An exciting development in the rapidly accelerating AI ecosystem is what is known as Retrieval-Augmented Generation.

TL;DR

RAG combines capabilities similar to search engines, i.e. retrieval of information, with the language model generative capabilities resulting in a more context specific and specialized agent.

Scout’s integration of RAG allows for the creation of agents that not only speed up your company workflows but can be customized to fit the unique use cases of your business.

Characteristics of RAG

RAG shows significant promise in the employment of models for daily workflows. Rather than having to train models on data and continue to ‘re-train’ the models for updated information, RAG takes the ‘dynamic’ approach that updates the model as it learns.

Below is our list of unique characteristics of RAG

Enhanced Information Access & Quality of Responses

• Retrieval: RAG’s can access as vast amount of external information that you would like so as to be able to retrieve relevant information. This is crucial for an AI agent to both gather and provide accurate and up-to-date information.
• Generative: After retrieving relevant information, the model can then access that information to generate responses. This allows for more informed, accurate, and contextually relevant responses than your typical stand alone agents.

Dynamic & Up-to-Date Knowledge

Using retrieval sources, the model is accessing the latest information available. This is critical for topics, domains, etc that are constantly and consistently evolving and ensures that the agent remains relevant.

Breadth & Depth of Knowledge

The knowledge gained via the retrieval process (breadth) combined with the contextualization that generative models are known for (depth) leads to a deep understanding of information. This allows the agent to provide nuanced and comprehensive responses.

Customization & Specialization

The beauty of the RAG method is that you can tailor the retrieval sources, ie customize the retrieval sources, allowing for AI agents to be highly specialized with specific types of information. Think technical documentation, healthcare, and research related fields. (Hello AI librarian! Can you imagine walking in and having instant access to all of a libraries information through an agent for research?!)

Reducing Bias and Errors

One of the known issues with current models are bias of information and therefore responses. However, by curating retrieval sources, RAG-enabled models have the ability to reduce some of the biases and errors that bubbles up in training data of standalone generative models.

Efficiency and Scalability

RAG also offers a highly scalable solution for knowledge-intensive tasks. Instead of requiring a generative model to learn and store relevant information, RAG models can leverage multiple external sources (think databases), therefore making them more efficient in handling vast datasets. Instead of storing all the information you want through training data, reference sources that already exist.

Application of RAG with Scout

We believe RAG is a significant step toward that future many envisioned for AI. But is RAG approachable? Creating RAG based applications from the ground up can involve a significant amount of work and overhead. The complexity lies in synchronizing your data sources into a vector database, performing nuanced retrieval over that database, and efficiently maintaining session state. Additionally, managing token limits, logging, and monitoring are essential tasks that contribute to the complexity.

Scout’s approach is all-together different. Rather than create one and maintain one ‘in-house’, offload the work of creating agents to someone else, and let Scout handle all of that.

Think of Scout as ‘serverless’ AI agents. But Scout is not limited in it’s model implementation. Really the ‘limit’ is what you want to do with Scout. How much information and retrieval sources do you want?

All you need to be successful with Scout is tell your agent what you want to use as your reference material. Scout handles the rest. You can leave it as high level or as ‘in the weeds’ as you want it.

Scout is currently in beta, join the waitlist here.