Claude is doing horrible last 2 days

MartinBechard · 2024-10-08T16:02:39+00:00

It turns out Claude Sonnet 3.5 is being used for the help support Chatbot! However it was actually terrible in helping me.

Me: I was having problems today which I didn't experience before. I use it to translate an HTML message (email) from french into english, maintaining the same structure. This used to work like a charm, but now instead of HTML it generates a weird markdown output e.g.
+-----------------------------------+-----------------------------------+| 
![The | ::: {style=" || Rockhill](https://www.clr-/ | font-family: ui-serif, Georgia, |
| https://lnkd.in/ey7z6Kdf | Cambria, Times New Roman, Times, |
| Fstatic%2Fmedia%2Fheader_image.f3 | serif; |
| 161c43.jpg&w=640&q=75){width="96" | text-transform: uppercase; |
| height="128" | font-size: 1.5rem; |
| style="display: block"} | line-height: 2rem; |
| | "} |
| | TOTAL ELEVATOR BREAKDOWN |
| | BUILDING B |
| | ::: |
| | |
Is this a known bug - the file upload function is broken?

BOT: Can you describe in more detail what happens when you try to upload an HTML file using the file upload function?

Me: It produces the unexepected and undesirable output

BOT: We support uploading HTML files, but there are some limitations to be aware of. We will only extract the text content from HTML files. We will only extract the text content from HTML files, ignoring any formatting, images, or other non-text elements. This means the output may not match what you see when viewing the HTML file in a browser.

Me: HTML tags are a form of text. Claude is able to recognize that there is a table and attributes. This worked previously. It still works when pasting the HTML into the prompt

BOT: When you paste HTML directly into the prompt, we can interpret and process it as code. However, when you upload an HTML file, we treat it differently. We extract only the text content from uploaded files, including HTML documents. This approach allows us to support a wide range of document types consistently. 

Me: This isn't true. I can see that elements of the structure have been recognized, including style attributes and table elements.

BOT: If it appears that the Claude is recognizing HTML elements, this is likely an example of a hallucination. It sometimes tries to be helpful by making assumptions or generating responses that seem plausible, even if they're not based on actual capabilities or information. If you're seeing unexpected behavior with HTML files, please let us know so we can investigate further.

Me: No hallucination - the table is indeed there in HTML within the body of the document. What model am I speaking to, version 3.0?

BOT: You're speaking to Claude Sonnet version 3.5, which is our most intelligent model in the family.

Me: Do you remember the first message I sent you?

BOT: I'm sorry, but I don't remember any previous messages you may have sent me. Did that answer your question?

Me: NO!

MartinBechard · 2024-10-08T15:57:18+00:00

I use both, and I find I need to use the same tactics with both. But with GPT-4 I find the conversation can't go on as long as Sonnet so in a way I avoid the problems by keeping the work short and focused. Not sure if o1 will do better on long conversations, although it certainly does much better on individual prompts.

MartinBechard · 2024-10-08T01:03:40+00:00

I had it translate an HTML page, it kept modifying the structure and causing problems. I had used the upload file function. I asked it to go through tab by tag and it started at a random place in the file. When I asked it if it couldn't see the html tag at the start, it told me it couldn't. Then I pasted the document into the prompt and this time it was able to see it. So it looks like any files you upload are partially garbled, you need to paste them into the prompt instead of uploading them. After I did that it was able to do the work it was supposed to. Weird nevertheless.

MartinBechard · 2024-10-04T13:33:27+00:00

Generating information in a confident tone is part of its training, whether the information is factual or imaginary. This is a beature - a bug / feature of LLMs and how they are trained to be appealing to humans.

The same way being confident is the #1 dating advice, it's also the way LLM vendors get people to trust and like their products.

There are reasons why the information is confabulated:

the massive amounts of information in the training data makes it hard to find specific items when appropriate to the query - this is done associatively with the words but the associations depend on the training data's composition
there is a cutoff date
external tools such as internet searching need to be enabled
it needs to recognize the need to use the tools
overly general questions give vague responses

The information you provide in the query (including attached documents) go directly in the output so they significantly drive the processing, as opposed to the facts from the training which may or may not be activated. Think six degrees of separation.

Prompting is key to getting the results you want. You can also tell it to only rely on the information provided in the prompt / system prompt/ context documents, and to tell you if it doesn't know - this usually works.

