Feel free to drop your email if you’re interested! For context: As we talked with developers and product builders we noticed a common need for customising LLMs

Feel free to drop your email if you’re interested! For context: As we talked with developers and product builders we noticed a common need for customising LLMs on their own data through fine-tuning (Retrieval Augmented Generation mainly, but some-times actual fine-tuning). Models like GPT, Claude and Llama2 have great reasoning capabilities but may not perform optimally for specific use cases where relevant information from knowledge sources is needed. As we looked how this is done today it requires mastering a bunch of things from data retrieval, configuring vector DBs, data enrichement using embedding and ensuring things work not only for a few documents but for large amounts of data. We're building ragapi to manage all this heavy lifting so you can focus on building the rest of the (i.e use case related things).