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Is there though? It's just a matter of swapping out $BASE_API_URL.
by jejeyyy77 4y ago
Is there though? It's just a matter of swapping out $BASE_API_URL.
- KRAKRISMOTT 4y agoYou have to rebuild all your prompts when switching providers.
- jkaptur 4y agoDo you? They're natural language, right?
- travisjungroth 4y agoYou don't have to, but they will have been optimized for one model. It's unlikely they'll work as well on a different model.
- GalenErso 4y agoI can't wait for TolkienAPI, where prompts will have to be written in Quenya.
- michaelje 4y agoI can’t wait to hire Stephen Colbert to write prompts then
- birracerveza 4y agoNo problem, just ask ChatGPT to translate it in Quenya.
- DrBenCarson 4y agoIf the superlative LLM can’t handle prompts from another provider, it just isn’t the superlative LLM. This area by definition has no moats. English is not proprietary. Use case is everything.
- drusepth 4y agoSwitching to another LLM isn't always about quality. Being able to host something yourself at a lower or equal quality might be preferred due to cost or other reasons; in this case, there's no assumption that the "new" model will have comparable outputs to another LLM's specific prompt style. In a lot of cases, you can swap models easier but all the prompt tweaking you did originally will probably need to be done again with the new model's black box.
- novaRom 4y agoHost something yourself also for educational reasons, just experimenting, this is how new applications and technologies to be discovered and created.
- hathawsh 4y agoI imagine AI would be able to perform the translation. "Given the following prompt, which is optimized for $chatbot1, optimize it for $chatbot2".
- jejeyyy77 4y agotechnically true, but the way these prompts are/can be template-ized it should be relatively trivial to do so.
- tstrimple 4y agoMost of the clients I'm working with aren't interested in the base level of service. They are looking to further train the models for their specific use cases. That's a much higher barrier to switch than replacing an API. You've got to understand how the underlying models are handling and building context. This sort of customer is paying far more than the advertised token rates and are locked in more tightly.
- jejeyyy77 4y agonot really. fine tuning generally just involves running tailored training data through the model - the actual training algorithm is fairly generalized. For example, the Dreambooth fine tuning algorithm was originally designed for Google's image, but was quickly applied to Stable Diffusion.