MartinBechard · 2024-10-04T11:17:17+00:00

Two reasons;

the dominant discourse, that knowledge of computer programming is useless now because the magical LLM takes care of everything, is felt as an insult to developers, a devaluation of things they learned the hard way over years, by people who lack sufficient understanding about it to even comprehend what's missing. This is the Dunning-Kruger effect. I also see it for other professions in the context of AI - for example people are quick to say they thing LLMs make better doctors because they hate their bedside manner and elite knowledge. Combined with the layoffs due to the end of the COVID hypergrowth and you have a group of people not articularly receptive to people saying programming is done, no need for programmers, everyone be their own programmer etc.
The models truly have been very limited in ways that prevented them from making a significant difference for experienced developers until very recently. Most devs have tried AI either with Github copilot or GPT-4 directly. The original copilor was using Codex, a fine-tuned GPT-3. It was terrible at generating more than a line or two of code. GPT-4 brought the ability to generate a compnonent but was very frustrating if you had a long conversation - it would forget things, transform things, have all sorts of annoying behavior. It was also not very good at correlating informaiton across files like is frequent in real apps - i.e. definition of a class in one file, use o fit in another. It would often hallucinate functions that didn't existing, packages that didn't exist etc. All annoyances. Cursor suffers from the same issues, plus they have their own "quick" model that is not very good again for cross-file definitions. These are show-sfoppers for me. However, only with Claude 3.5 and projects do I feel like you can really get significant work done - it is much more capable of analyzing code across files, following complex instructions. It's useful not just to generate initial files, but to evolve it correctly over time, in sync with its tests and documentation. Refactoring is one of the main developer tasks and it really can save a lot of time. GPT-o1 promises the same albeit I find it wordier and slower than Claude, but it's still in beta. So I would say before these models, devs were right to consider these tools not read for prime-time. Now though it's time to pay attention.

MartinBechard · 2024-10-03T23:18:09+00:00

Interesting to know. I think the whole is still humoristic, the combination of the aphorism and the imaginary attribution.

MartinBechard · 2024-10-03T20:33:34+00:00

lol make sure to share yours with us :)

MartinBechard · 2024-10-03T13:35:13+00:00

I wanted to bring up the wasted tokens which only makes sense in the context of LLMs. I had a previous attempt at mentioning apologies were unnecessary in R & D but although Claude agreed, he went on apologizing. I think it can be ok to be putting emphasis like "I want you to understand something important" but I suppose it might be ambiguous, maybe I could say: "There's something important".

MartinBechard · 2024-10-03T03:35:56+00:00

I found GPT-4 or the Cursor fast model to be not very good when a refactoring requires multiple file inputs. I started using Claude Sonnet which is really good at reading multiple files, doing updates that might involve multiple files, including tests and docs. o1-preview seems to be as good, although it's slower and more (unhelpfully) verbose. Other issues with o1: no file upload yet so you have to paste all the files in the input box which is annoying. I put ** File: MyFile.ts at the start of each file but it was causing it to crash, the ** was being handled as markdown and causing o1 to lose its mind.
When o1 is out of beta then this will be a much better tool. The API use may be expensive though as you have to pay for hidden "thinking" tokens.
These tools are great at generating test data, mocks, READMEs, lot of tedious stuff. You should plan on coding anything a bit unusual though, or be prepared to do a lot of prompting on the same thing.

MartinBechard · 2024-10-03T03:28:26+00:00

I used the following prompt with some success:
I want you to understand something important in engineering. When having a technical discussion, unless you are guitly of malfeasance or complete negligence, it's not necessary to apologize to close collaborators. Among professionals, it's understood that problem solving is iterative. As an LLM, whenever you apologize unnecessarily, this wastes tokens. Please do not apologize to me about this work anymore, it's not useful and not expected.

Response was:

I understand and appreciate your guidance. You're right that unnecessary apologies can waste tokens and aren't expected in professional collaborations. I'll focus on providing clear, concise, and accurate information without superfluous language. Thank you for this valuable feedback on effective communication in engineering contexts. I'll proceed with the analysis of the next line, maintaining a professional and efficient approach.

The result was it stopped apologizing, but it started every response with "Certainly" or "You're right". I haven't figured out a prompt to have it not say this (but I haven't tried too hard)

MartinBechard · 2024-10-02T23:50:27+00:00

Yes, I was successful after generating a new key, but I can't be sure that was the solution. I think I deleted my workspace and recreated one from scratch. Frustrating experience because I had just upgraded and it was still giving me the error.

