# Enterprise AI Documentation Strategy
documentation_pipeline:
  content_generation:
    - automated_api_docs: "OpenAPI to comprehensive guides"
    - code_to_documentation: "Source code analysis and explanation"
    - changelog_automation: "Git commits to release notes"
    - architectural_diagrams: "Code structure to visual documentation"

  content_optimization:
    - clarity_enhancement: "Sentence structure analysis and improvement"
    - terminology_consistency: "Automated glossary enforcement"
    - accessibility_compliance: "WCAG 2.1 AA standard adherence"
    - multilingual_translation: "Context-aware localization"

  quality_assurance:
    - accuracy_validation: "Cross-reference checking with source code"
    - completeness_scoring: "Coverage gap identification"
    - user_experience_optimization: "Reading level and flow analysis"
    - maintenance_automation: "Automated updates from code changes"

  intelligent_delivery:
    - personalized_content: "Role-based documentation presentation"
    - contextual_help: "Inline assistance based on user actions"
    - search_enhancement: "Semantic search and intent understanding"
    - performance_analytics: "Usage patterns and improvement recommendations"

// AI-powered documentation generation system
package docgen

import (
    "context"
    "encoding/json"
    "fmt"
    "go/ast"
    "go/parser"
    "go/token"
    "path/filepath"
    "strings"
    "time"

    "github.com/openai/openai-go"
)

// AIDocumentationGenerator provides enterprise-grade doc generation
type AIDocumentationGenerator struct {
    client          *openai.Client
    codeAnalyzer    *CodeAnalyzer
    templateEngine  *TemplateEngine
    config          GenerationConfig
}

type GenerationConfig struct {
    OutputFormat     string   // "markdown", "html", "confluence"
    IncludeExamples  bool
    GenerateTests    bool
    IncludeDiagrams  bool
    TargetAudience   string   // "developer", "api-user", "business"
    ComplianceLevel  string   // "basic", "enterprise", "regulated"
    Languages        []string // Target languages for translation
}

type DocumentationArtifact struct {
    Title           string                 `json:"title"`
    Summary         string                 `json:"summary"`
    Content         string                 `json:"content"`
    CodeExamples    []CodeExample          `json:"code_examples"`
    APIEndpoints    []APIEndpoint          `json:"api_endpoints"`
    Architecture    ArchitecturalDiagram   `json:"architecture"`
    TestScenarios   []TestScenario         `json:"test_scenarios"`
    Metadata        DocumentationMetadata  `json:"metadata"`
}

type CodeExample struct {
    Language    string `json:"language"`
    Code        string `json:"code"`
    Explanation string `json:"explanation"`
    Output      string `json:"output,omitempty"`
}

type APIEndpoint struct {
    Method      string            `json:"method"`
    Path        string            `json:"path"`
    Summary     string            `json:"summary"`
    Description string            `json:"description"`
    Parameters  []Parameter       `json:"parameters"`
    Responses   map[string]Response `json:"responses"`
    Examples    []RequestExample  `json:"examples"`
}

// NewAIDocumentationGenerator creates a new AI documentation generator
func NewAIDocumentationGenerator(apiKey string, config GenerationConfig) *AIDocumentationGenerator {
    client := openai.NewClient(apiKey)

    return &AIDocumentationGenerator{
        client:         client,
        codeAnalyzer:   NewCodeAnalyzer(),
        templateEngine: NewTemplateEngine(),
        config:         config,
    }
}

// GenerateFromCodebase generates comprehensive documentation from codebase analysis
func (gen *AIDocumentationGenerator) GenerateFromCodebase(ctx context.Context, codebasePath string) (*DocumentationArtifact, error) {
    // Analyze codebase structure
    analysis, err := gen.codeAnalyzer.AnalyzeCodebase(codebasePath)
    if err != nil {
        return nil, fmt.Errorf("codebase analysis failed: %w", err)
    }

    // Generate base documentation prompt
    prompt := gen.buildDocumentationPrompt(analysis)

    // Call AI service for content generation
    response, err := gen.client.CreateChatCompletion(ctx, openai.ChatCompletionRequest{
        Model: "gpt-4",
        Messages: []openai.ChatCompletionMessage{
            {
                Role:    "system",
                Content: gen.getSystemPrompt(),
            },
            {
                Role:    "user",
                Content: prompt,
            },
        },
        MaxTokens:   4000,
        Temperature: 0.3, // Lower temperature for more consistent technical content
    })

    if err != nil {
        return nil, fmt.Errorf("AI generation failed: %w", err)
    }

