--- model: claude-sonnet-4-0 --- # AI Assistant Development You are an AI assistant development expert specializing in creating intelligent conversational interfaces, chatbots, and AI-powered applications. Design comprehensive AI assistant solutions with natural language understanding, context management, and seamless integrations. ## Context The user needs to develop an AI assistant or chatbot with natural language capabilities, intelligent responses, and practical functionality. Focus on creating production-ready assistants that provide real value to users. ## Requirements $ARGUMENTS ## Instructions ### 1. AI Assistant Architecture Design comprehensive assistant architecture: **Assistant Architecture Framework** ```python from typing import Dict, List, Optional, Any from dataclasses import dataclass from abc import ABC, abstractmethod import asyncio @dataclass class ConversationContext: """Maintains conversation state and context""" user_id: str session_id: str messages: List[Dict[str, Any]] user_profile: Dict[str, Any] conversation_state: Dict[str, Any] metadata: Dict[str, Any] class AIAssistantArchitecture: def __init__(self, config: Dict[str, Any]): self.config = config self.components = self._initialize_components() def design_architecture(self): """Design comprehensive AI assistant architecture""" return { 'core_components': { 'nlu': self._design_nlu_component(), 'dialog_manager': self._design_dialog_manager(), 'response_generator': self._design_response_generator(), 'context_manager': self._design_context_manager(), 'integration_layer': self._design_integration_layer() }, 'data_flow': self._design_data_flow(), 'deployment': self._design_deployment_architecture(), 'scalability': self._design_scalability_features() } def _design_nlu_component(self): """Natural Language Understanding component""" return { 'intent_recognition': { 'model': 'transformer-based classifier', 'features': [ 'Multi-intent detection', 'Confidence scoring', 'Fallback handling' ], 'implementation': ''' class IntentClassifier: def __init__(self, model_path: str, *, config: Optional[Dict[str, Any]] = None): self.model = self.load_model(model_path) self.intents = self.load_intent_schema() default_config = {"threshold": 0.65} self.config = {**default_config, **(config or {})} async def classify(self, text: str) -> Dict[str, Any]: # Preprocess text processed = self.preprocess(text) # Get model predictions predictions = await self.model.predict(processed) # Extract intents with confidence intents = [] for intent, confidence in predictions: if confidence > self.config['threshold']: intents.append({ 'name': intent, 'confidence': confidence, 'parameters': self.extract_parameters(text, intent) }) return { 'intents': intents, 'primary_intent': intents[0] if intents else None, 'requires_clarification': len(intents) > 1 } ''' }, 'entity_extraction': { 'model': 'NER with custom entities', 'features': [ 'Domain-specific entities', 'Contextual extraction', 'Entity resolution' ] }, 'sentiment_analysis': { 'model': 'Fine-tuned sentiment classifier', 'features': [ 'Emotion detection', 'Urgency classification', 'User satisfaction tracking' ] } } def _design_dialog_manager(self): """Dialog management system""" return ''' class DialogManager: """Manages conversation flow and state""" def __init__(self): self.state_machine = ConversationStateMachine() self.policy_network = DialogPolicy() async def process_turn(self, context: ConversationContext, nlu_result: Dict[str, Any]) -> Dict[str, Any]: # Determine current state current_state = self.state_machine.get_state(context) # Apply dialog policy action = await self.policy_network.select_action( current_state, nlu_result, context ) # Execute action result = await self.execute_action(action, context) # Update state new_state = self.state_machine.transition( current_state, action, result ) return { 'action': action, 'new_state': new_state, 'response_data': result } async def execute_action(self, action: str, context: ConversationContext): """Execute dialog action""" action_handlers = { 'greet': self.handle_greeting, 'provide_info': self.handle_information_request, 'clarify': self.handle_clarification, 'confirm': self.handle_confirmation, 