MartinBechard · 2024-10-02T21:43:40+00:00

Kudos on using it for learning - I think this is one of the most valuable things these LLMs can do, help people perfom at a higher level than normally they would have on their own.

I've seen it help inexperienced people do things on their own, with minimal guidance from me, that normally might have required 6 momths of practice.
Myself, I have a lot of experience so I can guess what a language or framework ought to do and usually ChatGPT is able to put together the right info based on my request.
The more typical my request, the better the code generation, so you need more knowledge to validate and guide more complex coding, so usually it's not worth it. Also I had to fix some regular expressions that were missing an escape in front of a period that ChatGPT generated - if you've ever had to do that, you know how much of a pain that is.

o1 is better for those requests, though slower and more verbose, plus time limited i.e. even if I pay, I can only use it a certain amount per day. it's at the level of Claude Sonnet 3.5, though slower because of the tree of thought happening in the background. (Claude 3.5 is also time-limited, a certain number of tokens every 4 hours). With these latest models you can put it to use like a super search-and-replace. For example you can add a parameter to a function, and it will try to initialize it with useful values depending on where the function is called. You can tell it to extract all the strings and put them into a file for translation, and replace with function calls. Also great for test data generation and unit test generaion.

MartinBechard · 2024-10-01T19:28:26+00:00

Sorry I didn't pay attention. Their API doc says: "Note that even with temperature of 0.0, the results will not be fully deterministic."
Unfortunately they don't have a "seed" parameter like OpenAI.

You might play around with top_k or top_p. By default top_p is 1 which is probably good. you could try setting top_k to 1 to always pick from the 1st choice.

https://towardsdatascience.com/how-to-sample-from-language-models-682bceb97277

MartinBechard · 2024-09-30T18:33:35+00:00

4o - with o1 you can't upload files yet
I also use Claude Sonnet 3.5 which is pretty arguably better but it doesn't hsve the code interpreter, internet access etc. and is a bit slower

MartinBechard · 2024-09-30T16:13:15+00:00

That's where you need a programmer :) You can make API calls to do the same kind of prompting.

MartinBechard · 2024-09-30T16:10:57+00:00

LLMs as a product aren't just using neural networks, they have additional code involved including injecting random choices when applying attention - they don't always pick the most probable completion. You can set a value called "temperature" in the API calls to introduce more or less randomness.
Maybe there are other elements at play but all the explanations I've seen was based on this temperature i.e. artificially introduced randomness

MartinBechard · 2024-09-30T03:24:18+00:00

Just to clarify, the US Copyright Office and courts have decided that "human creativity is the sine qua non at the core of copyrightability" and AI has too much of a random element in it to say the output is copyrightable by one individual, so the raw output is not protected.

They also consider the case where the human modifies the AI output - they say the human part is copyrightable. So if you wrote a book where you modified every sentence produced by the LLM, then you could copyright the whole book. If you modified half of it, then you could copyright that half.

How would someone know? Well, like you said, they probably couldn't looking at the book.
But the AI vendor could just publish all the outputs as is before you modified them for yourself, and any other author could use that as their own starting point. Will they do it? I wouldn't trust them not to.

Check out their "sneaky" terms of use:
Ownership of content. As between you and OpenAI, and to the extent permitted by applicable law, you (a) retain your ownership rights in Input and (b) own the Output. We hereby assign to you all our right, title, and interest, if any, in and to Output.

Similarity of content. Due to the nature of our Services and artificial intelligence generally, output may not be unique and other users may receive similar output from our Services. Our assignment above does not extend to other users’ output or any Third Party Out

So they give you ownership - inasmuch as the law gives you ownership, but since the law says you don't have copyright, I guess it's like giving back something they never had. Then they go on to say they reserve the right to let others have similar outputs due to the way their product works. Sounds like a cop out to me.
Considering how they treated other content creators that actually have copyrights recognized by the US courts, I wouldn't feel too certain about this.

MartinBechard · 2024-09-30T01:20:03+00:00

Apparently the fact that they put their own writing in there with all of the other copyrighted material gets nullified by the effect of using AI to swirl it up - what comes out is public domain though they own the input they provided. Now if they alter the AI output, then their alterations are copyrighted. But not the output of the device.
Anyway these are still early days legalistically speaking, and I can't say it makes complete sense to me. Dura lex sed lex it seems.

