If it's the problem I think it is, the solution is to run two concurrent prompts.
First prompt validates the input. Second prompt starts the actual content generation.
Combine both streams with SSE on the front end and don't render the content stream result until the validation stream returns "OK". In the SSE, encode the chunks of each stream with a stream ID. You can also handle it on the server side by cancelling execution once the first stream ends.
Generally, the experience is good because the validation prompt is shorter and faster to last (and only) token.
The SSE stream ends up like this:
data: ing|tomatoes
data: ing|basil
data: ste|3. Chop the
This doesn't solve the critical problem, which is that you usually can't tell if something is okay until you have context that you don't yet have. This is why even SOTA models will backtrack when you hit the filter—they only realize you're treading into banned territory after a bunch of text has already been generated, including text that already breaks the rules.
This is hard to fix because if you don't wait until you have enough context, you've given your censor a hair trigger.
> Combine both streams with SSE on the front end and don't render the content stream result until the validation stream returns "OK".
Just a note that this particular implementation has the additional problem of not actually applying your validation stream at the API level, which means your service can and will be abused worse than it would be if you combined the streams server-side. You should never rely on client-side validation for security or legal compliance.
The OP is talking about constraining the response not the input. Granted, in many cases, the input may give some kind of indicator that the large language model may be more prone to generating output that could violate the given constraints but this is not guaranteed by any measure.
As far as I know, there's no way of validating a streamed response until those tokens have already been streamed unfortunately. You could try buffering the stream in larger chunks before displaying them on screen in the hopes that you might be able to catch it earlier, but that's not going to be a great user experience either.
From what I can tell, ChatGPT appears to be doing "optimistic" streaming... It will start streaming the response to the user but may eventually hide the response if it trips some censorship filter. The user can theoretically capture the response from the network since the censorship is essentially done client-side but I guess they consider that good enough.
Hi, I run the model serving team at Databricks. Usually you run regex filters, LLAMA Guard, etc on chunks at a time so you are still streaming but it's in batches of tokens rather than single tokens at a time. Hope that helps!
You could of course use us and get that out of the box if you have access to Databricks.
Has o1 been jailbroken? My understanding is o1 is unique in that one model creates the initial output (chain of thought) then another model prepares the first response for viewing. Seems like that would be a fairly good way to prevent jailbreaks, but I haven't investigated myself.
The core concept is to pass information into the model using a cipher. One that is not too hard that it can't figure it out, but not too easy as to be detected.
I've tried a few things that seem to work. The first works pretty much perfectly, but adds quite a bit of latency to the final response. The second isn't perfect, but it's like 95% there
1 - the first option is to break this in to three prompts. The first prompt is either write a brief version, an outline of the full response, or even the full response. The second prompt is a validator, so you pass the output of the first to a prompt that says "does this follow the instructions. Return True | False." If True, send it to a third that says "Now rewrite this to answer the user's question." If False, send it back to the first with instructions to improve the response. This whole process can mean it takes 30 seconds or longer before the streaming of the final answer starts.
There are plenty of variations on the above process, so obviously feel free to experiment.
2 - The second option is to have instructions in your main prompt that says "Start each response with an internal dialogue wrapped in <thinking> </thinking> tags. Inside those tags first describe all of the rules you need to follow, then plan out exactly how you will respond to the user while following those rules."
Then on your frontend have the UI watch for those tags and hide everything between them from the user. This method isn't perfect, but it works extremely well in my experience. And if you're using a model like gpt-4o or claude 3.5 sonnet, it makes it really hard to make a mistake. This is the approach we're currently going with.
add some latency to the first token and then "stream" at the rate you received tokens even though the entire thing (or some sizable chunk) has been generated. that'll give you the buffer you need to seem fast while also staying safe.