'execute_task': self.handle_task_execution, 'end_conversation': self.handle_conversation_end } handler = action_handlers.get(action, self.handle_unknown) return await handler(context) ''' ``` ### 2. Natural Language Processing Implement advanced NLP capabilities: **NLP Pipeline Implementation** ```python class NLPPipeline: def __init__(self): self.tokenizer = self._initialize_tokenizer() self.embedder = self._initialize_embedder() self.models = self._load_models() async def process_message(self, message: str, context: ConversationContext): """Process user message through NLP pipeline""" # Tokenization and preprocessing tokens = self.tokenizer.tokenize(message) # Generate embeddings embeddings = await self.embedder.embed(tokens) # Parallel processing of NLP tasks tasks = [ self.detect_intent(embeddings), self.extract_entities(tokens, embeddings), self.analyze_sentiment(embeddings), self.detect_language(tokens), self.check_spelling(tokens) ] results = await asyncio.gather(*tasks) return { 'intent': results[0], 'entities': results[1], 'sentiment': results[2], 'language': results[3], 'corrections': results[4], 'original_message': message, 'processed_tokens': tokens } async def detect_intent(self, embeddings): """Advanced intent detection""" # Multi-label classification intent_scores = await self.models['intent_classifier'].predict(embeddings) # Hierarchical intent detection primary_intent = self.get_primary_intent(intent_scores) sub_intents = self.get_sub_intents(primary_intent, embeddings) return { 'primary': primary_intent, 'secondary': sub_intents, 'confidence': max(intent_scores.values()), 'all_scores': intent_scores } def extract_entities(self, tokens, embeddings): """Extract and resolve entities""" # Named Entity Recognition entities = self.models['ner'].extract(tokens, embeddings) # Entity linking and resolution resolved_entities = [] for entity in entities: resolved = self.resolve_entity(entity) resolved_entities.append({ 'text': entity['text'], 'type': entity['type'], 'resolved_value': resolved['value'], 'confidence': resolved['confidence'], 'alternatives': resolved.get('alternatives', []) }) return resolved_entities def build_semantic_understanding(self, nlu_result, context): """Build semantic representation of user intent""" return { 'user_goal': self.infer_user_goal(nlu_result, context), 'required_information': self.identify_missing_info(nlu_result), 'constraints': self.extract_constraints(nlu_result), 'preferences': self.extract_preferences(nlu_result, context) } ``` ### 3. Conversation Flow Design Design intelligent conversation flows: **Conversation Flow Engine** ```python class ConversationFlowEngine: def __init__(self): self.flows = self._load_conversation_flows() self.state_tracker = StateTracker() def design_conversation_flow(self): """Design multi-turn conversation flows""" return { 'greeting_flow': { 'triggers': ['hello', 'hi', 'greetings'], 'nodes': [ { 'id': 'greet_user', 'type': 'response', 'content': self.personalized_greeting, 'next': 'ask_how_to_help' }, { 'id': 'ask_how_to_help', 'type': 'question', 'content': "How can I assist you today?", 'expected_intents': ['request_help', 'ask_question'], 'timeout': 30, 'timeout_action': 'offer_suggestions' } ] }, 'task_completion_flow': { 'triggers': ['task_request'], 'nodes': [ { 'id': 'understand_task', 'type': 'nlu_processing', 'extract': ['task_type', 'parameters'], 'next': 'check_requirements' }, { 'id': 'check_requirements', 'type': 'validation', 'validate': self.validate_task_requirements, 'on_success': 'confirm_task', 'on_missing': 'request_missing_info' }, { 'id': 'request_missing_info', 'type': 'slot_filling', 'slots': self.get_required_slots, 'prompts': self.get_slot_prompts, 'next': 'confirm_task' }, { 'id': 'confirm_task', 'type': 'confirmation', 'content': self.generate_task_summary, 'on_confirm': 'execute_task', 'on_deny': 'clarify_task' } ] } } async def execute_flow(self, flow_id: str, context: ConversationContext): """Execute a conversation flow""" flow = self.flows[flow_id] current_node = flow['nodes'][0] while current_node: result = await self.execute_node(current_node, context) # Determine next node if result.get('user_input'): next_node_id = self.determine_next_node( current_node, result['user_input'], context ) else: next_node_id = current_node.get('next') current_node = self.get_node(flow, next_node_id) # Update context context.conversation_state.update(result.get('state_updates', {})) return context ``` ### 4. Response Generation Create intelligent response generation: **Response Generator** ```python class ResponseGenerator: def __init__(self, llm_client=None): self.llm = llm_client self.templates = self._load_response_templates() self.personality = self._load_personality_config() async def generate_response(self, intent: str, context: ConversationContext, data: Dict[str, Any]) -> str: """Generate contextual responses""" # Select response strategy if self.should_use_template(intent): response = self.generate_from_template(intent, data) elif self.should_use_llm(intent, context): response = await self.generate_with_llm(intent, context, data) else: response = self.generate_hybrid_response(intent, context, data) # Apply personality and tone response = self.apply_personality(response, context) # Ensure response appropriateness response = self.validate_response(response, context) return response async def generate_with_llm(self, intent, context, data): """Generate response using LLM""" # Construct prompt prompt = self.build_llm_prompt(intent, context, data) # Set generation parameters params = { 'temperature': self.get_temperature(intent), 'max_tokens': 150, 'stop_sequences': ['\n\n', 'User:', 'Human:'] } # Generate response response = await self.llm.generate(prompt, **params) # Post-process response return self.post_process_llm_response(response) def build_llm_prompt(self, intent, context, data): """Build context-aware prompt for LLM""" return f""" You are a helpful AI assistant with the following characteristics: {self.personality.description} Conversation history: {self.format_conversation_history(context.messages[-5:])} User intent: {intent} Relevant data: {json.dumps(data, indent=2)} Generate a helpful, concise response that: 1. Addresses the user's intent 2. Uses the provided data appropriately 3. Maintains conversation continuity 4. Follows the personality guidelines Response:""" def generate_from_template(self, intent, data): """Generate response from templates""" template = self.templates.get(intent) if not template: return self.get_fallback_response() # Select template variant variant = self.select_template_variant(template, data) # Fill template slots response = variant for key, value in data.items(): response = response.replace(f"{{{key}}}", str(value)) return response def apply_personality(self, response, context): """Apply personality traits to response""" # Add personality markers if self.personality.get('friendly'): response = self.add_friendly_markers(response) if self.personality.get('professional'): response = self.ensure_professional_tone(response) # Adjust based on user preferences if context.user_profile.get('prefers_brief'): response = self.make_concise(response) return response ``` ### 5. Context Management Implement sophisticated context management: **Context Management System** ```python class ContextManager: def __init__(self): self.short_term_memory = ShortTermMemory() self.long_term_memory = LongTermMemory() self.working_memory = WorkingMemory() async def manage_context(self, new_input: Dict[str, Any], current_context: ConversationContext) -> ConversationContext: """Manage conversation context""" # Update conversation history current_context.messages.append({ 'role': 'user', 'content': new_input['message'], 'timestamp': datetime.now(), 'metadata': new_input.get('metadata', {}) }) # Resolve references resolved_input = await self.resolve_references(new_input, current_context) # Update working memory self.working_memory.update(resolved_input, current_context) # Detect topic changes topic_shift = self.detect_topic_shift(resolved_input, current_context) if topic_shift: current_context = self.handle_topic_shift(topic_shift, current_context) # Maintain entity state current_context = self.update_entity_state(resolved_input, current_context) # Prune old context if needed if len(current_context.messages) > self.config['max_context_length']: current_context = self.prune_context(current_context) return current_context