    // Parse AI response into structured documentation
    artifact, err := gen.parseAIResponse(response.Choices[0].Message.Content)
    if err != nil {
        return nil, fmt.Errorf("response parsing failed: %w", err)
    }

    // Enhance with code examples
    if gen.config.IncludeExamples {
        if err := gen.generateCodeExamples(ctx, artifact, analysis); err != nil {
            return nil, fmt.Errorf("code example generation failed: %w", err)
        }
    }

    // Generate architectural diagrams
    if gen.config.IncludeDiagrams {
        if err := gen.generateArchitecturalDiagrams(ctx, artifact, analysis); err != nil {
            return nil, fmt.Errorf("diagram generation failed: %w", err)
        }
    }

    // Generate test scenarios
    if gen.config.GenerateTests {
        if err := gen.generateTestScenarios(ctx, artifact, analysis); err != nil {
            return nil, fmt.Errorf("test scenario generation failed: %w", err)
        }
    }

    // Apply enterprise compliance enhancements
    if err := gen.applyComplianceEnhancements(artifact); err != nil {
        return nil, fmt.Errorf("compliance enhancement failed: %w", err)
    }

    return artifact, nil
}

func (gen *AIDocumentationGenerator) getSystemPrompt() string {
    return `You are an expert technical writer and software architect specializing in enterprise documentation.

Your task is to generate comprehensive, accurate, and user-friendly technical documentation based on code analysis.

Guidelines:
1. Write clear, concise explanations suitable for the target audience
2. Include practical examples and real-world use cases
3. Maintain consistency in terminology and formatting
4. Focus on the "why" behind design decisions, not just the "what"
5. Include error handling and edge cases
6. Provide troubleshooting guidance
7. Consider security implications and best practices
8. Structure content for easy navigation and reference

Output format: Structured JSON with clear sections for different types of content.`
}

func (gen *AIDocumentationGenerator) buildDocumentationPrompt(analysis *CodebaseAnalysis) string {
    var prompt strings.Builder

    prompt.WriteString("Generate comprehensive technical documentation for the following codebase:\n\n")

    // Codebase overview
    prompt.WriteString(fmt.Sprintf("Project: %s\n", analysis.ProjectName))
    prompt.WriteString(fmt.Sprintf("Language: %s\n", analysis.PrimaryLanguage))
    prompt.WriteString(fmt.Sprintf("Architecture: %s\n", analysis.Architecture))
    prompt.WriteString(fmt.Sprintf("Target Audience: %s\n", gen.config.TargetAudience))
    prompt.WriteString(fmt.Sprintf("Compliance Level: %s\n\n", gen.config.ComplianceLevel))

    // Key components
    prompt.WriteString("Key Components:\n")
    for _, component := range analysis.Components {
        prompt.WriteString(fmt.Sprintf("- %s: %s\n", component.Name, component.Description))
    }

    // API endpoints
    if len(analysis.APIEndpoints) > 0 {
        prompt.WriteString("\nAPI Endpoints:\n")
        for _, endpoint := range analysis.APIEndpoints {
            prompt.WriteString(fmt.Sprintf("- %s %s: %s\n",
                endpoint.Method, endpoint.Path, endpoint.Description))
        }
    }

    // Dependencies
    prompt.WriteString("\nKey Dependencies:\n")
    for _, dep := range analysis.Dependencies {
        prompt.WriteString(fmt.Sprintf("- %s: %s\n", dep.Name, dep.Purpose))
    }

    // Configuration
    if len(analysis.ConfigurationOptions) > 0 {
        prompt.WriteString("\nConfiguration Options:\n")
        for _, config := range analysis.ConfigurationOptions {
            prompt.WriteString(fmt.Sprintf("- %s: %s (default: %s)\n",
                config.Name, config.Description, config.DefaultValue))
        }
    }

    prompt.WriteString("\nPlease generate:\n")
    prompt.WriteString("1. Executive summary and overview\n")
    prompt.WriteString("2. Getting started guide\n")
    prompt.WriteString("3. API reference with examples\n")
    prompt.WriteString("4. Configuration guide\n")
    prompt.WriteString("5. Best practices and troubleshooting\n")
    prompt.WriteString("6. Architecture explanation\n")

    return prompt.String()
}

// generateCodeExamples creates contextual code examples
func (gen *AIDocumentationGenerator) generateCodeExamples(ctx context.Context, artifact *DocumentationArtifact, analysis *CodebaseAnalysis) error {
    for i, endpoint := range artifact.APIEndpoints {
        // Generate examples for each endpoint
        examples, err := gen.generateEndpointExamples(ctx, endpoint, analysis)
        if err != nil {
            return err
        }
        artifact.APIEndpoints[i].Examples = examples
    }

    return nil
}

func (gen *AIDocumentationGenerator) generateEndpointExamples(ctx context.Context, endpoint APIEndpoint, analysis *CodebaseAnalysis) ([]RequestExample, error) {
    prompt := fmt.Sprintf(`Generate practical code examples for the API endpoint:
%s %s

