All-in-one solution for IT leasing and device management. We procure, secure, assist and recycle IT equipment. Our first product is the Fleet Cockpit. 🚚 Track devices ⎮ 👥 Organize teams ⎮ ⌛️ Save time ⎮ 💻 Provide support ⎮🛡️Secure
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All-in-one solution for IT leasing and device management. We procure, secure, assist and recycle IT equipment. Our first product is the Fleet Cockpit. 🚚 Track devices ⎮ 👥 Organize teams ⎮ ⌛️ Save time ⎮ 💻 Provide support ⎮🛡️Secure
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!
