I posted this mainly as something to refer friends to when they complain about something being to competitive / hard to get. Thought I'd shoot it here too.
It surprises me that people still believe this! I've seen AI deliver incredible value over the past year. I believe the application level is utilizing <.5% (probably less) of the total value that can be derived from current foundation models.
Great idea! Will it highlight parts where the professor says something like "this is important and will be on the exam...". All of the information on the exam (which dictates the majority of your score in the class at most US universities) must be conveyed to the student one way or the other (worksheets, lectures. etc.). A cool runoff would be an "AI Exam Prep" which guessed what would be on the exam, based on previous exams and where the info came from
I hear this a lot and I think it is good advice because the only person who should actually start a startup is the one who sees this but still does it.
Based on my very limited knowledge of how current "AI" systems work, this is the much better approach to achieving true AI. We've only modeled one small aspect of the human (the neuron) and brute forced it to work. It takes an LLM millions of examples to learn what a human can in a couple of minutes--then how are we even "close" to achieving AGI?
Should we not mimic our biology as closely as possible rather than trying to model how we __think__ it works (i.e. chain of thought, etc.). This is how neural networks got started, right? Recreate something nature has taken millions of years developing and see what happens. This stuff is so interesting.
From what I'm seeing on GH, this could have technically already been built right? Is it not just taking screenshots of the computer screen and deciding what to do from their / looping until it gets to the solution ?
> Hi all, I am trying to build a simple LLM bot and want to add guard rails so that the LLM responses are constrained.
Give examples of how the LLM should respond. Always give it a default response as well (e.g. "If the user response does not fall into any of these categories, say x").
> I can manually add validation on the response but then it breaks streaming and hence is visibly slower in response.
I've had this exact issue (streaming + JSON). Here's how I approached it:
1. Instruct the LLM to return the key "test" in its response.
2. Make the streaming call.
3. Build your JSON response as a string as you get chunks from the stream.
4. Once you detect "key" in that string, start sending all subsequent chunks wherever you need.
5. Once you get the end quotation, end the stream.
I'm a licensed agent in CA and am particularly interested in the answer to this question.
There's very strict regulation around unlicensed individuals performing activities requiring a license (negotiating, showing property, etc). Thus I would be surprised if they have AI doing this for a client!
Glad to see I'm not the only one experiencing this. The agents framework I use is moving very fast and its not uncommon for even minor versions to break my current setup