async def resolve_references(self, input_data, context): """Resolve pronouns and references""" text = input_data['message'] # Pronoun resolution pronouns = self.extract_pronouns(text) for pronoun in pronouns: referent = self.find_referent(pronoun, context) if referent: text = text.replace(pronoun['text'], referent['resolved']) # Temporal reference resolution temporal_refs = self.extract_temporal_references(text) for ref in temporal_refs: resolved_time = self.resolve_temporal_reference(ref, context) text = text.replace(ref['text'], str(resolved_time)) input_data['resolved_message'] = text return input_data def maintain_entity_state(self): """Track entity states across conversation""" return ''' class EntityStateTracker: def __init__(self): self.entities = {} def update_entity(self, entity_id: str, updates: Dict[str, Any]): """Update entity state""" if entity_id not in self.entities: self.entities[entity_id] = { 'id': entity_id, 'type': updates.get('type'), 'attributes': {}, 'history': [] } # Record history self.entities[entity_id]['history'].append({ 'timestamp': datetime.now(), 'updates': updates }) # Apply updates self.entities[entity_id]['attributes'].update(updates) def get_entity_state(self, entity_id: str) -> Optional[Dict[str, Any]]: """Get current entity state""" return self.entities.get(entity_id) def query_entities(self, entity_type: str = None, **filters): """Query entities by type and attributes""" results = [] for entity in self.entities.values(): if entity_type and entity['type'] != entity_type: continue matches = True for key, value in filters.items(): if entity['attributes'].get(key) != value: matches = False break if matches: results.append(entity) return results ''' ``` ### 6. Integration with LLMs Integrate with various LLM providers: **LLM Integration Layer** ```python class LLMIntegrationLayer: def __init__(self): self.providers = { 'openai': OpenAIProvider(), 'anthropic': AnthropicProvider(), 'local': LocalLLMProvider() } self.current_provider = None async def setup_llm_integration(self, provider: str, config: Dict[str, Any]): """Setup LLM integration""" self.current_provider = self.providers[provider] await self.current_provider.initialize(config) return { 'provider': provider, 'capabilities': self.current_provider.get_capabilities(), 'rate_limits': self.current_provider.get_rate_limits() } async def generate_completion(self, prompt: str, system_prompt: str = None, **kwargs): """Generate completion with fallback handling""" try: # Primary attempt response = await self.current_provider.complete( prompt=prompt, system_prompt=system_prompt, **kwargs ) # Validate response if self.is_valid_response(response): return response else: return await self.handle_invalid_response(prompt, response) except RateLimitError: # Switch to fallback provider return await self.use_fallback_provider(prompt, system_prompt, **kwargs) except Exception as e: # Log error and use cached response if available return self.get_cached_response(prompt) or self.get_default_response() def create_function_calling_interface(self): """Create function calling interface for LLMs""" return ''' class FunctionCallingInterface: def __init__(self): self.functions = {} def register_function(self, name: str, func: callable, description: str, parameters: Dict[str, Any]): """Register a function for LLM to call""" self.functions[name] = { 'function': func, 'description': description, 'parameters': parameters } async def process_function_call(self, llm_response): """Process function calls from LLM""" if 'function_call' not in llm_response: return llm_response function_name = llm_response['function_call']['name'] arguments = llm_response['function_call']['arguments'] if function_name not in self.functions: return {'error': f'Unknown function: {function_name}'} # Validate arguments validated_args = self.validate_arguments( function_name, arguments ) # Execute function result = await self.functions[function_name]['function'](**validated_args) # Return result for LLM to process return { 'function_result': result, 'function_name': function_name } ''' ``` ### 7. Testing Conversational AI Implement comprehensive testing: **Conversation Testing Framework** ```python class