collaboration platforms. This ensures accurate and up-to-date responses to internal queries. RAG also has an important advantage: the information it retrieves is not used to train the models, which is crucial for maintaining data confidentiality. Another advantage of RAG is its independence from the specific LLM it uses. This independence makes it easy to switch between different versions or LLMs as needed, ensuring the system can adapt quickly to new advancements or specific requirements without significant reconfiguration. Challenges of implementing AI Implementing AI in any field has several challenges. We had to give careful consideration to the following factors before implementing AI: › Data preparation: AI solutions can only be as good as the data they use. Ensuring clean, reliable, and well-organized data helps AI systems learn effectively, achieving more accurate results. › Integration with existing systems: Integrating AI with existing systems is challenging due to data silos and outdated legacy material. In some cases, these systems store data in unconnected and inconsistent formats. Established processes tied to these legacy systems often lack the flexibility to support AI, making it difficult for AI to access and process data efficiently. Departments need to collaborate and adapt established processes to ensure seamless data flow and maximize the potential of AI tools. › Acceptance: Resistance to change, a lack of understanding, and trust in AI’s capabilities make accepting AI solutions challenging. Overcoming this reluctance requires training, clear communication, and demonstrating the value of AI. Encouraging a culture that embraces innovation with ongoing support helps smooth the transition and enhance the overall effectiveness of AI initiatives. Casper: Our experiments with GenAI and LLMs Casper is our first GenAI-powered assistant. It was developed in 2023 as part of an innovation project aimed at helping colleagues find and draft answers related to OMICRON products. It can retrieve information contained in files and other knowledge resources. Along with the answer, it references related documents, allowing the user to obtain more information that helps validate it. Casper is intended for internal use only. Currently, there are no plans to turn it into an OMICRON product. Experimenting with a GenAI-powered bot enhances our innovative capabilities and lets us explore new ways to Retrieval Augmented Generation schema Knowledge indexing Retrieval Augmented Generation Context retrieval Answer generation Question Domain knowledge Encode Encode Large Language Model Answer generation Add metadata Similarity search Knowledge context Input Vector database (embeddings) Knowledge context Conversation System prompt Question 40
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