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LibSearch | Empowering Intranet using Generative AI and Retrieval Augmented Generation

Bastien Chappis
Published on
RAG extends the already powerful capabilities of LLMs to specific domains or an organization's internal knowledge base, all without the need to retrain the model. It is a cost-effective approach to improving LLM output so it remains relevant, accurate, and useful in various contexts.

Enhancing intranet research with Retrieval-Augmented Generation (RAG)

  • Cost Efficiency: Retraining a model often requires significant human and machine resources. However, RAG presents an efficient alternative by incorporating domain knowledge into the Language Model, thus eliminating the need for retraining.
  • Boosting Trust and Confidence: RAG empowers the Language Model to provide accurate information complete with source attribution. This feature allows the output to include references to source materials, enabling users to independently verify the information or seek further clarification if needed.
  • Access to up-to-date Information: RAG has the ability to connect to various information sources, including the open web, live social media feeds, or regularly updated data sources, ensuring the provision of the most current information.
  • Control and Privacy: RAG gives developers significant control over the information provided to the Language Model. They can restrict access to sensitive information based on different authorization levels, thereby ensuring the generation of appropriate responses while maintaining privacy and data security.

How does it work?

Foundational Generative AI models excel in crafting text responses derived from extensive language models (LLMs). These LLMs are trained using a vast array of data points, but the information utilized to produce these responses is restricted to the training data, which usually consists of a generic LLM. The data in the LLM might be outdated by weeks, months, or even years. Furthermore, it may not encompass specific details about a company's products or services when used in a corporate AI chatbot. This limitation can undermine trust in the technology among customers or employees, making it challenging to directly implement within the organization.

RAG allows to bypass the limitations of foundational LLMs by referencing an authoritative knowledge base outside of its training data sources before generating a response, hence optimizing the output. So how does it actually work? 

RAG infuses the LLM with precise, up-to-date information without modifying the core architecture of the model. This infusion of targeted data ensures that the information is highly relevant to a specific organization or industry as well as guaranteeing that the AI's responses are rooted in the latest knowledge available. As a result, the model can deliver responses that are not only contextually accurate but also informed by the most current insights.

Create a knowledge library as a vector store
Organization’s intranet contains a diverse array of information assets, including structured data in databases, unstructured documents such as PDFs, blog posts, news articles, and transcripts from previous customer service interactions. This extensive and ever-evolving collection of data is converted into a standardized format and compiled into a centralized repository known as a knowledge library.

To facilitate the AI's understanding and utilization of this data, the contents of the knowledge library are transformed into numerical form through the application of a sophisticated algorithm known as an embedded language model. These numerical representations, or embeddings, are then stored within a vector database designed to be readily accessible to the generative AI, enabling it to draw upon a wealth of information.

Information retrieval

User query is converted into the same kind of vector and used for relevancy search. If an employee searches “What is a retrieval augmented generation framework” the system will retrieve this specific article alongside other technical documentations. All these documents will be returned because they are highly relevant to what the user has asked initially.

Augment the LLM prompt

The RAG model employs the technique of prompt engineering to integrate the user's question and the relevant retrieved document into a single prompt. This amalgamated prompt is then conveyed to the Large Language Model (LLM). By doing so, the enhanced prompt empowers the Large Language Model to generate precise responses to user queries.

How can fifty-five support your Retrieval-Augmented Generation requirements?

As a leading consulting firm, fifty-five offer a comprehensive range of services aimed at helping you maximize the potential of generative AI services. These services include:

  • Defining the technical infrastructure with regard to your needs
  • Assisting you in transforming your intranet data into a functional vector store,
  • Determining the model that best aligns with your needs in terms of privacy and efficiency,
  • Designing a user interface or seamlessly integrating it with your existing interfaces,
  • Collecting user feedback and tracking usage over time.

We are dedicated to providing support for organizations keen on developing their own bespoke generative AI solutions. We are committed to accelerating your RAG implementation process, enabling you to reap the benefits of this advanced technology more swiftly.

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