specs/005-content-personalize/spec.md

Feature Specification: Content Personalization API

Feature Branch: 005-content-personalize Created: 2025-12-14 Status: Draft Input: User description: "Add content personalization API with POST /api/personalize that adjusts content complexity based on user background (software_level, hardware_level, learning_goals)"

User Scenarios & Testing (mandatory)

User Story 1 - Personalize Content for Beginner Software User (Priority: P1)

A user with beginner software experience submits educational content for personalization. The system retrieves their profile and adapts the content by adding explanations, using simpler terminology, and breaking down complex concepts.

Why this priority: Core functionality - personalization for beginners is the primary use case for educational content adaptation.

Independent Test: Can be fully tested by creating a user with software_level="beginner", submitting technical content, and verifying the response includes additional explanations and simplified language.

Acceptance Scenarios:

1. Given a valid user with software_level="beginner", When technical content about programming is submitted, Then the system returns personalized content with added explanations and simpler terminology along with a description of adjustments made.

2. Given a valid user with software_level="beginner" and hardware_level="none", When content mentions hardware concepts, Then the system explains hardware terminology in accessible terms.

3. Given a request with valid content and user_id, When the personalize endpoint is called, Then the system returns a JSON response with personalized_content and adjustments_made fields.

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User Story 2 - Personalize Content for Advanced User (Priority: P1)

A user with advanced software experience submits educational content for personalization. The system recognizes their expertise level and enhances the content with technical depth, omitting basic explanations they already understand.

Why this priority: Core functionality - advanced users need streamlined content that respects their knowledge level.

Independent Test: Can be fully tested by creating a user with software_level="advanced", submitting content with basic explanations, and verifying the response has technical depth without redundant basics.

Acceptance Scenarios:

1. Given a valid user with software_level="advanced", When educational content is submitted, Then the system returns personalized content with added technical depth and without basic explanations.

2. Given a valid user with software_level="advanced" and hardware_level="experienced", When content is submitted, Then the system uses technical hardware terminology without additional explanation.

3. Given a user with specific learning_goals, When content is personalized, Then the adjustments_made field explains how the content was tailored to support those goals.

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User Story 3 - Personalize Content for Intermediate User with Hardware Focus (Priority: P2)

A user with intermediate software skills but basic hardware knowledge requests personalized content. The system balances the complexity appropriately - maintaining moderate software complexity while explaining hardware concepts.

Why this priority: Important for users with mixed experience levels across different domains.

Independent Test: Can be fully tested by creating a user with software_level="intermediate" and hardware_level="basic", submitting mixed content, and verifying appropriate adaptation for each domain.

Acceptance Scenarios:

1. Given a user with software_level="intermediate" and hardware_level="basic", When content covering both software and hardware topics is submitted, Then software concepts are presented at intermediate level while hardware concepts receive additional explanation.

2. Given a user with learning_goals="robotics", When robotics content is submitted, Then the system emphasizes aspects relevant to the stated learning goals.

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Edge Cases

• What happens when the user_id does not exist in the database? System should return a 404 error with a clear message indicating the user was not found.
• What happens when the content field is empty or missing? System should return a 400 error indicating content is required.
• What happens when the AI service is unavailable? System should return a 503 error indicating temporary service unavailability.
• How does the system handle very long content? System should process content up to a reasonable limit (e.g., 50,000 characters) and return an appropriate error for content exceeding this limit.
• What happens when user profile has default values (beginner, none, empty goals)? System should personalize using the default beginner-friendly approach.

Requirements (mandatory)

Functional Requirements

• FR-001: System MUST expose a POST endpoint at /api/personalize that accepts JSON requests.
• FR-002: System MUST accept a request body containing content (string) and user_id (integer) fields.
• FR-003: System MUST return a JSON response containing personalized_content (string) and adjustments_made (string) fields.
• FR-004: System MUST fetch the user's profile from the database using the provided user_id.
• FR-005: System MUST use the user's software_level to determine content complexity adjustments.
• FR-006: System MUST use the user's hardware_level to determine hardware concept explanations.
• FR-007: System MUST consider the user's learning_goals when personalizing content.
• FR-008: System MUST use AI to generate personalized content based on user background.
• FR-009: System MUST validate that content is a non-empty string and user_id is a valid integer.
• FR-010: System MUST return appropriate HTTP error codes (400 for validation errors, 404 for user not found, 500 for server errors, 503 for AI service unavailability).

Personalization Logic

• PL-001: For software_level="beginner": Add detailed explanations, use simpler terminology, break down complex concepts, provide examples.
• PL-002: For software_level="intermediate": Maintain moderate complexity, brief explanations for advanced concepts only.
• PL-003: For software_level="advanced": Add technical depth, skip basic explanations, use precise technical terminology.
• PL-004: For hardware_level="none": Explain all hardware concepts from scratch, use analogies.
• PL-005: For hardware_level="basic": Brief hardware explanations, define technical terms.
• PL-006: For hardware_level="experienced": Use technical hardware terminology without explanation.
• PL-007: When learning_goals are specified: Emphasize and connect content to the user's stated objectives.

Key Entities

• User: Existing entity containing the background profile used for personalization.

  • id: Unique identifier for the user
  • software_level: One of "beginner", "intermediate", "advanced"
  • hardware_level: One of "none", "basic", "experienced"
  • learning_goals: Free-text description of what the user wants to learn

• PersonalizationRequest: Input data for the personalization operation.

  • content: The educational content to be personalized
  • user_id: Reference to the user whose profile determines personalization

• PersonalizationResponse: Output from the personalization operation.

  • personalized_content: The adapted content
  • adjustments_made: Description of what changes were applied

Success Criteria (mandatory)

Measurable Outcomes

• SC-001: Users receive personalized content within 15 seconds for typical content (under 10,000 characters).
• SC-002: Beginner users report 80% or higher comprehension of personalized technical content.
• SC-003: Advanced users receive content without redundant basic explanations 95% of the time.
• SC-004: The adjustments_made field accurately describes the personalization approach applied.
• SC-005: The system handles at least 20 concurrent personalization requests without errors.
• SC-006: Content personalization aligns with the user's learning_goals when specified.

Assumptions

• User authentication and authorization are handled separately; this endpoint assumes the caller has permission to access the specified user's profile.
• The user's background profile (software_level, hardware_level, learning_goals) is already populated in the database.
• AI service (OpenAI) is configured and accessible with valid credentials.
• Content submitted for personalization is educational/technical material appropriate for adaptation.
• The personalization does not permanently modify the original content; it returns a personalized copy.

Out of Scope

• User authentication or authorization for the personalization endpoint
• Caching of personalized content (each request generates fresh personalization)
• Tracking or analytics of personalization effectiveness
• Batch personalization of multiple content items in a single request
• User preference for personalization intensity or style
• A/B testing of different personalization approaches
• Content moderation or safety filtering