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/**
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* INTERMEDIATE TUTORIAL: Self-Learning System
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*
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* Build an adaptive AI system that improves its output quality over time
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* through feedback loops and pattern recognition. This demonstrates how
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* to create systems that learn from their mistakes and successes.
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*
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* What you'll learn:
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* - Building feedback loops
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* - Tracking quality improvements
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* - Adaptive prompt engineering
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* - Learning from examples
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*
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* Prerequisites:
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* - Set GEMINI_API_KEY environment variable
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* - npm install dspy.ts @ruvector/agentic-synth
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*
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* Run: npx tsx examples/intermediate/self-learning-system.ts
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*/
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import { LM, ChainOfThought, Prediction } from 'dspy.ts';
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// Learning session configuration
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interface LearningConfig {
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targetQualityThreshold: number; // Stop when this quality is reached
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maxIterations: number; // Maximum learning iterations
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improvementRate: number; // How aggressively to adjust (0.1 = 10% per iteration)
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minImprovement: number; // Minimum improvement to continue
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}
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// Feedback from each iteration
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interface Feedback {
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quality: number;
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strengths: string[];
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weaknesses: string[];
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suggestions: string[];
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}
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// Learning history entry
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interface LearningEntry {
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iteration: number;
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quality: number;
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output: Prediction;
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feedback: Feedback;
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promptModifications: string[];
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timestamp: Date;
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}
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// Self-learning generator class
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class SelfLearningGenerator {
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private lm: LM;
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private history: LearningEntry[] = [];
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private config: LearningConfig;
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private basePrompt: string;
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private currentPromptAdditions: string[] = [];
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constructor(config: Partial<LearningConfig> = {}) {
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this.config = {
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targetQualityThreshold: config.targetQualityThreshold || 0.9,
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maxIterations: config.maxIterations || 10,
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improvementRate: config.improvementRate || 0.15,
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minImprovement: config.minImprovement || 0.02
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};
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this.lm = new LM({
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provider: 'google-genai',
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model: 'gemini-2.0-flash-exp',
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apiKey: process.env.GEMINI_API_KEY || '',
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temperature: 0.8 // Higher temperature for creativity during learning
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});
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this.basePrompt = '';
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}
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// Evaluate the quality of generated output
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private evaluateOutput(prediction: Prediction, criteria: any): Feedback {
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let quality = 0;
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const strengths: string[] = [];
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const weaknesses: string[] = [];
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const suggestions: string[] = [];
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// Check description quality
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if (prediction.description) {
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const desc = prediction.description;
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const length = desc.length;
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if (length >= 100 && length <= 200) {
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quality += 0.3;
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strengths.push('Description length is optimal');
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} else if (length < 50) {
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weaknesses.push('Description too short');
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suggestions.push('Expand description with more details');
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} else if (length > 250) {
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weaknesses.push('Description too verbose');
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suggestions.push('Make description more concise');
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} else {
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quality += 0.15;
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}
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// Check for emotional/engaging language
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const emotionalWords = ['amazing', 'powerful', 'innovative', 'premium', 'exceptional'];
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const hasEmotionalLanguage = emotionalWords.some(word =>
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desc.toLowerCase().includes(word)
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);
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if (hasEmotionalLanguage) {
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quality += 0.2;
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strengths.push('Uses engaging language');
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} else {
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weaknesses.push('Could be more engaging');
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suggestions.push('Add more descriptive and emotional words');
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}
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} else {
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weaknesses.push('Missing description');
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suggestions.push('Generate a complete description');
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}
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// Check features
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if (prediction.key_features && Array.isArray(prediction.key_features)) {
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const features = prediction.key_features;
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if (features.length >= 4 && features.length <= 6) {
