Retrieval-Augmented Generation (RAG): The knowledge booster for LLMs

Table of contents

About this guide

Retrieval-Augmented Generation or RAG for short — ever heard of it somewhere? If not, that's okay, because regardless of whether you've heard the term before or are new to AI: In this guide, we not only explain what retrieval augmented generation is all about, but also how it actually works and what the advantages and limits are. After this guide, all questions should be answered and it should be clear why RAG is such an important milestone in the use of LLMs.

Definition: What is retrieval augmented generation (RAG)

Retrieval-Augmented Generation (RAG) is an artificial intelligence approach that combines large language models (LLMs) with external knowledge. In the case of AI chatbots This means that the chatbot can provide users with both more precise and up-to-date answers and at the same time access important product data so that appropriate information is provided for the context. In contrast to traditional AI systems, which rely exclusively on their internal training, RAG uses a type of “memory” that can retrieve additional information in real time. It can therefore be described as an addition or add-on to LLMs to expand the knowledge base, i.e. the knowledge base.

This “extra” data can include almost anything from internal company information to personal data. Retrieval-augmented generation is particularly useful in areas that require precise or up-to-date information, such as AI chatbots in customer care, in Product advice or in science. So can False statements and knowledge gaps are reduced.

What are Large Language Models (LLMs)?

But what exactly does retrieval augmented generation actually improve? An LLM or in German: Large language models form a sub-category of AI models and are specifically trained to understand and generate human language. This means that LLMs can understand complex questions or texts and generate answers based on them using correct grammar and spelling or, for example, in a programming language. Well-known LLMs include the “GPT family” models from OpenAI, Mistral or Google Gemini, which can now process images, audio and even videos in addition to speech.

The problem:

Even though these AI language models (LLMs) may be pretty clever, but they're not infallible. For example, they can invent (hallucinate) false information, their knowledge is often out of date and there is no specific context for certain topics.

Retrieval-Augmented Generation provides a remedy here by connecting LLMs with searchable, external knowledge databases. Instead of “I don't know for sure” or “My current information is enough until October 2023,” it's now “I'll look it up quickly.” This makes answers more context-related, customer-specific, up-to-date and, above all, more reliable.

How does RAG work? The 4 steps

Retrieval-Augmented Generation therefore combines the power of a large language model with external sources of knowledge. To ensure that this works smoothly, the following four steps are usually followed:

1. Data preparation (cleaning & chunking):

  • The first step involves “cleaning” the external data to which the LLM should have access in the future. For example, this removes unnecessary symbols such as logos or emojis so that only the actual text remains.
  • The pure text is then divided into small pieces of text (so-called chunks). In RAG, these chunks become searchable “building blocks” that make it possible to find the right information quickly and easily.

2. Search for relevant information

As soon as the preparation of the data is complete, a search system (retrieval system) is set up, which is specifically designed to search the prepared chunks quickly and precisely. So when a request (e.g. “How long does the battery last”) comes, RAG searches the chunks using the retrieval system. There are several search methods for this, and two are presented below, which complement each other perfectly:

  • Semantic search (dense retrieval): The chunks and the request itself are converted into mathematical vectors that represent their meaning. This allows the model not only to search for the exact words of the request, but also to understand the meaning and content behind the request. Example: With “battery life,” the system also finds chunks that say “battery life” — different words, the same meaning.
  • Keyword search (lexical retrieval): In addition, important keywords are searched for in order to increase the hit rate. In contrast to semantic search, which takes into account the meaning behind the words, the keyword search only looks at whether the exact terms from the query appear literally in the data. So when you ask for “battery life,” the word is explicitly searched for.

At the end, RAG selects the best chunks and passes them on to the language model.

3. Fine-tune the data

After potentially suitable chunks have been found, the content is further processed:

  • Summarize & reformat: Long texts are (usually) shortened and restructured by a separate AI model, which has been specially trained for this purpose. In this way, the most important information is filtered out and made easier to understand.

4. Generate the answer

Now all important information has been collected and processed and it's time for the final answer:

  • Contextualized prompt: The selected chunks are built into the LLM prompt. In this way, the AI model has the right background information and can generate a precise, fact-based answer.

In-context learning: Thanks to in-context learning, the LLM can directly use the chunks and learn from them to provide context-appropriate answers. The practical thing about it: The model doesn't have to be retrained, but works with the information that is currently available.

What are prompts anyway?

Prompts are inputs that are used to get large language models (LLMs) to generate specific answers or content. They consist of instructions, questions, or text fragments that control the model by providing the context and the desired direction of the answer. Well-formulated prompts can significantly improve the quality and precision of answers, as they provide the model with clear framework conditions.

RAG: Beyond chunks

The steps described above show the classic retrieval augmented generation process, but RAG is not limited to predefined chunks. The main goal of RAG is to provide LLMs with additional knowledge; semantic searches on chunks of information are just one approach. There are other approaches to connect LLMs with other data sources and structures, such as:

  • databases
  • Recommendation systems
  • Search APIs

Relevance: Why is RAG so important right now?

Retrieval-augmented generation is becoming increasingly important in the AI world. Although LLMs are trained on huge data sets, the models face certain challenges:

  • Hallucinations: LLMs can produce false information.
  • Knowledge cutoff: LLMs only have access to data up to a specific point in time and are not up to date.
  • Specialized topics: LLMs find it difficult with fields such as medicine or law because they are based on general data.

