All things AI

Many Organizations are quite eager to adopt the latest AI advancements, skipping traditional machine learning (ML) solutions and diving straight into generative AI (GenAI), especially with large language models (LLMs). The attraction to LLMs is clear: their ability to handle data analysis, generate content for marketing and documentation, and even tackle customer-facing applications like support is unprecedented. However, adopting these powerful tools comes with unique challenges, particularly when it comes to controlling and validating the output of these expansive models.

The “Paradox” of Large Language Models

The core paradox of LLMs is that, for them to perform well, they must be trained on vast amounts of data. While this training equips the models with extensive knowledge, it also makes it harder to curate and control the data, leaving model owners with less insight into the origins and reliability of the responses generated. This lack of control becomes a significant challenge when organizations rely on LLMs in high-stakes applications, such as representing the company to real customers or producing content that must strictly align with organizational policies and regulatory standards.

The Limits of Retrieval-Augmented Generation (RAG)

One common approach to increase the relevance of LLM responses is Retrieval-Augmented Generation (RAG), which integrates external data sources to focus the model’s output on specific, relevant information. While RAG is widely used and beneficial in narrowing the LLM’s focus, it does not inherently validate or ground the final answer. The LLM can still fall back on its extensive, often uncontrolled training data, leading to hallucinations or unintended references to unknown sources; a risk when information accuracy and trustworthiness are crucial.

Building a Controlled Semantic Space with Embeddings

To mitigate these risks, organizations can employ an embedding model as part of their LLM ecosystem. By training an embedding model on the core, validated knowledge of the organization, companies can create a controlled semantic space. This model, carefully designed and validated for classification accuracy and bias mitigation, acts as the source of truth, grounding the LLM’s outputs in approved data.

Embedding models are often a natural part of a RAG configuration and, when combined with knowledge graphs and security controls, they provide a secure layer of data governance. When used throughout the data pipeline, the embedding model can be utilized to cross-validates the LLM’s responses by aligning them with the semantic space of the retrieved data, enhancing both accuracy grounding and language restriction.

Ensuring Validity and Bridling LLM Outputs

Although direct validation of LLMs remains challenging, embedding models help provide a robust check on output. By analyzing LLM responses within a controlled semantic space, organizations can ensure that generated content aligns with internal knowledge and compliance standards. This approach effectively “bridles” the LLM, controlling its outputs and ensuring that even the most powerful generative AI systems stay aligned with organizational values and policies.

In a world of expanding AI possibilities, this approach to LLM governance offers a way to harness the power of generative models while maintaining control over what ultimately reaches end-users, customers, and stakeholders.

At CitrusX.ai, we are at the forefront of responsible and trustworthy AI, helping organizations validate, govern, and mitigate AI-related risks. We’re excited to announce the upcoming release of our new solution for LLM governance. Please consider being our beta tester and register for an early release.