What are Private AI Systems?

A Private AI system is an artificial intelligence model deployed on local servers or secure cloud environments, fully owned and managed by the business itself.

Unlike open-access AI chatbots that store every prompt on external servers—often linked to user details like email, IP addresses, or location—private AI systems give you total control over your data. This eliminates the risk of employees accidentally sharing sensitive company information on third-party platforms. Additionally, for industries like healthcare, finance, and law, private AI ensures compliance with regulations such as GDPR, HIPAA, and others that govern data storage and processing.

The Benefits of Small Language Models

Now, one might worry that the computational power required for large language models could strain your IT infrastructure. Fortunately, recent advancements in small language models have made these concerns unnecessary.

Small language models require significantly fewer resources and can be fine-tuned for specific tasks.

In a business setting, AI chatbots often serve a specific purpose such as customer support or employee training. In these scenarios, the broad knowledge base of a large language model is not always necessary. A smaller model, trained on a more focused dataset could perform equally well. Importantly, these small language models are more efficient, consuming less energy and computational power.

Intriguingly, it is often found that, in such specialized applications, small models outperform general-purpose large language models. By deploying these smaller, purpose-built models locally, businesses gain full control over both user input and the model’s output, ensuring it is precisely aligned with specific business objectives.

Leveraging Retrieval-Augmented Generation (RAG)

While Private AI offers control and security, it can still face a challenge: how do you ensure the system has the latest, most relevant information to respond? This is where Retrieval-Augmented Generation (RAG) comes in.

RAG combines the strengths of traditional retrieval systems with generative AI models. Instead of depending solely on pre-trained knowledge, a RAG system dynamically retrieves relevant documents or data from internal knowledge bases to generate responses.

By incorporating a retriever into the language model, RAG eliminates the need for constant retraining when new information becomes available, offering a much more time- and resource-efficient approach.

Furthermore, because RAG systems draw from tailored, reliable knowledge sources, they are less likely to produce factual errors or hallucinations than general-purpose models. In addition, a RAG system can point to the source where they found the information, making it easy to verify the accuracy of the output.

Why Implement Small Language Models and RAG for Private AI?

In sum, combining small language models with RAG systems offers a robust solution for developing private AI chatbots that are secure, customizable, and efficient.

• Regain Control:
  • Running the chatbot locally provides full control over user data.
  • Small language models allow to customize model training.
• Enhanced Performance:
  • Small, customized language models outperform general-purpose large models.
  • RAG systems minimize factual errors by retrieving data from a focused knowledge base.
• Cost Efficiency:
  • Local systems avoid the recurring API costs from providers like OpenAI.
  • Small language models are more computationally- and energy-efficient.
  • RAG systems reduce the need for constant retraining, saving both time and computing resources.