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quality += 0.3;
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strengths.push('Optimal number of features');
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} else if (features.length < 3) {
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weaknesses.push('Too few features');
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suggestions.push('Include at least 4 key features');
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} else {
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quality += 0.15;
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}
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// Check feature quality (should be concise)
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const wellFormedFeatures = features.filter(f =>
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f.length >= 10 && f.length <= 50
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);
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if (wellFormedFeatures.length === features.length) {
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quality += 0.2;
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strengths.push('All features are well-formed');
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} else {
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weaknesses.push('Some features need better formatting');
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suggestions.push('Keep features concise (10-50 chars)');
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}
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} else {
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weaknesses.push('Missing features');
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suggestions.push('Generate key features list');
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}
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return { quality, strengths, weaknesses, suggestions };
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}
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// Adapt prompt based on feedback
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private adaptPrompt(feedback: Feedback): string[] {
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const modifications: string[] = [];
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// Add specific instructions based on weaknesses
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feedback.suggestions.forEach(suggestion => {
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if (suggestion.includes('short')) {
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modifications.push('Write detailed descriptions (100-200 characters)');
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} else if (suggestion.includes('verbose')) {
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modifications.push('Keep descriptions concise and focused');
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} else if (suggestion.includes('engaging')) {
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modifications.push('Use descriptive, engaging language');
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} else if (suggestion.includes('features')) {
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modifications.push('Include 4-6 specific, measurable key features');
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} else if (suggestion.includes('concise')) {
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modifications.push('Format features as short, punchy statements');
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}
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});
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// Remove duplicates
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return [...new Set(modifications)];
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}
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// Generate with current prompt
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private async generate(input: any): Promise<Prediction> {
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// Build enhanced signature with learned improvements
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const enhancedInstructions = this.currentPromptAdditions.length > 0
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? '\n\nImportant guidelines:\n' + this.currentPromptAdditions.map((s, i) => `${i + 1}. ${s}`).join('\n')
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: '';
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const signature = {
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input: 'product_name: string, category: string, price: number',
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output: 'description: string, key_features: string[]',
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description: 'Generate compelling product descriptions' + enhancedInstructions
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};
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const generator = new ChainOfThought(signature, { lm: this.lm });
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return await generator.forward(input);
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}
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// Main learning loop
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async learn(input: any, criteria: any = {}): Promise<void> {
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console.log('🧠 Starting Self-Learning Session\n');
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console.log('=' .repeat(70));
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console.log(`\nTarget Quality: ${(this.config.targetQualityThreshold * 100).toFixed(0)}%`);
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console.log(`Max Iterations: ${this.config.maxIterations}`);
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console.log(`Input: ${JSON.stringify(input, null, 2)}\n`);
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console.log('=' .repeat(70) + '\n');
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let iteration = 0;
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let previousQuality = 0;
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while (iteration < this.config.maxIterations) {
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iteration++;
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console.log(`\n📊 Iteration ${iteration}/${this.config.maxIterations}`);
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console.log('─'.repeat(70));
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// Generate output
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const startTime = Date.now();
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const output = await this.generate(input);
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const duration = Date.now() - startTime;
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// Evaluate
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const feedback = this.evaluateOutput(output, criteria);
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// Store in history
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this.history.push({
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iteration,
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quality: feedback.quality,
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output,
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feedback,
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promptModifications: [...this.currentPromptAdditions],
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timestamp: new Date()
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});
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// Display results
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console.log(`\n⏱️ Generation time: ${duration}ms`);
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console.log(`\n📝 Output:`);
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console.log(` Description: ${output.description || 'N/A'}`);
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if (output.key_features) {
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console.log(` Features:`);
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output.key_features.forEach((f: string) => console.log(` • ${f}`));
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}
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console.log(`\n📈 Quality: ${(feedback.quality * 100).toFixed(1)}%`);
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if (feedback.strengths.length > 0) {
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console.log(`\n✅ Strengths:`);
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feedback.strengths.forEach(s => console.log(` • ${s}`));
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}
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if (feedback.weaknesses.length > 0) {
