RAG (Retrieval Augmented Generation): How It Works and Providers
With the rise of Large Language Models (LLMs) companies are faced with the question of how to leverage their functionalities for their own applications. The available methods vary greatly in complexity and effort, ranging from simple Prompt Engineering to extensive fine-tuning. The topic of this article is a sort of ‘golden middle ground,’ known as RAG (Retrieval Augmented Generation). Learn in detail what this entails and which providers you should be aware of.
- 17. October. 2024
Patrick Ratheiser
CEO & Founder
Karin Schnedlitz
Content Managerin
RAG – Simply Explained
Imagine you’re hiring new employees. Typically, they are fluent in the preferred language from day one, ready to be trained and familiarized with your company’s operations. However, the company-specific knowledge—and where to find it—is initially new territory for them.This analogy describes the relationship between LLMs and the emerging RAG technology. LLMs excel at mastering and generating natural language, while RAG ‘augments’ the relevant company data.
Discover how RAG can revolutionize your data analysis!
Example from Customer Support
1. Retrieval: Information is retrieved from a knowledge base.
A customer sends an inquiry via email regarding the use of a specific product offered by the company. The system pulls relevant information from the internal database, which contains past similar inquiries and their solutions.
2. Augmented: The original prompt’s context is enriched.
The system uses the retrieved information from the database to enhance the context of the inquiry, identifying specific issues or common user errors associated with the product in question.
3. Generation: LLMs and augmented prompt generate the response.
Based on the enriched context, the LLM generates a precise, detailed, and helpful response for the customer, addressing the specific issue while also providing additional tips for optimal product use.
Welche What challenges exist with RAG?
The integration of RAG systems is complex because it requires the processing of various data sources and formats while maintaining high data quality. Additionally, these systems demand strict security measures regarding data protection to safeguard sensitive information and prevent misuse. At the same time, they must remain scalable to handle increasing volumes of data efficiently.
At Leftshift One, numerous successful integrations with clients have demonstrated that systems are continuously improving, especially through RAG, and delivering remarkable results even without costly fine-tuning.
RAG Providers to Watch
The following providers offer RAG solutions, ranging from general LLM applications to customized projects and smart productized solutions.
LLMs
- OpenAI
OpenAI utilizes RAG to extract contextually relevant information from data sources. By employing vector databases for semantic searches, which go beyond traditional keyword searches, OpenAI allows existing models to generate responses from new data, facilitating RAG integration into business processes.
- Cohere
Cohere introduced the Command-R model, specifically designed for RAG applications. This model works in conjunction with Cohere’s Embed and Rerank models, providing high-quality RAG functionality, including the integration of external data sources like search engines and databases.
Customized Projects
- Google AI
Google has incorporated advanced RAG capabilities into its AI platform, using Deep Retrieval to create highly scalable solutions for information retrieval. Google emphasizes the integration of its cloud services and AI functions to support seamless workflows.
- Azure Machine Learning
Microsoft, through Azure Machine Learning, offers deeply integrated RAG capabilities supporting a wide range of applications, from text analysis to complex question-answering systems. Azure places significant emphasis on security and data privacy.
Productized Solutions
- Langdock
Langdock leverages RAG technology to access internally stored data and knowledge during content generation, making responses more precise and personalized. This feature enables the AI to extract relevant information from various internal sources in real-time, offering a security advantage.
- Leftshift One
Leftshift One stands out among RAG providers for its unique combination of semantic search and RAG. Since 2017, this Austrian AI company has focused on making internal corporate knowledge efficient and secure within the AI context.
MyGPT is tailored to complement RAG technology through two key methods. Firstly, generic parsers cover a wide range of data structures. Additionally, user prompts are pre-processed, enabling the system to achieve optimal results. Based in Graz, Leftshift One has seen numerous successful client integrations, confirming that RAG systems are continuously improving and delivering remarkable results, even without expensive fine-tuning.
| Anbieter | Beschreibung | RAG-Lösung |
|---|---|---|
| Open AI | OpenAI utilizes RAG and vector databases to effectively extract information from data. This enhances the adaptation of models for business processes. | LLM |
| Cohere | Cohere has introduced the Command-R model for RAG applications, which enhances the integration of external data sources. | LLM |
| Google AI | Google has integrated advanced RAG capabilities for scalable information retrieval into its AI platform, promoting efficient workflows through cloud services. | Customized Projects |
| Azure Machine Learning | Microsoft offers deeply integrated RAG capabilities through Azure Machine Learning, supporting a wide range of applications with a strong emphasis on security and data privacy. | Customized Projects |
| Langdock | Langdock leverages RAG technology to generate more precise and personalized responses by securely using internally stored data in real-time. | Productized Solutions |
| Leftshift One | Leftshift One combines semantic search with RAG to securely integrate internal knowledge into AI since 2017. Their MyGPT solution delivers effective results without costly fine-tuning. | Productized Solutions |
What Does the Future of RAG Look Like?
In the future, the development of RAG technologies will continue to focus on data privacy, collaboration, and reliability. Privacy measures will be critical to protect sensitive information accessed through the use of external data sources. Furthermore, interdisciplinary collaboration will ensure the precision and customization of the technology to meet specific user needs. Lastly, the reliability of these systems will largely depend on the quality and timeliness of the data, as these factors directly influence accuracy and user trust.
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