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- 10 RAG examples and use cases from real companies
In this blog, we compiled 10 real-world examples of how companies apply RAG to improve customer experience, automate routine tasks, and improve productivity Doordash, a food delivery company, enhances delivery support with a RAG-based chatbot
- Boost LLM Performance with RAG and Real-Time Data Integration
LangChain is an open-source framework for building data-aware and agent-driven applications using LLMs It provides pre-built RAG pipelines, making connecting LLMs to external data sources and customizing retrieval logic easier Key Features: Easy-to-implement RAG architecture; Flexible document retrieval methods; Supports multiple data types
- RAG Implementation with LLMs from Scratch: A Step-by-Step . . . - CustomGPT
Implementing Retrieval-Augmented Generation (RAG) can significantly enhance the capabilities of large language models (LLMs), making them more accurate and contextually relevant In this blog, we will guide you through the process of RAG implementation with LLM, discuss the RAG framework, and explore its applications
- Retrieval Augmented Generation (RAG) LLM: Examples - Data Analytics
For data scientists and product managers keen on deploying contextually sensitive LLMs in production, the Retrieval-Augmented Generation (RAG) pattern offers a compelling solution if they want to leverage contextual information with prompts sent by the end users Apart from RAG, one can also go for LLM fine tuning
- How to Build RAG Pipelines for LLM Projects? - GeeksforGeeks
RAG Pipeline Architecture 1 Data Collection The first stage of a RAG pipeline involves gathering unstructured data from various sources, such as documents, online articles, databases, and emails This data is typically raw and unorganized, so it needs to be collected and prepared for subsequent steps
- The Secret to Building Enterprise-Grade RAG Systems: Blending Real-Time . . .
Blending real-time data retrieval with powerful LLMs lets you deliver answers that are not just clever, but confident and correct Start by mapping business needs, picking the right tools, and instilling guardrails from day one
- RAG: How to Connect LLMs to External Sources - Markovate
RAG introduces a dynamic, real-time data assimilation layer to the static, pre-trained architecture of LLMs This confluence mitigates the inherent limitations of LLMs, such as computational rigidity and lack of post-training adaptability, by incorporating an external, up-to-date data source
- How to Build a RAG System with Open-Source LLMs
Learn step by step how to build a cost-effective RAG-enabled pipeline using open-source LLMs and tools like Langflow and Astra DB Ever wondered how ChatGPT seems to know about recent events? The secret sauce is “retrieval-augmented generation”—more commonly referred to as “RAG ”
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