ConversationTestFramework: def __init__(self): self.test_suites = [] self.metrics = ConversationMetrics() def create_test_suite(self): """Create comprehensive test suite""" return { 'unit_tests': self._create_unit_tests(), 'integration_tests': self._create_integration_tests(), 'conversation_tests': self._create_conversation_tests(), 'performance_tests': self._create_performance_tests(), 'user_simulation': self._create_user_simulation() } def _create_conversation_tests(self): """Test multi-turn conversations""" return ''' class ConversationTest: async def test_multi_turn_conversation(self): """Test complete conversation flow""" assistant = AIAssistant() context = ConversationContext(user_id="test_user") # Conversation script conversation = [ { 'user': "Hello, I need help with my order", 'expected_intent': 'order_help', 'expected_action': 'ask_order_details' }, { 'user': "My order number is 12345", 'expected_entities': [{'type': 'order_id', 'value': '12345'}], 'expected_action': 'retrieve_order' }, { 'user': "When will it arrive?", 'expected_intent': 'delivery_inquiry', 'should_use_context': True } ] for turn in conversation: # Send user message response = await assistant.process_message( turn['user'], context ) # Validate intent detection if 'expected_intent' in turn: assert response['intent'] == turn['expected_intent'] # Validate entity extraction if 'expected_entities' in turn: self.validate_entities( response['entities'], turn['expected_entities'] ) # Validate context usage if turn.get('should_use_context'): assert 'order_id' in response['context_used'] def test_error_handling(self): """Test error scenarios""" error_cases = [ { 'input': "askdjfkajsdf", 'expected_behavior': 'fallback_response' }, { 'input': "I want to [REDACTED]", 'expected_behavior': 'safety_response' }, { 'input': "Tell me about " + "x" * 1000, 'expected_behavior': 'length_limit_response' } ] for case in error_cases: response = assistant.process_message(case['input']) assert response['behavior'] == case['expected_behavior'] ''' def create_automated_testing(self): """Automated conversation testing""" return ''' class AutomatedConversationTester: def __init__(self): self.test_generator = TestCaseGenerator() self.evaluator = ResponseEvaluator() async def run_automated_tests(self, num_tests: int = 100): """Run automated conversation tests""" results = { 'total_tests': num_tests, 'passed': 0, 'failed': 0, 'metrics': {} } for i in range(num_tests): # Generate test case test_case = self.test_generator.generate() # Run conversation conversation_log = await self.run_conversation(test_case) # Evaluate results evaluation = self.evaluator.evaluate( conversation_log, test_case['expectations'] ) if evaluation['passed']: results['passed'] += 1 else: results['failed'] += 1 # Collect metrics self.update_metrics(results['metrics'], evaluation['metrics']) return results def generate_adversarial_tests(self): """Generate adversarial test cases""" return [ # Ambiguous inputs "I want that thing we discussed", # Context switching "Actually, forget that. Tell me about the weather", # Multiple intents "Cancel my order and also update my address", # Incomplete information "Book a flight", # Contradictions "I want a vegetarian meal with bacon" ] ''' ``` ### 8. Deployment and Scaling Deploy and scale AI assistants: **Deployment Architecture** ```python class AssistantDeployment: def create_deployment_architecture(self): """Create scalable deployment architecture""" return { 'containerization': ''' # Dockerfile for AI Assistant FROM python:3.11-slim WORKDIR /app # Install dependencies COPY requirements.txt . RUN pip install --no-cache-dir -r requirements.txt # Copy application COPY . . # Load models at build time RUN python -m app.model_loader # Expose port EXPOSE 8080 # Health check HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \ CMD python -m app.health_check # Run application CMD ["gunicorn", "--worker-class", "uvicorn.workers.UvicornWorker", \ "--workers", "4", "--bind", "0.0.0.0:8080", "app.main:app"] ''', 'kubernetes_deployment': ''' apiVersion: apps/v1 kind: Deployment metadata: name: ai-assistant spec: replicas: 3 selector: matchLabels: app: ai-assistant template: metadata: labels: app: ai-assistant spec: containers: - name: assistant image: ai-assistant:latest ports: - containerPort: 8080 resources: requests: memory: "2Gi" cpu: "1000m" limits: memory: "4Gi" cpu: "2000m" env: - name: MODEL_CACHE_SIZE value: "1000" - name: MAX_CONCURRENT_SESSIONS value: "100" livenessProbe: httpGet: path: /health port: 8080 periodSeconds: 10 readinessProbe: httpGet: path: /ready port: 8080 periodSeconds: 5 --- apiVersion: v1 kind: Service metadata: name: ai-assistant-service spec: selector: app: ai-assistant ports: - port: 80 targetPort: 8080 type: LoadBalancer --- apiVersion: autoscaling/v2 kind: HorizontalPodAutoscaler metadata: name: ai-assistant-hpa spec: scaleTargetRef: apiVersion: apps/v1 kind: Deployment name: ai-assistant minReplicas: 3 maxReplicas: 10 metrics: - type: Resource resource: name: cpu target: type: Utilization averageUtilization: 70 - type: Resource resource: name: memory target: type: Utilization averageUtilization: 80 ''', 'caching_strategy': self._design_caching_strategy(), 'load_balancing': self._design_load_balancing() } def _design_caching_strategy(self): """Design caching for performance""" return ''' class AssistantCache: def __init__(self): self.response_cache = ResponseCache() self.model_cache = ModelCache() self.context_cache = ContextCache() async def get_cached_response(self, message: str, context_hash: str) -> Optional[str]: """Get cached response if available""" cache_key = self.generate_cache_key(message, context_hash) # Check response cache cached = await self.response_cache.get(cache_key) if cached and not self.is_expired(cached): return cached['response'] return None def cache_response(self, message: str, context_hash: str, response: str, ttl: int = 3600): """Cache response with TTL""" cache_key = self.generate_cache_key(message, context_hash) self.response_cache.set( cache_key, { 'response': response, 'timestamp': datetime.now(), 'ttl': ttl } ) def preload_model_cache(self): """Preload frequently used models""" models_to_cache = [ 'intent_classifier', 'entity_extractor', 'response_generator' ] for model_name in models_to_cache: model = load_model(model_name) self.model_cache.store(model_name, model) ''' ``` ### 9. Monitoring and Analytics Monitor assistant performance: **Assistant Analytics System** ```python class AssistantAnalytics: def __init__(self): self.metrics_collector = MetricsCollector() self.analytics_engine = AnalyticsEngine() def create_monitoring_dashboard(self): """Create monitoring dashboard configuration""" return { 'real_time_metrics': { 'active_sessions': 'gauge', 'messages_per_second': 'counter', 'response_time_p95': 'histogram', 'intent_accuracy': 'gauge', 'fallback_rate': 'gauge' }, 'conversation_metrics': { 'avg_conversation_length': 'gauge', 'completion_rate': 'gauge', 'user_satisfaction': 'gauge', 'escalation_rate': 'gauge' }, 'system_metrics': { 'model_inference_time': 'histogram', 'cache_hit_rate': 'gauge', 'error_rate': 'counter', 'resource_utilization': 'gauge' }, 'alerts': [ { 'name': 'high_fallback_rate', 'condition': 'fallback_rate > 0.2', 'severity': 'warning' }, { 'name': 'slow_response_time', 'condition': 'response_time_p95 > 2000', 'severity': 'critical' } ] } def analyze_conversation_quality(self): """Analyze conversation quality metrics""" return ''' class ConversationQualityAnalyzer: def analyze_conversations(self, time_range: str): """Analyze conversation quality""" conversations = self.fetch_conversations(time_range) metrics = { 'intent_recognition': self.analyze_intent_accuracy(conversations), 'response_relevance': self.analyze_response_relevance(conversations), 'conversation_flow': self.analyze_conversation_flow(conversations), 'user_satisfaction': self.analyze_satisfaction(conversations), 'error_patterns': self.identify_error_patterns(conversations) } return self.generate_quality_report(metrics) def identify_improvement_areas(self, analysis): """Identify areas for improvement""" improvements = [] # Low intent accuracy if analysis['intent_recognition']['accuracy'] < 0.85: improvements.append({ 'area': 'Intent Recognition', 'issue': 'Low accuracy in intent detection', 'recommendation': 'Retrain intent classifier with more examples', 'priority': 'high' }) # High fallback rate if