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

1 years 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.

Google Ads Audit, Done Automatically

Featured

Run an automated Google Ads audit directly from Google Sheets with 40+ built-in checks. Review campaigns, ad groups, keywords, ads, and account settings to catch missed best practices and optimization gaps. Ideal for agencies and in-house teams to standardize audits, save hours of manual work, and turn findings into clear, actionable recommendations.