The launch of OpenAI’s ChatGPT opened up endless possibilities of AI applications for businesses.
Yet, even as business leaders are excited about the prospects of applying AI to increase productivity, successful AI implementation remains challenging for a few key reasons.
This article discusses the key challenges with LLMs, why RAG is a promising solution, and explains how to get started quickly with RAG-as-a-service solutions.
The technology behind ChatGPT and other similar models is Large Language Model (LLM). Fundamentally, LLMs work by predicting the next most probable word, based on the data that the model was trained on.
There are a few key issues with this mechanism that pose challenges to wide spread adoption of LLM in businesses.
First of all, LLMs are not deterministic — they may be able to produce plausible responses that look a lot like the real thing, but they do not have an intrinsic understanding of what is correct or incorrect, beyond answers that are explicitly contained in their training data.
This could spell disaster for businesses attempting to apply LLMs in what, at first glance, seem like obvious use cases: from AI-enabled customer support chatbots promising customers invalid discounts, to AI legal researcher generating nonexistent cases, the real-world, high-profile incidents all come to highlight the dangers associated with overly relying on LLMs.
Second, LLMs are ignorant outside of their training data. An LLM trained on publicly available data up till the end of 2024 would not know anything about the latest financial data, critical updates to policies or laws, or medical breakthroughs published in private data not available to the LLM.
This poses limitations for businesses wanting to leverage LLMs to research or analyze the latest information, or needing LLMs to provide customized outputs specific to the business’s private knowledge or proprietary data.
Finally, if a business wishes to customize an LLM to their private knowledge and needs by creating a private LLM or retraining an LLM, it can be looking at millions of dollars in up-front investment, effectively pricing out most small-to-medium sized businesses. As a reference point, according to industry reports, OpenAI’s model-building service starts at $2-3 million.
Moreover, for businesses that require constantly incorporating new information in their LLM, the associated costs would compound over time, as a complete retraining of the LLM would be required every time new information needs to be incorporated.
RAG, short for retrieval-augmented generation, is a technique that enhances the accuracy and reliability of LLMs by adding a “retrieval” step where AI actively fetches information from specific and relevant data sources before generating a response.
This approach separates the concerns of information management from the language model capabilities, allowing organizations to leverage pre-existing, commercially available LLMs, but augment them with relevant knowledge, to produce a much more affordable, more accurate, and more reliable AI solution.
There are a few key features of a RAG-enabled AI system that make it especially suited for business applications.
RAG combines retrieval and generation to ground AI generated responses in relevant knowledge. RAG enabled systems have been shown to boost relevance and accuracy in their responses compared with other LLMs.
RAG technology opens up the ability for AI to get access to specialized, private, or real-time data through retrieving from a knowledge base, without needing to include that data within the training data.
The RAG method allows businesses to sidestep the cost and complexity of finetuning, retraining, or building private models. It also makes access to real-time data much more affordable and feasible.
Knowledge can also be easily updated with a RAG implementation, simply by updating the knowledge base or swapping data sources. This would be a much more costly and complex exercise in traditional LLM approaches, requiring retraining or further finetuning the model.
Moreover, with RAG, outdated knowledge or data can be confidently excluded from AI outputs simply by removing the old data from the knowledge base. In contrast, asking LLMs to “unlearn” a memorized data within their training dataset is a much more difficult process with uncertain success.
RAG-as-a-service represents a significant evolution in how businesses can access and implement artificial intelligence capabilities without extensive technical expertise or infrastructure investment.
RAG as a service is a cloud-based service model that provides businesses with access to RAG technology through managed platforms, eliminating the need for organizations to build and maintain their own RAG infrastructure. This approach delivers the powerful capabilities of RAG through a comprehensive solution that handles the entire process from data chunking to query response generation, all accessible via standardized APIs and user interfaces that can be easily managed even by a non-technical team.
RAG as a Service dramatically reduces the time-to-value for AI implementations by eliminating many of the complex technical steps typically required. Rather than spending months on infrastructure setup, organizations can begin implementing AI POCs and solutions instantly through a RAG-as-a-Service platform like Ragdoll AI.
RAG as a Service also enables organizations that may lack specialized AI expertise to leverage advanced AI capabilities and implement AI solutions through user interfaces that can be accessed by non-technical team members.
A service model to RAG allows organizations to leverage RAG technology while skipping significant upfront investment in infrastructure. Instead, businesses can effortlessly adjust their AI capabilities based on fluctuating demands. With RAG-as-a-service platforms like Ragdoll AI, companies can start with minimal costs and scale up based on their needs.
Finally, RAG-as-a-service platforms provide ready made data connections and integration capabilities that facilitate straightforward connection with other existing business systems and applications.
Interested in using RAG for your business? Here’s how you can easily get started with Ragdoll AI:
Based on your use case, what knowledge and data sources would be relevant? For example, if you are planning to build a chatbot that answers questions related to shipping and refund policies and product inquiries, you may want to prepare the FAQs, product information and company policy.
You should think about how to test and verify the outputs of your RAG-enabled AI. For example, you may want to prepare a list of commonly asked questions from customers and test it against your RAG knowledge base.
Upload or connect the relevant data, and start indexing to build your RAG knowledge base, then chat with it to verify the validity of its underlying data. If you spot anything that seems outdated or inaccurate, simply review and update the underlying data in the knowledge base.
Once you are satisfied with the responses, you can deploy your RAG knowledge base to be used by your AI applications or LLMs, or directly deploy your RAG-enabled chatbot using API keys.
Unlocking the potential of AI in business application has never been easier. Get started with Ragdoll AI for free.
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