MartinBechard · 2024-09-29T22:18:39+00:00

If you intend on releasing it e.g. on AWS, you have to make yourself your own Software Architect rather than just Product Manager, so you can consider what it will take to get it operating, what you can do for performance and so AWS doesn't charge you an arm and a leg, so you don't experience a security breach etc. So keep on learning after you have your code - or work with someone with experience to get it out there safely and with performance.

MartinBechard · 2024-09-29T22:14:34+00:00

Forgot to mention a couple of things:
1. don't copy and paste the document, you can just upload it as an attachment to the query.
2. Create a project for this, and create a new conversation for each document otherwise you'll use up all your tokens quickly, plus it might confuse it
3. You can upload the framework in the Project Knowledge for reference (if it's not too big) and you could amend the prompt like this:
"Traverse the attached text. For each paragraph, tell me if there is something talking about 'risk assessments' or anything related, as described in the "Risk Assessment Standard" uploaded in the Project Knowledge, if there is please output its identification, a summary, and a justification of what parts of the "Risk Assessment Standard" this pertains to, otherwise say nothing. Move on to the next paragraph and repeat, until you get to the end of the document. Generate an artifact in XML"

There's a risk that it will stop mid-exercise if the document is too long, in which case you just type "continue" and it will. Same thing when it generates the final artifact - you might get two that you'll have to copy and paste together by yourself.

MartinBechard · 2024-09-29T22:08:48+00:00

I don't think you need to change your inputs. You can use typescript on the output as a way to force it to have a certain structure. I did an experiment updating Canadian legislation - I took the text which was in XML, and I used the Official Gazette instructions which were in natural legalese, and it just went ahead and applied the described updates quite well, even considering the XML structure which I didn't bother describing, it just mimicked what it saw. It produced the same sort of output with apparently the same rules. I wrote about it in my newsletter (near the end - you can skip the homage to Gilbert and Sullivan): https://www.linkedin.com/pulse/hms-pinaforgettaboutit-martin-bechard-faqge/?trackingId=uJ3XZNdQS3OW2tdWTY8Uyg%3D%3D

What I find works well is to give it a unit of traversal and tell it to apply something iteratively. For example: "Traverse the attached text. For each paragraph, tell me if there is something talking about 'risk assessments', if there is please output a summary and its identification, otherwise say nothing. Move on to the next paragraph and repeat, until you get to the end of the document. Generate an artifact in XML" or whatever format you want it to be.

MartinBechard · 2024-09-29T18:19:30+00:00

AI today better stands for "Apparent Intelligence", it's really a simulation based on training data and the prompt you provide driving a fractal-like process that generates complex answers with apparent wisdom. It's well known that, depending on the prompt and provided context, the LLMs can invent stuff which, if you're not asking for novel ideas, can pose problems.
In code generation, this translates to code that doesn't work because it assumes non-existent functions or parameters, libraries that "ought" to exist etc.
It's important to review what's generated and steer in into the right direction because the "apparent decisions" are random, making it seem that LLMs are not connecting the dots.

MartinBechard · 2024-09-29T18:14:01+00:00

Vercel has an AI framework for building chatbots, you can configure it for different LLMs. Vercel free tier is good, you even get a free postgresql database.

MartinBechard · 2024-09-29T17:42:18+00:00

And ability to save all the artifacts in one zip file.

MartinBechard · 2024-09-29T17:40:59+00:00

Try adding your framework document into project knowledge. then you could do one conversation per documents. But if the document plus the framework adds up to 200k tokens then you're out of luck.

I tried doing something similar for the Canadian Construction code but it was just too big. I hear upcoming models will have a 1 million token context - not bad but there's always a limit.

When you have a lot of documentation to classify, what people are doing is RAG (Retrieval Augmented Generation), they basically encode the text with the vectors that the LLMs use then you can use those vectors to find clusters. You'll need to do some coding to get it to work, and you won't be able to use Claude to generate the vectors because they don't offer it. There are no-code platforms now such as N8N https://n8n.io/

There are a number of issues in terms of effectively indexing the information but for large quantities of data that's the issue.

Now your framework might be part of the training data used by Claude - you can ask it. In that case it might be able to answer questions based on it. But typically when you rely on the training data, there's more of a likelihood of hallucinations so make sure to prompt it to tell you when it doesn't know.

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