In today's fast-evolving AI landscape, companies and managers must fine-tune existing Large Language Models (LLMs) on their data or explore new alternatives like Retrieval Augmented Generation (RAG). While fine-tuning promises customization, it brings with it some significant challenges. As the market becomes more educated, the limitations of fine-tuning are becoming apparent, leading to a growing interest in RAG AI models.
Fine-tuning large language models (LLMs) on company-specific data is a possible solution to improve the accuracy and relevancy of the response.
However, this approach has several drawbacks, as you will see below:
Data Security Risks: The Fine-tuning process exposes company data to third-party platforms, raising concerns about privacy and security.
High Costs: The process is expensive, and requires significant computational resources and expertise, which is almost impossible to justify for the vast majority of businesses.
Rapidly Changing Information: highly dependent on the model’s ability to stay up-to-date. Today’s fast-paced information environment can render the model less effective.
A recent Salesforce report highlights these concerns, with 75% of workers expressing distrust in AI-generated data and 56% failing to get desired results from AI tools. This lack of trust is often due to the inherent limitations and risks of fine-tuning LLMs.
RAG is emerging as a powerful alternative that addresses many of the shortcomings of fine-tuning highlighted above. RAG enhances the generative capabilities of LLMs with a sophisticated information retrieval system, resulting in several key advantages:
Dynamic Information Retrieval
RAG ML models leverage LLMs to search and retrieve information from sources beyond their initial training data. This ensures the responses are based on the most recent and relevant data sources.
User-Specific Ranking
The system ranks the retrieved information using customized signals such as data recency, platform specifics, and user roles to rank the retrieved information, ensuring high relevance and accuracy.
No Training on User Data
Unlike fine-tuning, RAG does not require training on company data. The data is indexed and retrieved on demand; this ensures relevant responses from the most up-to-date data available.
One of RAG’s most compelling use cases is in sales enablement software. Sales teams often need help finding up-to-date, relevant information about specific products using out-of-the-box LLMs. Traditional models must provide unique answers tailored to the company's specific knowledge base.
RAG ML solutions, such as Unleash, revolutionize this process. By indexing all company documents, slides, and other resources, Unleash RAG AI models can provide precise answers with references, significantly boosting trust and accuracy. This makes RAG an indispensable tool for sales enablement, offering quick access to detailed product information and training materials.
Security is a paramount concern for enterprises adopting AI solutions. Unleash addresses this by ensuring that company-specific data is not used for model training. Instead, each customer's data is stored in an isolated, encrypted index. The retrieval process is secure, and answers are generated solely from the company's knowledge base, ensuring that the company's secrets remain confidential.
The Unleash AI integrates seamlessly with identity providers like Azure Active Directory, Okta, and Firebase, preserving existing permission controls. This minimizes the administrative burden on IT personnel and leverages existing security infrastructure, making deployment straightforward and secure.
Incorporating AI into your company's operations doesn't have to involve the risks of fine-tuning LLMs on sensitive data. RAG presents a viable and secure alternative, allowing companies to harness the power of AI without compromising data integrity. By leveraging advanced retrieval mechanisms, RAG ensures that AI solutions remain accurate, relevant, and trustworthy, making it a preferred choice for enterprise AI search and sales enablement tools.
