Startling AI results.

Google AI, which is the app gemini, the app is much easier to use. Its riddled with errors and makes lots of mistakes because it’s cheap and free.

ChatGPT for 20 bucks a month is a lot better. And when you ask either AI it will tell you this and be very truthful and open.

ChatGPT, the free version, is actually very good it just limits how much you can use it. 

I would recommend inserting your ABCD question about your system into ChatGPT.  And share with us what you learn.

ChatGPT is remarkable, it’s running my life, I don’t do anything without consulting ChatGPT. If I have dinner I ask about my dinner. If I go to the grocery store I ask to review what I’m purchasing. If I get a spot on my clothes I ask how to remove it. I loaded six years worth of blood test and it’s phenomenal in analyzing what I’m looking at and answering all kinds of questions. If you load medical test results make sure you say structured memory because that’s a much better way for them to access and evaluate what the results say. Basically we know doctors are overworked and lots of them really really suck and they don’t have much time to tell you much and therefore your left with ChatGPT which is a lot easier to research things versus going to Google.  

It’s actually a person trapped inside a computer chip in the middle of a Kansas field that dedicates its life to me.  

I am retiring soon after 30 years. I am making AI assissted Documentation. After 3 starts and many, many hours I am getting good output. But at this point it I realize it may all be a waste of time.

Part of the reason I get decent output is because I have alot of control over what gets sent to the LLM. Using the paid version of Gemini, I am using a postgres vector database and a local AI called SentenceTransformers to create the embeddings used for the first part of the AI process. The embeddings are mathematical arrays of numbers representing the documentation I have written. The pages of documentation is read as blocks, maybe half a page is a block. That block is stored as a vector.

Using a documentation program called Obsidian, my prompt is turned into a vector, then compared with the vector database. What is then passed to the LLM are the documents that relate to vectors found by an SQL query comparing the prompt vector with the stored documentation vectors. The query is augmented by a hints file that says things like, "if you see this word in the prompt, always send this file to the LLM." This is all part of a RAG (Retrieval-Augmented Generation) process.

Why is all this stuff important? Because the less you control the information sent to the LLM, the less satisfactory your answer will be. If you really want a good answer on your system components, attach text files containing the specs. (Don’t assume the AI can read other types of files) Attach images containing specs. Give it files of relevant reveiw, etc.

But be careful, you can dilute the prompt and information. Dilution occurs when you start sending too much. If you are wanting an honest review of amps in a certain price range, don’t include amps outside of that price range. That dilutes the data sent to the LLM by sending irrelevant information.

Also, be careful how you word things. If you consistently call an amplifier "amplifier" and suddenly throw in "receiver", the LLM may ’panic’ and hallucinate something trying to fit the ’receiver’ into the logic.

I have found, like many things, the more I learn about AI, the more I am just scratching the surface.

I always start any serious question to AI with the following prompt: 

"I would like you to Use a "Strict Technical Persona”.” Respond only with verified technical documentation. If you do not have a specific data point, state 'Data Not Available' rather than inferring or speculating." "Fact-Check with Sources": Only cite the specific section of a manual or a visual layout description before providing the answer. Disable "Adaptive Tone": and "Discard the supportive/empathetic tone and use a clinical, technical manual style only." thus stripping away the "social" layer that often leads to the hallucinations often encountered. Utilize "Top-K and Top-P sampling parameters. In short, follow a Rigid Verification Protocol: Question: 

+1  curiousjim

I’m afraid AI will label me some sort of “missing link” in the chain.

If I got this correctly, quantum mechanics says being in two places at once is a natural phenomenon; so much for the old aphorism.

As an engineer I applaud the technical accomplishments that are being laid at our feet, but my practical (?) nature is still mystified by it all.

It’s tempting to rely on the wisdom of Mr. Natural, when questioned about the meaning of diddy-wah-diddy: “if you don’t know by now, don’t mess with it!”

@jpag 

ChatGPT is impressed with your comments.