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console.log(`\n⚠️ Weaknesses:`);
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feedback.weaknesses.forEach(w => console.log(` • ${w}`));
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}
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// Check if target reached
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if (feedback.quality >= this.config.targetQualityThreshold) {
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console.log(`\n🎯 Target quality reached!`);
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break;
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}
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// Check for improvement
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const improvement = feedback.quality - previousQuality;
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if (iteration > 1 && improvement < this.config.minImprovement) {
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console.log(`\n⚠️ Improvement too small (${(improvement * 100).toFixed(1)}%), stopping...`);
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break;
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}
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// Adapt for next iteration
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const modifications = this.adaptPrompt(feedback);
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if (modifications.length > 0) {
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console.log(`\n🔧 Adapting strategy:`);
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modifications.forEach(m => console.log(` • ${m}`));
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// Add new modifications
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modifications.forEach(m => {
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if (!this.currentPromptAdditions.includes(m)) {
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this.currentPromptAdditions.push(m);
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}
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});
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}
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previousQuality = feedback.quality;
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// Brief pause between iterations
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await new Promise(resolve => setTimeout(resolve, 1000));
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}
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// Final summary
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this.displaySummary();
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}
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// Display learning summary
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private displaySummary(): void {
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console.log('\n\n' + '=' .repeat(70));
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console.log('\n🎓 LEARNING SUMMARY\n');
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if (this.history.length === 0) {
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console.log('No learning history available.\n');
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return;
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}
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const firstQuality = this.history[0].quality;
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const lastQuality = this.history[this.history.length - 1].quality;
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const improvement = lastQuality - firstQuality;
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const improvementPercent = (improvement / firstQuality) * 100;
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console.log(`Total Iterations: ${this.history.length}`);
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console.log(`Starting Quality: ${(firstQuality * 100).toFixed(1)}%`);
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console.log(`Final Quality: ${(lastQuality * 100).toFixed(1)}%`);
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console.log(`Improvement: ${improvement >= 0 ? '+' : ''}${(improvement * 100).toFixed(1)}% (${improvementPercent >= 0 ? '+' : ''}${improvementPercent.toFixed(1)}%)`);
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console.log(`\n📊 Quality Progression:`);
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this.history.forEach(entry => {
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const bar = '█'.repeat(Math.floor(entry.quality * 50));
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const percent = (entry.quality * 100).toFixed(1);
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console.log(` Iteration ${entry.iteration}: ${bar} ${percent}%`);
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});
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console.log(`\n🔧 Learned Improvements (${this.currentPromptAdditions.length}):`);
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this.currentPromptAdditions.forEach((mod, i) => {
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console.log(` ${i + 1}. ${mod}`);
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});
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console.log('\n💡 Key Insights:');
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if (improvement > 0) {
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console.log(` ✓ System successfully learned and improved`);
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console.log(` ✓ Quality increased by ${(improvement * 100).toFixed(1)}%`);
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}
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console.log(` ✓ Discovered ${this.currentPromptAdditions.length} optimization strategies`);
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console.log(` ✓ These improvements can be applied to future generations\n`);
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console.log('=' .repeat(70) + '\n');
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}
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// Get the learned prompt modifications
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getLearnedImprovements(): string[] {
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return [...this.currentPromptAdditions];
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}
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// Get learning history
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getHistory(): LearningEntry[] {
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return [...this.history];
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}
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}
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// Main execution
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async function runSelfLearning() {
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const generator = new SelfLearningGenerator({
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targetQualityThreshold: 0.85,
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maxIterations: 8,
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improvementRate: 0.15,
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minImprovement: 0.03
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});
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const testProduct = {
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product_name: 'Professional DSLR Camera',
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category: 'Photography',
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price: 1299
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};
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await generator.learn(testProduct);
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// Save learned improvements
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const improvements = generator.getLearnedImprovements();
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console.log('📝 Learned improvements can be reused:\n');
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console.log(JSON.stringify(improvements, null, 2) + '\n');
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}
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// Run the example
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if (import.meta.url === `file://${process.argv[1]}`) {
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runSelfLearning().catch(error => {
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console.error('❌ Learning failed:', error);
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process.exit(1);
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});
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}
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export { SelfLearningGenerator, LearningConfig, LearningEntry };
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