RAG Against The Machine: Injecting First-Party Data Into AI Models For Better Results | AdExchanger
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from AdExchanger1 month ago
Proprietary data sets can significantly impact key marketing areas for generative AI, like content production, campaign insights, and customer service.
AdExchangerhttps://www.adexchanger.com/data-driven-thinking/rag-against-the-machine-injecting-first-party-data-into-ai-models-for-better-results/
First-party data can be integrated into foundation models by utilizing processes like retrieval augmented generation pipelines (RAG) to enhance AI tools from OpenAI, Google, and Meta.
AdExchangerhttps://www.adexchanger.com/data-driven-thinking/rag-against-the-machine-injecting-first-party-data-into-ai-models-for-better-results/
Generative AI applications rely on user-provided instructions to produce text or imagery, emphasizing the importance of incorporating context, especially when utilizing tools like GPT, Dall-E, Imagen, Gemini, or Llama.
AdExchangerhttps://www.adexchanger.com/data-driven-thinking/rag-against-the-machine-injecting-first-party-data-into-ai-models-for-better-results/
While foundation models from Google and OpenAI are powerful, they may lack up-to-date and brand-specific information, highlighting the need for injecting first-party data to personalize AI outputs for specific audience groups.
AdExchangerhttps://www.adexchanger.com/data-driven-thinking/rag-against-the-machine-injecting-first-party-data-into-ai-models-for-better-results/
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#first-party-data #generative-ai #marketing-applications #personalization #ai-enabled-campaigns
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