RAG helps solve these problems by giving LLMs access to external sources of information. This makes them more accurate, reliable and adaptable.

RAG (Retrieval-Augmented Generation) is actually a crucial step towards taking LLMs' capabilities to a new level. The integration of external, up-to-date, and reliable sources of information enables models to deliver more accurate and trustworthy content. Without this ability, the models remain limited to their static training data, which limits their usefulness in many practical applications.

Benefits and limits of RAG

In contrast to LLMs, which are based exclusively on their own, pre-trained knowledge, retrieval-augmented generation offers several important advantages:

  • Fewer hallucinations: RAG helps to avoid hallucinations — i.e. generating false or made-up information. Because: With retrieval augmented generation, the models have access to reliable external sources, which makes the answers more accurate and reliable and strengthens user trust.
  • Latest information: LLMs have a knowledge limit based on the data they were trained with. That means they're not always up to date. RAG solves this problem by giving LLMs access to up-to-date data from external sources such as live data feeds, databases, or APIs. This allows LLMs to always provide the latest information, even on fast-moving topics.
  • Specialization in subject areas: With retrieval augmented generation, LLMs can be specifically adapted for specific fields, such as medicine or law. By connecting an LLM to a specialized knowledge database, for example, it can respond precisely to technical questions without having to extensively retrain the entire model. This saves time and money.
  • Better data security: Training LLMs with sensitive data involves a certain risk of data leaks. RAG offers a more secure solution by keeping sensitive data external and not storing it in the LLM itself. This minimizes the risk of data breaches and at the same time allows LLMs to be used with confidential data without jeopardizing privacy.
  • Easy to implement: Compared to other methods such as completely retraining an LLM, retrieval augmented generation is easy to implement. In this context, however, “simple” means that IT skills and knowledge are still needed, as there are still many challenges that need to be overcome. For this reason, “ready to go” solutions, such as moinAI, are recommended. This is where AI experts take care of the RAG solutions. Because even at RAG, the following applies: RAG ≠ RAG, there are good and bad solutions.

The limits of RAG: Where technology (still) reaches its limits

Of course, there are also some limits of retrieval-augmented generation that should not be ignored:

  • Quality of retrieval systems: RAG's success depends heavily on the quality of the search system, which retrieves relevant information from external sources. If this system works poorly, it can produce incorrect or irrelevant results.
  • Prompt design: It can be difficult to create the right prompts that help the LLM make correct use of the information retrieved. A poorly formulated prompt could cause the model to either ignore or misunderstand the information, reducing the usefulness of RAG.
  • Calculation costs: Even though RAG is less resource-intensive than retraining an entire model, other resources may still be required — especially when using large knowledge databases or complex systems.
  • Difficulties in evaluating performance: Evaluating the performance of a RAG system is complicated because both data retrieval and generation must be evaluated.
  • Bias and Fairness: The external sources used in RAG can lead to bias and fairness problems in the LLM's answers. It is therefore important to choose these sources carefully to prevent the model from working with biased or harmful information.

What is the role of LLMs in RAG?

LLMs are very important for retrieval-augmented generation and form the core, so to speak. Because while RAG draws on external sources of knowledge to improve the accuracy and reliability of answers, it is the fundamental capabilities of LLMs that make the entire process possible in the first place.
Ideally, one or even several different LLMs are used in several phases of the RAG process; for example, they can also improve the selection of relevant knowledge and not just generate the final answer:

  1. In-context learning: LLMs can directly process additional information from external sources. Combined with the user request, this results in a well-founded and fact-based answer.
  2. Text generation: Since LLMs were developed specifically for text generation, they are perfect for final output in RAG applications.
  3. Adaptability: Whether answering facts or writing creative stories, a single LLM can be used in many RAG scenarios.
  4. Prompts: Although RAG relies on external sources of knowledge, success depends on how well the LLM understands and uses the context provided. With well-thought-out prompts, developers can optimize RAG's performance and get the most out of external data.

RAG in customer service

Retrieval-Augmented Generation makes customer service significantly smarter, as it connects language models with external sources of knowledge. As a result, inquiries can be answered much faster and more precisely, because the retrieval-augmented generation works in real time on data such as faqs or access product information. It also allows more personalized answers to be delivered as customer history can be taken into account.

Another major advantage: RAG is ready to use around the clock and can even offer proactive help, for example through automatic recommendations following a purchase. It is important that knowledge databases remain well organized and up to date. And even though many things happen automatically, human control remains important in order to be able to intervene with more complex inquiries — for example via a Live chat, where a support agent can help directly when needed.

Good to know: moinAI also relies on RAG

Of course, moinAI also uses RAG-based solutions to make AI chatbots of high quality, with little effort and more scalable and flexible. moinAI customers benefit from using generative AI and LLMs in the chatbot and still being able to connect their internal knowledge and corresponding databases — in order to provide users with precise and context-relevant answers.

Conclusion: Knowledge on demand — RAG makes LLMs smarter

Retrieval-Augmented Generation or RAG is an exciting development that enables large language models to access external sources of knowledge and thus provide more precise, up-to-date and trustworthy answers. By combining semantic and keyword search, RAG becomes a real game changer that not only reduces hallucinations but also covers specialized areas of expertise. Whether in customer service, in research or in other areas — RAG's potential applications are enormous and offer a lot of potential for the future.

Learn more about how moinAI uses RAG and how AI solutions can optimally support your individual use case. Get to know moinAI without obligation.

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