and best practices

ADDITIONAL CONSTRAINTS:
- include practical code examples for each endpoint
- provide troubleshooting guidance for common issues

Technical preference constraints

Guide implementation approaches and technical choices:

builder = (
    tb.PromptBuilder()
    .persona("senior software architect", "microservices")
    .critical_constraint("focus only on production-ready patterns")
    .task_context("review microservices architecture design")
    .constraint("prefer established patterns over novel approaches")
    .constraint("consider scalability implications for each recommendation")
    .constraint("include performance trade-offs in analysis")
    .core_analysis([
        "service decomposition strategy",
        "inter-service communication patterns",
        "data consistency approaches"
    ])
)

print(builder)

You are a senior software architect with expertise in microservices.

CRITICAL REQUIREMENTS:
- focus only on production-ready patterns

TASK: review microservices architecture design

CORE ANALYSIS (Required):
- service decomposition strategy
- inter-service communication patterns
- data consistency approaches

ADDITIONAL CONSTRAINTS:
- prefer established patterns over novel approaches
- consider scalability implications for each recommendation
- include performance trade-offs in analysis

Behavioral and tone constraints

Shape the AI’s communication style and approach:

builder = (
    tb.PromptBuilder()
    .persona("senior developer", "code quality")
    .task_context("review pull request for junior developer")
    .constraint("provide constructive, encouraging feedback")
    .constraint("explain the reasoning behind each suggestion")
    .constraint("include positive reinforcement for good practices")
    .constraint("suggest learning resources for improvement areas")
    .core_analysis([
        "code correctness and logic",
        "security considerations",
        "maintainability and readability"
    ])
)

print(builder)

You are a senior developer with expertise in code quality.

CRITICAL REQUIREMENTS:
- provide constructive, encouraging feedback

TASK: review pull request for junior developer

CORE ANALYSIS (Required):
- code correctness and logic
- security considerations
- maintainability and readability

ADDITIONAL CONSTRAINTS:
- explain the reasoning behind each suggestion
- include positive reinforcement for good practices
- suggest learning resources for improvement areas

Context-specific constraints

Add domain or situation-specific requirements. In this example for a healthcare application, we focus on HIPAA compliance and patient privacy.

builder = (
    tb.PromptBuilder()
    .persona("healthcare software architect", "HIPAA compliance")
    .critical_constraint("all recommendations must maintain patient privacy")
    .task_context("design patient data management system")
    .constraint("consider healthcare industry regulations")
    .constraint("prioritize data security over performance optimizations")
    .constraint("include audit trail requirements in recommendations")
)

print(builder)

You are a healthcare software architect with expertise in HIPAA compliance.

CRITICAL REQUIREMENTS:
- all recommendations must maintain patient privacy

TASK: design patient data management system

ADDITIONAL CONSTRAINTS:
- consider healthcare industry regulations
- prioritize data security over performance optimizations
- include audit trail requirements in recommendations

Multiple related constraints

Group related constraints for comprehensive guidance. This example focuses on data analysis with multiple quality constraints:

builder = (
    tb.PromptBuilder()
    .persona("data scientist", "business analytics")
    .task_context("analyze customer behavior patterns")
    .constraint("support findings with statistical evidence")
    .constraint("use clear visualizations to illustrate trends")
    .constraint("explain methodology and assumptions clearly")
    .constraint("provide actionable business recommendations")
    .constraint("include confidence levels for predictions")
    .core_analysis([
        "customer segmentation patterns",
        "behavioral trend analysis",
        "predictive modeling opportunities"
    ])
)

print(builder)

You are a data scientist with expertise in business analytics.

CRITICAL REQUIREMENTS:
- support findings with statistical evidence

TASK: analyze customer behavior patterns

CORE ANALYSIS (Required):
- customer segmentation patterns
- behavioral trend analysis
- predictive modeling opportunities

ADDITIONAL CONSTRAINTS:
- use clear visualizations to illustrate trends
- explain methodology and assumptions clearly
- provide actionable business recommendations
- include confidence levels for predictions

Combining with critical constraints

Use standard constraints to complement critical requirements:

builder = (
    tb.PromptBuilder()
    .persona("security engineer", "application security")
    .critical_constraint("Identify blocking security vulnerabilities immediately")
    .task_context("security audit of web application")
    .constraint("consider OWASP Top 10 guidelines")                    # Standard
    .constraint("evaluate both code and infrastructure security")      # Standard
    .constraint("provide remediation priority levels")                 # Standard
    .constraint("include compliance implications where relevant")      # Standard
    .core_analysis([
        "authentication and authorization",
        "input validation and sanitization",
        "data protection and encryption"
    ])
)

print(builder)

You are a security engineer with expertise in application security.

CRITICAL REQUIREMENTS:
- Identify blocking security vulnerabilities immediately

TASK: security audit of web application

CORE ANALYSIS (Required):
- authentication and authorization
- input validation and sanitization
- data protection and encryption

ADDITIONAL CONSTRAINTS:
- consider OWASP Top 10 guidelines
- evaluate both code and infrastructure security
- provide remediation priority levels
- include compliance implications where relevant

Output enhancement constraints

We can improve the structure and usability of responses by adding quality-focused constraints:

builder = (
    tb.PromptBuilder()
    .persona("technical documentation specialist")
    .task_context("create troubleshooting guide for deployment issues")
    .constraint("organize information from most common to least common issues")
    .constraint("include step-by-step resolution procedures")
    .constraint("provide prevention strategies for each issue type")
    .constraint("use consistent formatting and terminology throughout")
    .output_format([
        "issue description and symptoms",
        "root cause analysis",
        "step-by-step resolution",
        "prevention recommendations"
    ])
)

print(builder)

You are a technical documentation specialist.

CRITICAL REQUIREMENTS:
- organize information from most common to least common issues

TASK: create troubleshooting guide for deployment issues

ADDITIONAL CONSTRAINTS:
- include step-by-step resolution procedures
- provide prevention strategies for each issue type
- use consistent formatting and terminology throughout

OUTPUT FORMAT:
- issue description and symptoms
- root cause analysis
- step-by-step resolution
- prevention recommendations