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Source channel @githubtrending · Post #14993 · Jul 24

#jupyter_notebook Retrieval Augmented Generation (RAG) helps large language models (LLMs) answer questions using up-to-date or private information by connecting them to external data sources, unlike fine-tuning which retrains the model on specific data. RAG is useful when you need current, dynamic information without costly retraining, making it ideal for tasks like customer support or knowledge management. Fine-tuning is better for deep expertise in a specialized field but requires more data and effort. Using RAG lets you get accurate, relevant answers quickly by combining the model’s language skills with fresh, specific data, improving usefulness and reliability. https://github.com/langchain-ai/rag-from-scratch

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DSR Corporation News

@dsr_news · Post #1036 · 06/19/2025, 09:18 AM

❓Интересуешься внедрением DI в Kotlin-скрипты и хочешь избежать tight coupling? Мы подробно разобрали это в свежей статье на Medium — ‘Keep your scripts decoupled: flexible and safe dependency injection for Kotlin Scripts’! Наши инженеры: Игорь Покотило, Senior Software Engineer, и Константин Некрасов, Principal Software Engineer рассказывают: ✅ как внедрить DI в скрипты так, чтобы не переписывать их при развитии приложения ✅ как использовать Spring IoC ✅ и как при этом не завязать бизнес-логику на инфраструктуру 📖 Читать: ссылка ✍️ Статья на английском языке #Kotlin#DependencyInjection#BackendDevelopment

Repositorio data science

@repo_science · Post #4259 · 12/08/2025, 03:46 AM

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