Building Customer Service Rag Applications

In the age of AI, ensuring accurate information from Large Language Models (LLMs) is critical, especially when the trust of the customer is on the line. When AI-generated responses are used to answer queries, even a single misstep can lead to a loss of confidence in your business. That’s why it’s crucial to minimize hallucinations in your AI applications.

The most effective approach to achieve this is through Retrieval-Augmented Generation (RAG) systems. These systems can help ensure that LLMs stay on track by providing context and accurate reference points, reducing the risk of generating misleading or false information. Even if you have trained a private model on your date, RAG will still be required for the most optimal results. Why you may ask? Mainly when adding new data to the knowledge base, retraining the SLM every time new data is added incurs cost and takes time. When you have an existing RAG pipeline, new data can be added to your knowledge base and is immediately in production.

But how can you make sure your system doesn’t hallucinate when deploying a new AI application? Here are a few strategies to consider:

  1. Prepare Your Data

    The foundation of a successful RAG system is properly formatted data. When working with various document types, such as PDFs, Word documents, and websites, it’s essential to extract structured information that is compatible with LLMs. This includes:

  • Extracting headings and body text that correlate with each other.
  • Capturing metadata such as page numbers, titles, and other relevant details.

Proper data formatting ensures that the language model has a clear context to draw from, reducing the likelihood of generating incorrect information.

  1. Use Context to Guide Responses
    RAG systems work by retrieving relevant information from a knowledge base before generating a response. This step ensures that the LLM has accurate and up-to-date information to reference. Consider setting up a robust retrieval system that can access a wide range of data sources, ensuring that the information provided to customers is reliable.
  2. Regularly Update Your Knowledge Base
    As your business evolves, so does the information that your AI system needs to know. Make it a priority to update your knowledge base regularly, incorporating new data and removing outdated information. This proactive approach helps maintain accuracy and minimizes the risk of hallucinations.
  3. Build the Rag System                                                                                                                                                                                                Start by leveraging pre-processed datasets to establish a robust knowledge base. Selecting an embedding model and database is crucial for this task. Among the widely favored options are vector databases such as MongoDB Atlas, Datastax Astra, and Pinecone. Given the continuous evolution of embedding and language models, it’s advisable to opt for the top-performing model available at the time of implementation. Simplifying this process, Dataworkz offers a user-friendly interface with a seamless point-and-click dropdown menu, facilitating effortless transitions between embedding models and language models.


If you are looking to create a RAG application for your business, check out the blog for building a RAG system in minutes with Dataworkz: Building a Production RAG App in minutes

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