How to increase LLM trustworthiness through retrieval-augmented generation and guardrails

silogen logo and head of technology aarne talman

In today's information-rich world, searching for relevant content can be a challenging task. Although Large Language Models (LLMs) can be useful, they can also be unreliable. While LLMs can generate text closely resembling human writing, they struggle to retrieve precise information, meaning they cannot be used as standalone search tools. With SiloGen’s architecture for Retrieval-Augmented Generation (RAG), guardrails and controls, users can restrict the domain in which the LLM functions to effectively manage the content it generates.

By defining the specific contexts in which the LLM operates through RAG, you can ensure the LLM only provides information on topics it is knowledgeable about. This allows users to ask questions and receive responses that are generated from content retrieved by the system, providing a human-like conversational experience that makes it easier to find the information you need. While RAG does not fully eliminate the so-called hallucinations of a LLM it can be used in combination with guardrails and other technical solutions to effectively manage the content it generates. Guardrails help to limit and control the LLM, ensuring that its output is guaranteed reliable behavior.

RAG is a technique that can be used to improve reliability by combining a vector similarity search with a LLM. This approach can create an intelligent conversational search solution, connect to various knowledge bases or document storage, and serve as the foundation for co-pilot solutions. Another reason for implementing RAG is to enable LLM with access to information that has not been available during model training or fine-tuning. RAG can also be used to provide the model with more up-to-date information without the need for re-training or fine-tuning. Combining LLM-based conversational search with RAG can make the search process faster, more efficient, and more trustworthy when it comes to finding relevant information, as current foundation models tend to be unreliable.

How to integrate RAG into LLMs

Integrating RAG into LLMs requires a vector database where documents are stored and indexed as numerical vector representations. The documents are then segmented into appropriately sized chunks that represent their semantic content at the document, section, or paragraph levels. Once segmented, you need to create a vector embedding representation for each chunk to capture its semantic content. These embeddings are then inserted into the vector database along with their respective chunks. To retrieve information from the database, you need to transform a query or input text into an embedding, which is then compared to the embeddings in the vector database to retrieve the most similar chunks. 

To augment the final query to the LLM with semantically relevant information from a vector database, you need a component that can analyze the request, query the vector database for related content, and augment the final prompt that is sent to the LLM. The LLM then receives the augmented prompt and generates a response that takes into account the information provided.

If the source collection's documents change, the vector database needs to be refreshed accordingly. This can be done on a schedule or on demand as content changes.

Overcoming challenges in designing RAG-based LLM solutions: tips and strategies

When building LLM solutions powered by RAG, developers have various challenges to overcome. One such challenge is to determine the appropriate size of text chunks. Additionally, selecting the right embedding model to create the embeddings is crucial. Factors to consider when choosing an embedding model include whether the content is multilingual and the size of the search queries compared to the indexed content.

Another challenge is deciding between fine-tuning the model and integrating RAG while ensuring a balance between cost and quality requirements. Adding more context to the prompts can increase their size, which in turn increases the cost. Therefore, evaluating whether fine-tuning a model would be more efficient for the use case is important. Although the fine-tuning process can be time-consuming, it can pay off in the long run, as prompts and token usage can be smaller, resulting in lower costs.

Using RAG architectures can help us create intelligent conversational search solutions that are more dependable. It is important to note, however, that RAG is not a silver bullet answering all of our needs. Developing a successful search solution requires adapting the LLM to the context and having expertise in the field.

RAG-powered LLMs in the real world

SiloGen's specialized offering, combining finetuned models with RAG, controls and other post-processing, can be further trained and finetuned to create a competitive edge for various clients in different sectors. SiloGen's LLMs have successfully provided documentation support, resulting in, for example, efficient and accurate customer service and healthcare. 

For instance, in collaboration with Tietoevry Care, SiloGen is building a co-pilot for healthcare professionals. The LLM-based search and discussion tool provides effective support for medical professionals to search and explore care guidelines and patient data. This results in freeing up medical staff's time for patients, improving treatment outcomes, and saving costs for society. Another practical use case has been conversational enterprise search, integrating specialized LLM co-pilots with support documentation and knowledge bases indexed into a vector database. 

SiloGen's customer support focused field services LLM has become increasingly popular as an AI-powered co-pilot in the industrial setting. In this use case, its primary function is to assist field service technicians by retrieving information from service manuals and other data sources, streamlining the information-seeking process and making their tasks easier. RAG in conjunction with the co-pilot LLM and SiloGen’s application can be easily adapted to various use cases. The model and development platform allows users to further customize LLMs and RAG solutions to suit their industry-, and use case-specific needs

In conclusion, RAG-powered LLMs have great potential to revolutionize various industries. It's important to understand their limitations. While the current generation of LLMs still suffers from hallucinations and factual errors, most practical LLM implementations will likely depend on some kind of a knowledge base and a retrieval system that complements the LLM-generated content.

Key takeaways: 

  • LLMs by themselves are not always reliable and they still suffer from hallucinations and factual errors.
  • RAG enables domain restriction and context definition for LLMs, increasing the probability that they provide information only on topics they are knowledgeable about.
  • RAG-powered LLMs enable a human-like conversational experience that makes it easier to find the needed information from your documents. 
  • With SiloGen’s architecture for RAG, guardrails and controls, users can restrict the domain in which the LLM functions to effectively manage the content it generates.
  • Combining LLM-based conversational search with RAG makes document search faster, more efficient, and more reliable.
  • RAG-powered LLMs are not a silver bullet answering all of our needs and it’s important to understand their limitations. Developing a successful search solution requires adapting the LLM to the context and having expertise in the field.


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Pertti Hannelin
VP, Business Development
+358 40 063 8810
Aarne Talman, PhD
Head of Technology

Aarne is an AI leader and researcher with over 16 years of experience in research, software engineering, management consulting and leadership. He currently works on generative AI, large language models and language understanding. At SiloGen, Aarne leads technology and works closely with clients to help them harness the power of LLMs and GenAI.

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