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This is where Retrieval-Augmented Generation (RAG) comes in – an approach that combines the language capabilities of LLMs with the ability to access dynamic and specific data sources. This allows us to link world knowledge with specialized knowledge to deliver contextual and precise answers while also tracking which data was used to respond to a prompt.

The Limitations of LLMs and RAG as a Solution

LLMs obtain their knowledge from a training dataset and can therefore only retrieve information that existed up until their last training date. In dynamic environments where timeliness and expertise are crucial, this is a major limitation. RAG offers a solution by enabling LLMs to incorporate information from external and current data sources. This connection to verified knowledge increases the precision and timeliness of generated content.

What is RAG?

Retrieval-Augmented Generation combines an LLM’s language capabilities with a retrieval system that can access relevant data and make it available to the model. The process generally consists of the following steps:

  1. User Query: A user submits a query that may require current or specific information.
  2. Retrieval: A retrieval system searches defined external sources, such as databases or knowledge repositories, and finds matching documents or text passages.
  3. Augmentation: This relevant information is passed to the LLM and serves as contextual foundation for generating the response.
  4. Generation: The LLM uses the additional data to create a well-founded, contextualized response.

Through this combination, RAG can deliver answers that contain not only general knowledge but also highly specific and current information – a significant improvement over pure LLMs. [1]

Grounding: Anchoring in Verified Data

One of RAG’s principal advantages is “grounding” – the process of anchoring responses in verifiable data sources. This makes the generated knowledge more precise and reliable since the answers are based on explicit data sources that users can verify. This anchoring is crucial in fields like medicine, science, and law, where accurate and verifiable information is essential. With RAG, generated answers can be backed by a verifiable data foundation, increasing the security and quality of the information.

The Motivation Behind RAG

The development of RAG is based on several important motivations:

  1. Timeliness: RAG enables LLMs to access current information by connecting to external sources. Organizations that regularly generate new content – including research reports and market analyses – can always incorporate up-to-date data into their answers through RAG.
  2. Specialized Knowledge: Many companies possess internal knowledge that is essential for specific use cases. Through RAG, this special knowledge can be incorporated into the answers, increasing the usefulness and applicability of generated responses in professional contexts.
  3. Trustworthiness: With RAG, it’s possible to track which sources contributed to the answer. This is particularly valuable in critical scenarios where accuracy and reliability of answers are crucial.
  4. Efficiency and Scalability: RAG makes it possible to efficiently utilize specialized knowledge without constantly needing to retrain the underlying LLM. Additionally, only relevant information is passed to an LLM, which reduces costs and response time.

Conclusion: An LLM with Reliable Data

RAG is the bridge between an LLM’s generic capabilities and the requirements for updated, specialized knowledge. By combining LLMs with dynamic, verified data sources, the model becomes both a comprehensive knowledge provider and a specialized advisor. This creates a system that can deliver not only informative but also more well-founded and contextualized answers – a crucial improvement for many professional and industrial applications.

In upcoming articles, we will dive deeper into the components and practical implementation of RAG, showing how to implement Retrieval-Augmented Generation and effectively deploy it in enterprise settings.

A brochure titled 'Retrieval-Augmented Generation' on a colorful surface with shades of blue and orange.

This article is an excerpt from our free primer on Retrieval-Augmented Generation (in German). A quick introduction for software architects and developers.

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  1. The notion that LLMs acquire additional knowledge through in–context learning is, in fact, illusory. The provided context influences the calculations in the transformer network‘s attention mechanism. Nevertheless, an LLM cannot calculate anything it doesn’t know. Due to the enormous size of LLMs, the supply of patterns is virtually inexhaustible and hardly reaches its limits through context from typical business data. What we perceive as hallucinations is the result of misguided calculations. RAG provides additional context for more stable, targeted calculation in our subject area. This creates the illusion that the LLM has “understood” our data.  ↩

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