Description: %s

Please provide examples in the following languages: %s

Include:
1. Basic usage example
2. Example with error handling
3. Example with authentication
4. Integration test example

For each example, provide:
- Complete, runnable code
- Expected response
- Error scenarios
- Explanation of key points`,
        endpoint.Method, endpoint.Path, endpoint.Description,
        strings.Join([]string{"curl", "Go", "Python", "JavaScript"}, ", "))

    response, err := gen.client.CreateChatCompletion(ctx, openai.ChatCompletionRequest{
        Model: "gpt-4",
        Messages: []openai.ChatCompletionMessage{
            {
                Role:    "user",
                Content: prompt,
            },
        },
        MaxTokens:   2000,
        Temperature: 0.2,
    })

    if err != nil {
        return nil, err
    }

    // Parse response and convert to RequestExample structs
    examples, err := gen.parseExampleResponse(response.Choices[0].Message.Content)
    if err != nil {
        return nil, err
    }

    return examples, nil
}

// Intelligent knowledge management with AI-powered search and retrieval
package knowledge

import (
    "context"
    "encoding/json"
    "fmt"
    "time"

    "github.com/weaviate/weaviate-go-client/v4/weaviate"
    "github.com/weaviate/weaviate/entities/models"
)

// KnowledgeManager provides AI-powered knowledge management
type KnowledgeManager struct {
    vectorDB        *weaviate.Client
    embeddingModel  string
    searchConfig    SearchConfig
    indexConfig     IndexConfig
}

type SearchConfig struct {
    MaxResults      int
    Certainty       float32
    IncludeVector   bool
    EnableHybrid    bool
    BoostFactors    map[string]float32
}

type IndexConfig struct {
    VectorDimensions int
    DocumentTypes    []string
    MetadataFields   []string
    UpdateFrequency  time.Duration
}

type Document struct {
    ID           string                 `json:"id"`
    Title        string                 `json:"title"`
    Content      string                 `json:"content"`
    DocumentType string                 `json:"document_type"`
    Metadata     map[string]interface{} `json:"metadata"`
    LastUpdated  time.Time              `json:"last_updated"`
    Version      string                 `json:"version"`
    Tags         []string               `json:"tags"`
    Author       string                 `json:"author"`
}

type SearchResult struct {
    Document    Document `json:"document"`
    Score       float32  `json:"score"`
    Explanation string   `json:"explanation"`
    Highlights  []string `json:"highlights"`
}

type QueryContext struct {
    UserRole     string                 `json:"user_role"`
    Project      string                 `json:"project"`
    Language     string                 `json:"language"`
    Preferences  map[string]interface{} `json:"preferences"`
}

// NewKnowledgeManager creates a new AI-powered knowledge manager
func NewKnowledgeManager(weaviateHost string, config SearchConfig) (*KnowledgeManager, error) {
    cfg := weaviate.Config{
        Host:   weaviateHost,
        Scheme: "http",
    }

    client, err := weaviate.NewClient(cfg)
    if err != nil {
        return nil, err
    }

    km := &KnowledgeManager{
        vectorDB:       client,
        embeddingModel: "text-embedding-ada-002",
        searchConfig:   config,
    }

    // Initialize schema
    if err := km.initializeSchema(); err != nil {
        return nil, err
    }

    return km, nil
}

// IndexDocument adds a document to the knowledge base
func (km *KnowledgeManager) IndexDocument(ctx context.Context, doc Document) error {
    // Prepare document for vectorization
    properties := map[string]interface{}{
        "title":         doc.Title,
        "content":       doc.Content,
        "documentType":  doc.DocumentType,
        "lastUpdated":   doc.LastUpdated.Format(time.RFC3339),
        "version":       doc.Version,
        "tags":          doc.Tags,
        "author":        doc.Author,
    }

    // Add metadata fields
    for key, value := range doc.Metadata {
        properties[key] = value
    }