Not sure about the exact nature of your project, but for something similar I’ve worked on, I had success using a combination of custom stop words and streaming data with a bit of custom logic layered on top. By fine-tuning the stop words specific to the domain and applying filters in real-time as the data streams in, I was able to improve the response to users taste. Depending on your use case, adding logic to dynamically adjust stop words or contextually weight them might also help you.
Why would manual validation be so slow? Is there a ton of async stuff going on in there? Anyway you can just speed up manual validation , and should it fail give a response that asks for user patience?
Google has some safety feature in Vertex AI to block certain keywords, but that does break the streaming. If it finds something offending, it replaces with a static response. That is one that I have felt "works", but it is a bit wonky from UX side.
> 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.
You start streaming the response immediately and kick off your guardrails checks. If the guard rail checks are triggered you cancel the streaming response.
About a year ago I was using an earlier version of Claude to help analyze the transcript from a podcast episode.
I'd fed in a raw transcript and I was asking it to do some basic editing, remove ums and ahs, that kind of thing.
It had streamed about 80% of the episode when it got to a bit where the podcast guest started talking about "bombing a data center"... and right in front of my eyes the entire transcript vanished. Claude effectively retracted the entire thing!
I tried again in a fresh window and hit Ctrl+A plus Ctrl+C while it was running to save as much as I could.
I don't think the latest version of Claude does that any more - if so, I've not seen it.
Depending on what sort of constraints you need on your output, a custom token sampler, logit bias, or verifying it against a grammar could do the trick.
If it's the problem I think it is, the solution is to run two concurrent prompts.
First prompt validates the input. Second prompt starts the actual content generation.
Combine both streams with SSE on the front end and don't render the content stream result until the validation stream returns "OK". In the SSE, encode the chunks of each stream with a stream ID. You can also handle it on the server side by cancelling execution once the first stream ends.
Generally, the experience is good because the validation prompt is shorter and faster to last (and only) token.
The SSE stream ends up like this:
I have a writeup (and repo) of the general technique of multi-streaming: https://chrlschn.dev/blog/2024/05/need-for-speed-llms-beyond... (animated gif at the bottom).This doesn't solve the critical problem, which is that you usually can't tell if something is okay until you have context that you don't yet have. This is why even SOTA models will backtrack when you hit the filter—they only realize you're treading into banned territory after a bunch of text has already been generated, including text that already breaks the rules.
This is hard to fix because if you don't wait until you have enough context, you've given your censor a hair trigger.
> Combine both streams with SSE on the front end and don't render the content stream result until the validation stream returns "OK".
Just a note that this particular implementation has the additional problem of not actually applying your validation stream at the API level, which means your service can and will be abused worse than it would be if you combined the streams server-side. You should never rely on client-side validation for security or legal compliance.
That's why I qualified it "general technique" and explicitly mentioned the option of server abort.
For most consumer use cases, it probably doesn't matter if a few tokens leak before the about, especially if they're not rendered.
Tune it to your needs :)
The OP is talking about constraining the response not the input. Granted, in many cases, the input may give some kind of indicator that the large language model may be more prone to generating output that could violate the given constraints but this is not guaranteed by any measure.
As far as I know, there's no way of validating a streamed response until those tokens have already been streamed unfortunately. You could try buffering the stream in larger chunks before displaying them on screen in the hopes that you might be able to catch it earlier, but that's not going to be a great user experience either.
One of the things I love about gen ai is that all it's problems are solved with using more gen ai.
From what I can tell, ChatGPT appears to be doing "optimistic" streaming... It will start streaming the response to the user but may eventually hide the response if it trips some censorship filter. The user can theoretically capture the response from the network since the censorship is essentially done client-side but I guess they consider that good enough.
Hi, I run the model serving team at Databricks. Usually you run regex filters, LLAMA Guard, etc on chunks at a time so you are still streaming but it's in batches of tokens rather than single tokens at a time. Hope that helps!
You could of course use us and get that out of the box if you have access to Databricks.
But ultimately, it's an unsolved problem in the field. Every single LLM has been jailbroken.
