Name: Fleet
Rating: 5.0 (4 reviews)

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:

When integrating internal knowledge into AI applications, three main approaches stand out:

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!

Tagline of the product holds the real meaning.

The right solution to simplify your IT management!

Tagline of the product holds the real meaning.

The right solution to simplify your IT management!

Fleet

Your all-in-one IT Solution

Your all-in-one IT Solution

Fleet

Your all-in-one IT Solution

Your all-in-one IT Solution

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Forum Threads

The differences between prompt context, RAG, and fine-tuning and why we chose prompting

Fleet Launches

Do you use Fleet?

Forum Threads

The differences between prompt context, RAG, and fine-tuning and why we chose prompting