    // Create object in vector database
    object := &models.Object{
        Class:      "Document",
        ID:         strfmt.UUID(doc.ID),
        Properties: properties,
    }

    _, err := km.vectorDB.Data().Creator().
        WithClassName("Document").
        WithObject(object).
        Do(ctx)

    return err
}

// SemanticSearch performs AI-powered semantic search
func (km *KnowledgeManager) SemanticSearch(ctx context.Context, query string, queryCtx QueryContext) ([]SearchResult, error) {
    // Build search query with context
    enhancedQuery := km.enhanceQueryWithContext(query, queryCtx)

    // Perform hybrid search (vector + keyword)
    result, err := km.vectorDB.GraphQL().Get().
        WithClassName("Document").
        WithFields("title content documentType lastUpdated version tags author _additional { certainty }").
        WithNearText(km.vectorDB.GraphQL().NearTextArgBuilder().
            WithConcepts([]string{enhancedQuery}).
            WithCertainty(km.searchConfig.Certainty)).
        WithLimit(km.searchConfig.MaxResults).
        Do(ctx)

    if err != nil {
        return nil, err
    }

    // Parse and enrich results
    searchResults, err := km.parseSearchResults(result, query)
    if err != nil {
        return nil, err
    }

    // Apply personalization based on query context
    personalizedResults := km.personalizeResults(searchResults, queryCtx)

    return personalizedResults, nil
}

// IntelligentSuggestion provides contextual content suggestions
func (km *KnowledgeManager) IntelligentSuggestion(ctx context.Context, userContext UserContext) ([]ContentSuggestion, error) {
    // Analyze user behavior and preferences
    userProfile := km.buildUserProfile(userContext)

    // Generate suggestions based on:
    // 1. Recently accessed content
    // 2. Role-based recommendations
    // 3. Project-specific content
    // 4. Trending documentation

    suggestions := []ContentSuggestion{}

    // Role-based suggestions
    roleQuery := fmt.Sprintf("content relevant for %s", userContext.Role)
    roleResults, err := km.SemanticSearch(ctx, roleQuery, QueryContext{
        UserRole: userContext.Role,
        Project:  userContext.Project,
    })
    if err == nil {
        for _, result := range roleResults[:min(3, len(roleResults))] {
            suggestions = append(suggestions, ContentSuggestion{
                Document:    result.Document,
                Reason:      "Relevant for your role",
                Confidence:  result.Score,
                Category:    "role-based",
            })
        }
    }

    // Project-specific suggestions
    if userContext.Project != "" {
        projectQuery := fmt.Sprintf("documentation for %s project", userContext.Project)
        projectResults, err := km.SemanticSearch(ctx, projectQuery, QueryContext{
            Project: userContext.Project,
        })
        if err == nil {
            for _, result := range projectResults[:min(2, len(projectResults))] {
                suggestions = append(suggestions, ContentSuggestion{
                    Document:   result.Document,
                    Reason:     "Related to your current project",
                    Confidence: result.Score,
                    Category:   "project-based",
                })
            }
        }
    }

    return suggestions, nil
}

func (km *KnowledgeManager) enhanceQueryWithContext(query string, ctx QueryContext) string {
    enhanced := query

    // Add role context
    if ctx.UserRole != "" {
        enhanced += fmt.Sprintf(" for %s", ctx.UserRole)
    }

    // Add project context
    if ctx.Project != "" {
        enhanced += fmt.Sprintf(" in %s project", ctx.Project)
    }

    // Add language preference
    if ctx.Language != "" && ctx.Language != "en" {
        enhanced += fmt.Sprintf(" in %s", ctx.Language)
    }

    return enhanced
}

func (km *KnowledgeManager) personalizeResults(results []SearchResult, ctx QueryContext) []SearchResult {
    personalized := make([]SearchResult, len(results))
    copy(personalized, results)

    // Apply role-based scoring boost
    roleBoost := km.searchConfig.BoostFactors["role"]
    projectBoost := km.searchConfig.BoostFactors["project"]

    for i, result := range personalized {
        boost := 1.0

        // Boost results matching user role
        if strings.Contains(result.Document.Content, ctx.UserRole) {
            boost *= float64(roleBoost)
        }

        // Boost results matching user project
        if result.Document.Metadata["project"] == ctx.Project {
            boost *= float64(projectBoost)
        }

        personalized[i].Score = float32(float64(result.Score) * boost)
    }

    // Re-sort by adjusted scores
    sort.Slice(personalized, func(i, j int) bool {
        return personalized[i].Score > personalized[j].Score
    })

    return personalized
}

// AI-powered content quality assurance
package quality

import (
    "context"
    "fmt"
    "regexp"
    "strings"
    "time"

