#retrieval-augmented-generation

#retrieval-augmented-generation

Retrieval Augmented Generation (RAG) combines the power of Large Language Models (LLMs) with a custom knowledge base, enabling precise and contextually relevant responses from customers.

Fine-tuning provides consistent and fast responses, but requires lengthy retraining for updates, while RAG offers instant updates but involves handling latency and interface challenges.

Cloudflare's AutoRAG automates the complex process of retrieval-augmented generation, enabling developers to integrate data and improve LLM accuracy without a patchwork of tools.

The allure of large context windows in AI models promises vast data handling and perfect recall, but reality often diverges from these enticing features.