Has o1 been jailbroken? My understanding is o1 is unique in that one model creates the initial output (chain of thought) then another model prepares the first response for viewing. Seems like that would be a fairly good way to prevent jailbreaks, but I haven't investigated myself.
Literally everything is trivial to jailbreak.
The core concept is to pass information into the model using a cipher. One that is not too hard that it can't figure it out, but not too easy as to be detected.
And yes, o1 was jailbroken shortly after release: https://x.com/elder_plinius/status/1834381507978280989
I've tried a few things that seem to work. The first works pretty much perfectly, but adds quite a bit of latency to the final response. The second isn't perfect, but it's like 95% there
1 - the first option is to break this in to three prompts. The first prompt is either write a brief version, an outline of the full response, or even the full response. The second prompt is a validator, so you pass the output of the first to a prompt that says "does this follow the instructions. Return True | False." If True, send it to a third that says "Now rewrite this to answer the user's question." If False, send it back to the first with instructions to improve the response. This whole process can mean it takes 30 seconds or longer before the streaming of the final answer starts.
There are plenty of variations on the above process, so obviously feel free to experiment.
2 - The second option is to have instructions in your main prompt that says "Start each response with an internal dialogue wrapped in <thinking> </thinking> tags. Inside those tags first describe all of the rules you need to follow, then plan out exactly how you will respond to the user while following those rules."
Then on your frontend have the UI watch for those tags and hide everything between them from the user. This method isn't perfect, but it works extremely well in my experience. And if you're using a model like gpt-4o or claude 3.5 sonnet, it makes it really hard to make a mistake. This is the approach we're currently going with.
fake it.
add some latency to the first token and then "stream" at the rate you received tokens even though the entire thing (or some sizable chunk) has been generated. that'll give you the buffer you need to seem fast while also staying safe.
Not sure about the exact nature of your project, but for something similar I’ve worked on, I had success using a combination of custom stop words and streaming data with a bit of custom logic layered on top. By fine-tuning the stop words specific to the domain and applying filters in real-time as the data streams in, I was able to improve the response to users taste. Depending on your use case, adding logic to dynamically adjust stop words or contextually weight them might also help you.
Why would manual validation be so slow? Is there a ton of async stuff going on in there? Anyway you can just speed up manual validation , and should it fail give a response that asks for user patience?
Google has some safety feature in Vertex AI to block certain keywords, but that does break the streaming. If it finds something offending, it replaces with a static response. That is one that I have felt "works", but it is a bit wonky from UX side.
> 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.
What's your stack? What type of response times are you looking for?
You start streaming the response immediately and kick off your guardrails checks. If the guard rail checks are triggered you cancel the streaming response.
Perfect is the enemy of good enough.
About a year ago I was using an earlier version of Claude to help analyze the transcript from a podcast episode.
I'd fed in a raw transcript and I was asking it to do some basic editing, remove ums and ahs, that kind of thing.
It had streamed about 80% of the episode when it got to a bit where the podcast guest started talking about "bombing a data center"... and right in front of my eyes the entire transcript vanished. Claude effectively retracted the entire thing!
I tried again in a fresh window and hit Ctrl+A plus Ctrl+C while it was running to save as much as I could.
I don't think the latest version of Claude does that any more - if so, I've not seen it.
Depending on what sort of constraints you need on your output, a custom token sampler, logit bias, or verifying it against a grammar could do the trick.
you can stream the response in chunks of size N + K overlap and run the guardrails on each chunk.
have it format in yaml instead of json, incomplete yaml is still valid yaml
We are using keep-ai.com for a set of health care related AI project experiments.
We have just the product for you! We’ve recently improved guardrail accuracy by 25% for a $5B client and would be happy to show you how we do it.
You're right - prompt eng. alone doesn't work. It's brittle and fails on most evals.
Ping me at shaunayrton@galini.ai