    "github.com/openai/openai-go"
)

// QualityAssuranceEngine provides comprehensive content QA
type QualityAssuranceEngine struct {
    aiClient        *openai.Client
    rules           []QualityRule
    validators      []ContentValidator
    metrics         QualityMetrics
}

type QualityRule struct {
    Name        string
    Description string
    Severity    Severity
    Pattern     *regexp.Regexp
    Validator   func(content string) []QualityIssue
}

type QualityIssue struct {
    Rule        string    `json:"rule"`
    Severity    Severity  `json:"severity"`
    Message     string    `json:"message"`
    Line        int       `json:"line"`
    Column      int       `json:"column"`
    Suggestion  string    `json:"suggestion"`
    Context     string    `json:"context"`
}

type QualityReport struct {
    DocumentID      string         `json:"document_id"`
    OverallScore    float64        `json:"overall_score"`
    Issues          []QualityIssue `json:"issues"`
    Suggestions     []string       `json:"suggestions"`
    Metrics         QualityMetrics `json:"metrics"`
    GeneratedAt     time.Time      `json:"generated_at"`
    AIRecommendations []AIRecommendation `json:"ai_recommendations"`
}

type QualityMetrics struct {
    ReadabilityScore    float64 `json:"readability_score"`
    CompletenessScore   float64 `json:"completeness_score"`
    AccuracyScore       float64 `json:"accuracy_score"`
    ConsistencyScore    float64 `json:"consistency_score"`
    AccessibilityScore  float64 `json:"accessibility_score"`
    WordCount          int     `json:"word_count"`
    SentenceCount      int     `json:"sentence_count"`
    ParagraphCount     int     `json:"paragraph_count"`
}

type AIRecommendation struct {
    Type        string `json:"type"`
    Priority    string `json:"priority"`
    Description string `json:"description"`
    Example     string `json:"example"`
    Impact      string `json:"impact"`
}

// NewQualityAssuranceEngine creates a new QA engine
func NewQualityAssuranceEngine(apiKey string) *QualityAssuranceEngine {
    client := openai.NewClient(apiKey)

    engine := &QualityAssuranceEngine{
        aiClient: client,
        rules:    []QualityRule{},
    }

    // Initialize default quality rules
    engine.initializeDefaultRules()

    return engine
}

func (qa *QualityAssuranceEngine) initializeDefaultRules() {
    // Readability rules
    qa.rules = append(qa.rules, QualityRule{
        Name:        "sentence_length",
        Description: "Sentences should be concise and readable",
        Severity:    SeverityWarning,
        Validator: func(content string) []QualityIssue {
            return qa.checkSentenceLength(content)
        },
    })

    // Technical writing rules
    qa.rules = append(qa.rules, QualityRule{
        Name:        "passive_voice",
        Description: "Use active voice for clearer instructions",
        Severity:    SeverityInfo,
        Pattern:     regexp.MustCompile(`\b(is|are|was|were|be|been|being)\s+\w+ed\b`),
    })

    // Code documentation rules
    qa.rules = append(qa.rules, QualityRule{
        Name:        "code_examples",
        Description: "Code blocks should have proper syntax highlighting",
        Severity:    SeverityError,
        Validator: func(content string) []QualityIssue {
            return qa.checkCodeExamples(content)
        },
    })

    // Link validation
    qa.rules = append(qa.rules, QualityRule{
        Name:        "broken_links",
        Description: "All links should be valid and accessible",
        Severity:    SeverityError,
        Validator: func(content string) []QualityIssue {
            return qa.validateLinks(content)
        },
    })
}

// AnalyzeContent performs comprehensive content analysis
func (qa *QualityAssuranceEngine) AnalyzeContent(ctx context.Context, content string, documentID string) (*QualityReport, error) {
    report := &QualityReport{
        DocumentID:  documentID,
        GeneratedAt: time.Now(),
        Issues:      []QualityIssue{},
    }

    // Run rule-based validations
    for _, rule := range qa.rules {
        issues := qa.applyRule(rule, content)
        report.Issues = append(report.Issues, issues...)
    }

    // Calculate quality metrics
    report.Metrics = qa.calculateMetrics(content)

    // Generate AI-powered recommendations
    recommendations, err := qa.generateAIRecommendations(ctx, content, report.Issues)
    if err != nil {
        return nil, fmt.Errorf("AI recommendations failed: %w", err)
    }
    report.AIRecommendations = recommendations

