// How to Leverage LLMs on Your Enterprise Data?

Key Takeaway: Learn how Notilyze transformed a customer's journey to AI with transparency and trust at the forefront. 

Leveraging ‘Large Language Models’ (LLMs) on enterprise data is often considered the “holy grail” for businesses seeking to unlock significant time and cost savings. At Notilyze, we've spent the past few months working closely with our clients to explore how LLMs can be applied to their enterprise data, and perhaps you've been having similar discussions in your organization.  

Whether it’s an employee wondering if AI can take over repetitive tasks or a tech-savvy colleague suggesting a practical use case for LLMs in your enterprise, these LLMs have become a hot topic in many boardrooms. 

However, implementing LLMs in an enterprise setting raises several critical questions. Can we trust the output from these models? How can we ensure the security of our enterprise data when using LLMs? How do we prevent bias from creeping into our decision-making processes? And then there are the practicalities: How do we update or add new information to the model? Who should have access to which parts of the data, especially when sensitive documents are involved? 

At Notilyze, we’ve been down this road. We believe the key to successfully leveraging LLMs isn’t just the model itself - it’s the framework that surrounds and supports it. The framework is what makes or breaks the effectiveness of an LLM-based solution. Through real-world examples from our LLM applications, we’ll show you why and how this framework is essential with some screenshots. 

Accessibility Is Key 

For an LLM to be effective in your enterprise, it needs to be easily accessible to your employees. For example, GPT-3 had been available through an API months before its potential was unlocked by the user-friendly interface of ChatGPT. The lesson here? An intuitive interface can make all the difference. In the example below, we’ve simplified access to the LLM by allowing users to select a language and database on the left, then start chatting with the model in real time. This is crucial for driving adoption across teams. 

Enhancing Efficiency with RAG (Retrieval-Augmented Generation) 

In this case, the LLM searches within your enterprise data using Retrieval-Augmented Generation (RAG). This setup allows you to use existing LLMs on your own databases without needing costly retraining of the LLM. While retraining can sometimes yield more accurate results, RAG solutions provide a flexible and cost-effective alternative. RAG also allows for easier integration of multiple databases and seamless updating of information as your data evolves. 

Ensuring Trust and Transparency Through Prompt Engineering 

At Notilyze, we ensure consistency and transparency in the LLM’s responses through prompt engineering. The model cites the original source of the data for each answer, enabling users to easily validate and review the context behind each response. This increases trust and reduces uncertainty in the AI’s recommendations. 

Transparency and explainability are key when applying AI in enterprises, especially in fields where decisions can have significant business impacts. Providing users with source references gives them the confidence to make informed decisions. 

Delivering Contextual Relevance for Better Decision-Making 

We enhance the transparency of the LLM-generated answers by ensuring they are rich in relevant context. For example, by presenting a timeline of all relevant information, users can quickly determine whether the referenced legislation or document is up to date. Additionally, providing access to the full dossier from which the answer was derived allows employees to dive deeper into the topic if needed. 

Ready to Explore AI for Your Enterprise? 

At Notilyze, we guide our customers through the journey of integrating AI into their operations with transparency and trust. If you’re curious to see how LLMs can transform your business and make processes more efficient, we’re happy to help! 

Reach out to us today to discuss how we can support your path to AI.