Retrieval-Augmented Generation (RAG) is an innovative method that optimizes the performance of an extensive language model by drawing on a comprehensive knowledge base outside of its traditional training data before generating a response. As is well known, large language models (LLMs) are created through intensive training with huge amounts of data and the use of billions of parameters to produce original outputs for a wide variety of tasks, whether it is answering questions, translating languages or completing sentences.

However, RAG's innovative approach goes one step further by expanding the already impressive capabilities of LLMs in specific areas of expertise or even an organization's internal knowledge base, without the need for extensive retraining of the model. This method not only provides a cost-effective solution to improve LLM results, but also ensures that they remain relevant, accurate, and extremely useful in various contexts. By integrating external sources of knowledge, RAG is able to provide even deeper insight into complex issues and significantly increase the performance of language models in their field of application.

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The challenges of LLMs & RAG as a solution

LLMs are undeniably a key technology for artificial intelligence (AI), which forms the basis for intelligent chatbots and other natural language processing (NLP) applications. However, the nature of LLM technology involves a certain amount of unpredictability in the models' responses. In addition, the training data from LLMs is static and reflects a specific level of knowledge at a fixed point in time.

Challenges of LLMs:

• The presentation of false information when an appropriate answer is not available.
• The presentation of outdated or too general information when users expect a specific and up-to-date answer.
• Generating answers from non-authoritative sources.
• The development of inaccurate answers due to terminological confusion, as different training sources can use the same terminology to describe different concepts.

That's where RAG comes in, as an approach to tackling some of these challenges. RAG instructs the LLM to retrieve relevant information from authoritative sources that have been defined in advance. This gives companies greater control over the model's text output, while users gain insight into the process of how the LLM generates the response. This not only improves accuracy, but also strengthens user trust, as they know that the answers are based on a wider and more trustworthy foundation of knowledge.

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The benefits of RAG

Retrieval-Augmented Generation (RAG) offers a number of advantages that make it an extremely promising approach in the world of artificial intelligence and natural language processing:

1. Improved accuracy: By integrating external sources of knowledge, LLMs can provide more accurate answers as they can access a wider and more reliable source of information.
2. Timeliness of information: RAG makes it possible to retrieve up-to-date information from external sources, which ensures that the answers generated are relevant and up to date. This is particularly important in fast-paced environments where information is constantly changing.
3. Trusted answers: By using authoritative sources of knowledge, the answers generated can build user trust because they know that the information comes from trustworthy sources.
4. Better control over output: Organizations can better control the quality of the model's text output because they have the ability to select and customize the sources of knowledge that the model accesses.
5. Expanding areas of application: RAG expands the areas of application of LLMs by allowing them to effectively use specific domains or internal knowledge bases of organizations without the need to retrain the model.
6. Cost efficiency: Compared to completely retraining the model, RAG is a more cost-effective solution for improving LLM performance because it optimizes existing models rather than starting from scratch.

Overall, RAG provides an elegant solution to some of the challenges that LLMs face, enabling them to deliver more accurate, timely, and trustworthy answers that better meet users' needs.

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How RAG works

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Retrieval-Augmented Generation (RAG) combines two important components to generate high-quality answers: the large language model (LLM) and an external knowledge base. Here's a basic way RAG works:

1. Large language model (LLM): The LLM forms the basis of the system. It is an extensive artificial neural network that has been trained with huge amounts of text data. These models can answer questions, generate texts, and perform many other natural language processing tasks.
2. External knowledge base: RAG accesses an external knowledge base or knowledge sources outside of the LLM's training data. This knowledge base can come from a variety of sources, including sources containing authoritative and relevant information on a wide range of topics.
3. Retrieval procedure: Before the LLM generates a response, RAG first carries out a retrieval process. The input, such as a question, is used to retrieve relevant information from the external knowledge base. This is done through various information retrieval techniques, such as searching, indexing, or querying the knowledge source based on the context of the input.
4. Generation process: Once relevant information has been retrieved from the external knowledge base, RAG combines it with the LLM's internal knowledge to generate a high-quality answer. The LLM uses its trained skills to interpret and formulate the information in natural language, taking into account the context of the input and the external information retrieved.
5. Output of the answer: Finally, RAG outputs the generated response, which is now based on a combination of the LLM's internal knowledge and the information retrieved externally. This answer is usually more precise, timely, and trustworthy as it is based on a wider body of information.

In summary, RAG integrates the LLM's internal knowledge with external sources of knowledge to generate high-quality answers that better meet users' needs. This approach makes it possible to provide more accurate, timely, and trustworthy information while continuing to utilize the flexibility and power of LLMs.

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Semantic search vs. RAG

Retrieval-augmented generation (RAG) and semantic search are both techniques in the area of information retrieval and natural language processing (NLP), but have different approaches and goals.

1. Retrieval-Augmented Generation (RAG):

• Retrieval-Augmented Generation is about improving the generation of texts or answers by integrating retrieval components. This means that instead of just relying on previous training data, the system actively retrieves external information and includes it in the generation process. This could mean, for example, that the model queries a database or knowledge graph before generation to obtain relevant information that can be integrated into the generated text. In this way, generation models such as GPT-3 can produce more contextually relevant and informative output by taking into account external sources of knowledge.

1. Semantic search:

• Semantic search refers to a method of searching for information that not only looks at keywords but also takes into account the meaning and relationship between the words. The goal is to provide relevant information based on the semantic meaning of the search query, rather than just reacting to the exact occurrence of keywords. This type of search often uses semantic models or knowledge graphs to understand the meaning of terms and make connections between different concepts. Essentially, semantic search tries to understand the intent behind a search query in order to deliver better results.

In summary, retrieval-augmented generation focuses on integrating retrieval mechanisms into the process of generating texts, while semantic search aims to find relevant information based on the meaning of search queries, taking into account semantic relationships between terms.

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How Bavest enables the creation of retrieval-augmented generation for fintechs, banks and asset managers

Our infrastructure allows access to current financial data, ESG and climate data, and sentiment data. We also offer a report and fillings database specifically for the development of RAG, which has all reports published since IPO in the form of PDFs, such as annual reports, quarterly reports and sustainability reports. This gives your portfolio managers or users of your fintech app access to constantly new data. Talk to us to find out more: https://calendly.com/ramtin-babaei/bavest-demo-de