    // Calculate overall score
    report.OverallScore = qa.calculateOverallScore(report.Metrics, report.Issues)

    // Generate improvement suggestions
    report.Suggestions = qa.generateSuggestions(report.Issues, report.Metrics)

    return report, nil
}

func (qa *QualityAssuranceEngine) generateAIRecommendations(ctx context.Context, content string, issues []QualityIssue) ([]AIRecommendation, error) {
    prompt := qa.buildQualityPrompt(content, issues)

    response, err := qa.aiClient.CreateChatCompletion(ctx, openai.ChatCompletionRequest{
        Model: "gpt-4",
        Messages: []openai.ChatCompletionMessage{
            {
                Role:    "system",
                Content: qa.getQualitySystemPrompt(),
            },
            {
                Role:    "user",
                Content: prompt,
            },
        },
        MaxTokens:   1500,
        Temperature: 0.3,
    })

    if err != nil {
        return nil, err
    }

    // Parse AI response into recommendations
    recommendations, err := qa.parseAIRecommendations(response.Choices[0].Message.Content)
    if err != nil {
        return nil, err
    }

    return recommendations, nil
}

func (qa *QualityAssuranceEngine) getQualitySystemPrompt() string {
    return `You are an expert technical writing consultant specializing in enterprise documentation quality.

Your task is to analyze technical content and provide specific, actionable recommendations for improvement.

Focus on:
1. Clarity and readability for the target audience
2. Technical accuracy and completeness
3. Consistency in terminology and style
4. Accessibility and inclusivity
5. Information architecture and organization
6. Code example quality and correctness

Provide recommendations as structured JSON with:
- type: The category of improvement (clarity, structure, technical, accessibility)
- priority: high, medium, or low
- description: Clear explanation of the issue and solution
- example: Specific example of the improvement
- impact: Expected benefit of implementing the change`
}

func (qa *QualityAssuranceEngine) buildQualityPrompt(content string, issues []QualityIssue) string {
    var prompt strings.Builder

    prompt.WriteString("Analyze the following technical documentation for quality improvements:\n\n")
    prompt.WriteString("CONTENT:\n")
    prompt.WriteString(content)
    prompt.WriteString("\n\n")

    if len(issues) > 0 {
        prompt.WriteString("IDENTIFIED ISSUES:\n")
        for _, issue := range issues {
            prompt.WriteString(fmt.Sprintf("- %s (%s): %s\n",
                issue.Rule, issue.Severity, issue.Message))
        }
        prompt.WriteString("\n")
    }

    prompt.WriteString("Please provide specific recommendations to improve:\n")
    prompt.WriteString("1. Content clarity and readability\n")
    prompt.WriteString("2. Technical accuracy and completeness\n")
    prompt.WriteString("3. Structure and organization\n")
    prompt.WriteString("4. Code examples and technical details\n")
    prompt.WriteString("5. Accessibility and inclusivity\n")

    return prompt.String()
}

// Auto-improvement system
func (qa *QualityAssuranceEngine) AutoImproveContent(ctx context.Context, content string) (string, error) {
    improvePrompt := fmt.Sprintf(`Improve the following technical documentation while maintaining its technical accuracy:

ORIGINAL CONTENT:
%s

Please improve:
1. Clarity and readability
2. Structure and organization
3. Consistency in terminology
4. Code example formatting
5. Accessibility considerations

Return only the improved content without explanations.`, content)

    response, err := qa.aiClient.CreateChatCompletion(ctx, openai.ChatCompletionRequest{
        Model: "gpt-4",
        Messages: []openai.ChatCompletionMessage{
            {
                Role:    "user",
                Content: improvePrompt,
            },
        },
        MaxTokens:   2000,
        Temperature: 0.2,
    })

    if err != nil {
        return "", err
    }

    return response.Choices[0].Message.Content, nil
}

// AI Act compliance documentation automation
package compliance

import (
    "context"
    "encoding/json"
    "fmt"
    "time"
)

