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Optimizing RAG Pipelines: Indexing, Query, Retrieval, Document Selection, and Context

Optimizing RAG Pipelines: Indexing, Query, Retrieval, Document Selection, and Context

10 months ago

Retrieval Augmented Generation (RAG) encodes data into embeddings and indexes it in a vector database. When a user queries, it searches for similar embeddings to construct a prompt for an LLM. The RAG pipeline includes indexing, querying, retrieval, document selection, and context optimization. Strategies for optimization include indexing by small data chunks, questions the document answers, and document summaries.

Marketing Workflows Powered by AI

Marketing Workflows Powered by AI

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Markifact is a no-code marketing automation platform that lets users create AI-driven workflows for automating marketing tasks. Users can trigger workflows, connect apps, and track performance through a visual interface. The platform offers pre-built templates, integrates with various marketing tools, and supports team collaboration.

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