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Fleet AI Copilot is your new AI-driven IT assistant, streamlining equipment management and transforming daily IT tasks. It optimizes productivity by personalizing support, centralizing operations, and adapting to your needs, simplifying your life effectively.
When integrating internal knowledge into AI applications, three main approaches stand out:
1. Prompt Context Load all relevant information into the context window and leverage prompt caching.
2. Retrieval-Augmented Generation (RAG) Use text embeddings to fetch only the most relevant information for each query.
3. Fine-Tuning Train a foundation model to better align with specific needs.
Each approach has its own strengths and trade-offs:
It sounds like a really practical solution for IT teams! The choice of prompt context to streamline operations is interesting especially with the increased context window sizes. As the knowledge base grows, how do you foresee the transition to a hybrid approach (RAG and fine-tuning) impacting the system's efficiency and scalability? Also, are there any specific use cases where you think fine-tuning would be essential over RAG or prompt context? Excited to see how Fleet AI Copilot evolves!
