I've noticed that while a bunch of developers have played with LLM's for toy projects, few seem to have any actual experience taking it to prod in front of real users. I’ve personally had to do so for a few startups, and it's like trying to nail Jell-O to a tree. Every random thing you change, from prompts to models, yields massively different/unpredictable results.
I think because of this, a bunch of companies/tools have tried to hop in this space and promised the world, but often times people are best served by just hitting OpenAI/GPT directly, and jiggling the results until they get what they want. If you're not comfortable doing that, there are even companies that do that for you, so you can just focus on the prompt itself.
So that being said, help me understand why I should be adding this whole system/process to my workflow, versus just hitting OpenAI/Anthropic/Google directly?
You're right - hitting OpenAI/Anthropic/Google directly is often the quickest way to get started, and for many simple applications, it might be all you need. However, Release.ai addresses the needs of companies that require more control, customization, and scalability in their AI systems.
Release.ai isn't about replacing the big players but about giving you options. It's for when you need more than a generic API call but don't want to build an entire ML infrastructure from scratch. You can build exactly what you need without getting a Ph.D. in machine learning or becoming a DevOps expert.
I think because of this, a bunch of companies/tools have tried to hop in this space and promised the world, but often times people are best served by just hitting OpenAI/GPT directly, and jiggling the results until they get what they want. If you're not comfortable doing that, there are even companies that do that for you, so you can just focus on the prompt itself.
So that being said, help me understand why I should be adding this whole system/process to my workflow, versus just hitting OpenAI/Anthropic/Google directly?
Release.ai isn't about replacing the big players but about giving you options. It's for when you need more than a generic API call but don't want to build an entire ML infrastructure from scratch. You can build exactly what you need without getting a Ph.D. in machine learning or becoming a DevOps expert.