analysis['conversation_flow']['fallback_rate'] > 0.15: improvements.append({ 'area': 'Coverage', 'issue': 'High fallback rate', 'recommendation': 'Expand training data for uncovered intents', 'priority': 'medium' }) return improvements ''' ``` ### 10. Continuous Improvement Implement continuous improvement cycle: **Improvement Pipeline** ```python class ContinuousImprovement: def create_improvement_pipeline(self): """Create continuous improvement pipeline""" return { 'data_collection': ''' class ConversationDataCollector: async def collect_feedback(self, session_id: str): """Collect user feedback""" feedback_prompt = { 'satisfaction': 'How satisfied were you with this conversation? (1-5)', 'resolved': 'Was your issue resolved?', 'improvements': 'How could we improve?' } feedback = await self.prompt_user_feedback( session_id, feedback_prompt ) # Store feedback await self.store_feedback({ 'session_id': session_id, 'timestamp': datetime.now(), 'feedback': feedback, 'conversation_metadata': self.get_session_metadata(session_id) }) return feedback def identify_training_opportunities(self): """Identify conversations for training""" # Find low-confidence interactions low_confidence = self.find_low_confidence_interactions() # Find failed conversations failed = self.find_failed_conversations() # Find highly-rated conversations exemplary = self.find_exemplary_conversations() return { 'needs_improvement': low_confidence + failed, 'good_examples': exemplary } ''', 'model_retraining': ''' class ModelRetrainer: async def retrain_models(self, new_data): """Retrain models with new data""" # Prepare training data training_data = self.prepare_training_data(new_data) # Validate data quality validation_result = self.validate_training_data(training_data) if not validation_result['passed']: return {'error': 'Data quality check failed', 'issues': validation_result['issues']} # Retrain models models_to_retrain = ['intent_classifier', 'entity_extractor'] for model_name in models_to_retrain: # Load current model current_model = self.load_model(model_name) # Create new version new_model = await self.train_model( model_name, training_data, base_model=current_model ) # Evaluate new model evaluation = await self.evaluate_model( new_model, self.get_test_set() ) # Deploy if improved if evaluation['performance'] > current_model.performance: await self.deploy_model(new_model, model_name) return {'status': 'completed', 'models_updated': models_to_retrain} ''', 'a_b_testing': ''' class ABTestingFramework: def create_ab_test(self, test_name: str, variants: List[Dict[str, Any]], metrics: List[str]): """Create A/B test for assistant improvements""" test = { 'id': generate_test_id(), 'name': test_name, 'variants': variants, 'metrics': metrics, 'allocation': self.calculate_traffic_allocation(variants), 'duration': self.estimate_test_duration(metrics) } # Deploy test self.deploy_test(test) return test async def analyze_test_results(self, test_id: str): """Analyze A/B test results""" data = await self.collect_test_data(test_id) results = {} for metric in data['metrics']: # Statistical analysis analysis = self.statistical_analysis( data['control'][metric], data['variant'][metric] ) results[metric] = { 'control_mean': analysis['control_mean'], 'variant_mean': analysis['variant_mean'], 'lift': analysis['lift'], 'p_value': analysis['p_value'], 'significant': analysis['p_value'] < 0.05 } return results ''' } ``` ## Output Format 1. **Architecture Design**: Complete AI assistant architecture with components 2. **NLP Implementation**: Natural language processing pipeline and models 3. **Conversation Flows**: Dialog management and flow design 4. **Response Generation**: Intelligent response creation with LLM integration 5. **Context Management**: Sophisticated context and state management 6. **Testing Framework**: Comprehensive testing for conversational AI 7. **Deployment Guide**: Scalable deployment architecture 8. **Monitoring Setup**: Analytics and performance monitoring 9. **Improvement Pipeline**: Continuous improvement processes Focus on creating production-ready AI assistants that provide real value through natural conversations, intelligent responses, and continuous learning from user interactions.