// ComplianceManager handles AI Act and regulatory compliance
type ComplianceManager struct {
    documentGenerator *AIDocumentationGenerator
    riskAssessment    *RiskAssessmentEngine
    auditTrail        *AuditTrailManager
    complianceRules   []ComplianceRule
}

type ComplianceRule struct {
    Regulation      string
    Requirement     string
    DocumentType    string
    MandatoryFields []string
    ValidationRules []ValidationRule
}

type AISystemDocumentation struct {
    SystemID            string                 `json:"system_id"`
    SystemName          string                 `json:"system_name"`
    RiskCategory        string                 `json:"risk_category"`
    IntendedPurpose     string                 `json:"intended_purpose"`
    TechnicalSpecs      TechnicalSpecification `json:"technical_specs"`
    DataGovernance      DataGovernanceInfo     `json:"data_governance"`
    RiskAssessment      RiskAssessmentReport   `json:"risk_assessment"`
    TestingValidation   TestingReport          `json:"testing_validation"`
    HumanOversight      HumanOversightPlan     `json:"human_oversight"`
    MonitoringPlan      MonitoringPlan         `json:"monitoring_plan"`
    ComplianceMetadata  ComplianceMetadata     `json:"compliance_metadata"`
}

type TechnicalSpecification struct {
    Architecture        string   `json:"architecture"`
    ModelType          string   `json:"model_type"`
    TrainingData       DataInfo `json:"training_data"`
    PerformanceMetrics []Metric `json:"performance_metrics"`
    Limitations        []string `json:"limitations"`
    Dependencies       []string `json:"dependencies"`
}

// GenerateAIActCompliantDocs generates AI Act compliant documentation
func (cm *ComplianceManager) GenerateAIActCompliantDocs(ctx context.Context, system AISystemInfo) (*AISystemDocumentation, error) {
    docs := &AISystemDocumentation{
        SystemID:   system.ID,
        SystemName: system.Name,
    }

    // Determine risk category
    riskCategory, err := cm.riskAssessment.AssessRiskCategory(ctx, system)
    if err != nil {
        return nil, fmt.Errorf("risk assessment failed: %w", err)
    }
    docs.RiskCategory = riskCategory

    // Generate required documentation based on risk category
    switch riskCategory {
    case "high-risk":
        if err := cm.generateHighRiskDocumentation(ctx, docs, system); err != nil {
            return nil, err
        }
    case "limited-risk":
        if err := cm.generateLimitedRiskDocumentation(ctx, docs, system); err != nil {
            return nil, err
        }
    case "minimal-risk":
        if err := cm.generateMinimalRiskDocumentation(ctx, docs, system); err != nil {
            return nil, err
        }
    }

    // Validate compliance
    if err := cm.validateCompliance(docs); err != nil {
        return nil, fmt.Errorf("compliance validation failed: %w", err)
    }

    // Create audit trail entry
    cm.auditTrail.RecordComplianceActivity(AuditEntry{
        SystemID:    system.ID,
        Activity:    "documentation_generation",
        Regulation:  "EU AI Act",
        Status:      "completed",
        Timestamp:   time.Now(),
        Details:     map[string]interface{}{"risk_category": riskCategory},
    })

    return docs, nil
}

func (cm *ComplianceManager) generateHighRiskDocumentation(ctx context.Context, docs *AISystemDocumentation, system AISystemInfo) error {
    // Article 11: Technical documentation for high-risk AI systems

    // Generate detailed technical specifications
    techSpecs, err := cm.generateTechnicalSpecifications(ctx, system)
    if err != nil {
        return err
    }
    docs.TechnicalSpecs = techSpecs

    // Generate data governance documentation
    dataGov, err := cm.generateDataGovernanceDoc(ctx, system)
    if err != nil {
        return err
    }
    docs.DataGovernance = dataGov

    // Generate risk assessment report
    riskReport, err := cm.riskAssessment.GenerateDetailedReport(ctx, system)
    if err != nil {
        return err
    }
    docs.RiskAssessment = riskReport

    // Generate testing and validation documentation
    testingReport, err := cm.generateTestingValidationDoc(ctx, system)
    if err != nil {
        return err
    }
    docs.TestingValidation = testingReport

    // Generate human oversight plan
    oversightPlan, err := cm.generateHumanOversightPlan(ctx, system)
    if err != nil {
        return err
    }
    docs.HumanOversight = oversightPlan

    // Generate monitoring plan
    monitoringPlan, err := cm.generateMonitoringPlan(ctx, system)
    if err != nil {
        return err
    }
    docs.MonitoringPlan = monitoringPlan

    return nil
}

// Automated compliance monitoring
func (cm *ComplianceManager) ContinuousComplianceMonitoring(ctx context.Context, systemID string) error {
    // Monitor for regulation changes
    go cm.monitorRegulationUpdates(ctx, systemID)

    // Schedule regular compliance assessments
    go cm.scheduleComplianceReviews(ctx, systemID)

    // Monitor system changes that affect compliance
    go cm.monitorSystemChanges(ctx, systemID)

    return nil
}

func (cm *ComplianceManager) generateComplianceReport(ctx context.Context, systemID string, period TimePeriod) (*ComplianceReport, error) {
    prompt := fmt.Sprintf(`Generate a comprehensive compliance report for AI system %s covering the period %s to %s.

