Enterprise RAG 2026: Как снизить галлюцинации до <3%

# Псевдокод production-системы (2026 standard)
class MultiAgentRAG:
    def __init__(self):
        self.retriever = HybridRetriever()
        self.validator = RetrievalValidator()
        self.grounder = AnswerGrounder()
        self.scorer = ConfidenceScorer()
        self.arbiter = DebateArbiter()
    
    async def process_query(self, user_query: str, user_context: dict) -> dict:
        # 1. Стандартный RAG
        chunks = await self.retriever.retrieve(user_query, top_k=10)
        initial_answer = await self.synthesizer.generate(chunks, user_query)
        
        # 2. Запуск verification agents параллельно
        validation_task = self.validator.validate(chunks, user_query)
        grounding_task = self.grounder.check(initial_answer, chunks)
        
        # 3. Сбор метрик
        validation_result = await validation_task
        grounding_result = await grounding_task
        
        # 4. Confidence scoring (ключевой шаг!)
        confidence = self.scorer.calculate(
            retrieval_quality=validation_result.score,
            grounding_coverage=grounding_result.coverage,
            llm_confidence=initial_answer.metadata.confidence,
            agent_agreement=self._measure_agent_agreement([validation_result, grounding_result])
        )
        
        # 5. Принятие решений
        if confidence < 0.95:
            if confidence < 0.80:
                # Низкая уверенность → эскалация
                return self._escalate_to_human(user_query, initial_answer, confidence)
            else:
                # Средняя → повторный retrieval с уточнением
                refined_chunks = await self.retriever.retrieve(
                    user_query, 
                    context_clarification=validation_result.missing_aspects
                )
                final_answer = await self.synthesizer.generate(refined_chunks, user_query)
                confidence = self.scorer.recalculate_with_new_retrieval(...)
        
        return {
            "answer": final_answer if confidence >= 0.80 else initial_answer,
            "confidence": round(confidence * 100, 1),
            "validation_flags": validation_result.flags,
            "grounding_gaps": grounding_result.gaps,
            "trace_id": self._generate_trace_id()  # для debugging
        }