RU

Как мы научили ИИ читать корпоративные регламенты: от наивного RAG до измеримого pipeline

В статье - практический кейс создания корпоративного RAG-сервиса для поиска по внутренним регламентным и нормативным документам. Мы прошли путь от с…

RAGRetrieval-Augmented GenerationLLMкорпоративный поискгибридный поискsemantic searchBM25Arize Phoenixобработка документовкорпоративные знания
Habr
EN

How DoorDash Built an AI Shopping Assistant That Doesn’t Rely on the LLM Alone

DoorDash details the architecture behind Ask DoorDash, its AI-powered conversational shopping assistant, combining LLMs, specialized AI agents, MCP-…

MemoryPlatform EngineeringModel Context Protocol (MCP)Retrieval-Augmented GenerationDistributed SystemsArtificial IntelligenceChatBotsNatural Language ProcessingLarge language modelsPrompt EngineeringAgentsMicroservicesArchitecture & DesignDevelopmentAI, ML & Data Engineeringnews
InfoQ
EN

Inside Target’s LLM-Based System for Semantic Matching in Marketing Forecast Pipelines

Target built a generative AI system to improve marketing campaign forecasting by retrieving and ranking similar historical campaigns. Using embeddin…

Systems ThinkingRetrieval-Augmented GenerationLarge Concept Modelsvector databasesData AnalyticsObservabilityEvolutionary ArchitectureMLOpsMachine LearningMarketingGenerative AIModel Fine TuningBusiness AnalyticsArchitecture & DesignDevelopmentAI, ML & Data Engineeringnews
InfoQ