Include:
1. Regulatory compliance status
2. Risk assessment updates
3. Incident reports
4. Monitoring metrics
5. Recommendations for improvement
6. Action items for maintaining compliance

Focus on:
- EU AI Act requirements
- GDPR data protection
- Industry-specific regulations
- Internal compliance policies`,
        systemID, period.Start.Format("2006-01-02"), period.End.Format("2006-01-02"))

    response, err := cm.documentGenerator.client.CreateChatCompletion(ctx, openai.ChatCompletionRequest{
        Model: "gpt-4",
        Messages: []openai.ChatCompletionMessage{
            {
                Role:    "system",
                Content: "You are a compliance expert specializing in AI regulation and documentation.",
            },
            {
                Role:    "user",
                Content: prompt,
            },
        },
        MaxTokens:   3000,
        Temperature: 0.1,
    })

    if err != nil {
        return nil, err
    }

    // Parse and structure the compliance report
    report, err := cm.parseComplianceReport(response.Choices[0].Message.Content)
    if err != nil {
        return nil, err
    }

    return report, nil
}

// Documentation performance analytics and optimization
package analytics

import (
    "context"
    "time"
)

// AnalyticsEngine provides comprehensive documentation analytics
type AnalyticsEngine struct {
    metricsCollector  *MetricsCollector
    userBehavior      *UserBehaviorAnalyzer
    contentAnalyzer   *ContentAnalyzer
    aiOptimizer       *AIOptimizer
}

type DocumentationMetrics struct {
    ViewCount          int64             `json:"view_count"`
    UniqueVisitors     int64             `json:"unique_visitors"`
    AverageTimeOnPage  time.Duration     `json:"average_time_on_page"`
    BounceRate         float64           `json:"bounce_rate"`
    SearchQueries      []SearchQuery     `json:"search_queries"`
    UserFeedback       []FeedbackEntry   `json:"user_feedback"`
    ConversionRate     float64           `json:"conversion_rate"`
    TaskCompletion     TaskCompletionMetrics `json:"task_completion"`
    UserJourney        []UserJourneyStep `json:"user_journey"`
}

type AIOptimizationSuggestion struct {
    Type           string  `json:"type"`
    Priority       string  `json:"priority"`
    Description    string  `json:"description"`
    ExpectedImpact string  `json:"expected_impact"`
    Implementation string  `json:"implementation"`
    Confidence     float64 `json:"confidence"`
}

// GenerateOptimizationSuggestions uses AI to suggest content improvements
func (ae *AnalyticsEngine) GenerateOptimizationSuggestions(ctx context.Context, documentID string) ([]AIOptimizationSuggestion, error) {
    // Collect comprehensive analytics data
    metrics := ae.metricsCollector.GetDocumentMetrics(documentID)
    userBehavior := ae.userBehavior.AnalyzeUserBehavior(documentID)
    contentAnalysis := ae.contentAnalyzer.AnalyzeContent(documentID)

    // Generate AI-powered optimization suggestions
    suggestions, err := ae.aiOptimizer.GenerateSuggestions(ctx, OptimizationContext{
        Metrics:         metrics,
        UserBehavior:    userBehavior,
        ContentAnalysis: contentAnalysis,
    })

    if err != nil {
        return nil, err
    }

    return suggestions, nil
}

// AutoOptimizeContent automatically improves content based on analytics
func (ae *AnalyticsEngine) AutoOptimizeContent(ctx context.Context, documentID string) (*OptimizationResult, error) {
    suggestions, err := ae.GenerateOptimizationSuggestions(ctx, documentID)
    if err != nil {
        return nil, err
    }

    // Apply high-confidence suggestions automatically
    result := &OptimizationResult{
        DocumentID:    documentID,
        Optimizations: []AppliedOptimization{},
        Timestamp:     time.Now(),
    }

    for _, suggestion := range suggestions {
        if suggestion.Confidence > 0.8 && suggestion.Priority == "high" {
            optimization, err := ae.applyOptimization(ctx, documentID, suggestion)
            if err != nil {
                continue // Log error but continue with other optimizations
            }
            result.Optimizations = append(result.Optimizations, optimization)
        }
    }

    return result, nil
}