Is there any AI that can actually read through datasheets? - Page 1
Forum section: electronics / projects

author: Pkoff123
author_rank: Contributor
timestamp: July 14, 2025, 09:25:25 pm
content: No versions of chatgtp can. It will hallucinate completely fake data most than half the time. It even gives fake pages and quotes when I ask it were did it get the info from.. I could be a Rockstar engineer and get projects done like 5x faster if it could just do this one simple thing accurately.. Is there an option to turn on attention to detail??

author: tggzzz
author_rank: Super Contributor
timestamp: July 14, 2025, 09:57:05 pm
content: Quote from: Pkoff123 on July 14, 2025, 09:25:25 pmNo, none can.Understand that all they do is to create grammatically correct sentences of words that arestatisticallylikely to be found together in similar documents. Think of it as word salad:superficiallyrelevant collections of concepts, assembled without understanding.When people do that they are called bullshitters.N.B. Even human bullshitters occasionally make sense.

author: Pkoff123
author_rank: Contributor
timestamp: July 14, 2025, 10:00:58 pm
content: So its all bullshit? All these billions invested by everyone is for nothing??

author: TimFox
author_rank: Super Contributor
timestamp: July 14, 2025, 10:12:23 pm
content: Even when not hallucinating, errors can occur.Recently, I looked at the AI summary on Google for a recent historical question.The first Google link below the initial summary was a legitimate story, but two years old, and it drove the response given by the summary.However, looking further down, I found Google links to more recent stories, where the actual situation had changed dramatically.

author: tggzzz
author_rank: Super Contributor
timestamp: July 14, 2025, 10:13:49 pm
content: Quote from: Pkoff123 on July 14, 2025, 10:00:58 pmUnderstand what they are and how they work, and financial motivations for hyping their capabilities.Then make your decision as to what they can and can't do well.

author: wraper
author_rank: Unknown
timestamp: July 14, 2025, 10:17:37 pm
content: Grok 4 can absolutely do it (if you upload it or it actually looks for it online), Grok 3 will do it as well but it's much less capable. What AI hallucinates from its AI model without looking into actual datasheet may well be a total BS.

author: Smokey
author_rank: Super Contributor
timestamp: July 14, 2025, 10:18:42 pm
content: The problem, as I understand it, is that the LLM is fundamentally trained on a vast database of texts. Most of which are not datasheets.So when you ask it to predict the next token based on the prompt "what is the memory page size of this microcontroller?", it's pulling from the training that has lots of references to words like "memory" "page" "microcontroller" and lots of sources that may not be accurate like reddit questions and other bullshit.So if you could manage to train an LLM predominantly on technical datasheets, it would probably do significantly better than modern stuff. But very few people would pay to use that, so it doesn't happen. The flip side of this is that the modern LLMs were trained on how to write code because there are way more programmers than hardware guys.

author: Analog Kid
author_rank: Super Contributor
timestamp: July 14, 2025, 10:25:46 pm
content: Quote from: Smokey on July 14, 2025, 10:18:42 pmIt seems that the Techno-Lords of the Universe--thems who are pushing this stuff on us--coulddo that; i.e., create LLMs that are only trained on reputable, reliable technical sources, not the flotsam and jetsam of the Intertubes. And wouldn't people pay some kind of premium for such a (presumably) better model?Although even here, I don't doubt that there would be plenty of room for hallucinations, no matter the quality of the training/resource material.

author: moffy
author_rank: Super Contributor
timestamp: July 14, 2025, 10:41:31 pm
content: It's nice to see them called what they are, LLMs. They have an inherent problem in that their internal structure is incredibly complex and indecipherable because of the nature of the training used to create it. They can be coherent for part of the time and then start producing nonsense and there is no way to tell what it will do. If you run into problems then you retrain, add complexity, or start again. It must be a frustrating area of research.

author: tggzzz
author_rank: Super Contributor
timestamp: July 14, 2025, 10:45:00 pm
content: Quote from: moffy on July 14, 2025, 10:41:31 pmPrecisely. Spot on.Determining "why" an LLM produced the output it did is, to use the frightening phrase "an active unresolved research topic".Those points were first "discovered" 40yearsago, with Igor Aleksander's WISARD.

author: tggzzz
author_rank: Super Contributor
timestamp: July 14, 2025, 10:48:54 pm
content: Quote from: Pkoff123 on July 14, 2025, 10:00:58 pmStart with this post in a different thread partly about datasheets.https://www.eevblog.com/forum/chatgptai/ai-in-low-level-embedded-systems/msg5929650/#msg5929650Read the references in this post:https://www.eevblog.com/forum/beginners/beginner-friendliness-of-rf-circuit-design-by-chris-bowick/msg5884570/#msg5884570

author: wraper
author_rank: Unknown
timestamp: July 14, 2025, 11:06:06 pm
content: Quote from: Smokey on July 14, 2025, 10:18:42 pmAI model does not contain actual data from those datasheets. Unless it reads the actual document, AI sort of pulls it out of its ass with a significant chance it to be true. You can train AI through the nose but it will still spew BS if it does not look into a real datasheet. That's why AI needs to actually read the datasheet to provide correct information. As with Grok I mentioned, you either upload it, or it can search for it online.

author: ejeffrey
author_rank: Super Contributor
timestamp: July 14, 2025, 11:06:22 pm
content: If you just ask any LLM chatbot a question like "what is the output offset voltage of an OP27" it's going to fail at all but the simplest queries about the most popular chips. Here it only has access to whatever parts of the training data (all of the internet) are intact and hidden within it's weights.If you use an interface where you can upload a PDF and ask questions about it, then they can do OK. It works best with the "biggest" (most expensive) version of whatever LLM you use. These will have the biggest context buffers, so much more able to handle queries that cover a large part of a large datasheet.I think now most of the LLMs out there can go and do internet searches based on your query and automatically scrape the results to feed in. In principle this makes option 1 above behave similar to option 2, but it's not super reliable so I wouldn't count on it working, just upload the data sheet.Even then the value is not that great. Sometimes it gives an answer a few seconds faster than ctrl-F. It wasn't sufficient for much that you couldn't solve with Ctrl F.

author: tggzzz
author_rank: Super Contributor
timestamp: July 14, 2025, 11:08:50 pm
content: Quote from: wraper on July 14, 2025, 11:02:56 pmLLMs take some snippets fromthisdatasheet, other snippets fromthatdatasheet. They then munge the snippets together, to produce an "average". LLMs cannot "read" anything, let alone "comprehend" anything.

author: wraper
author_rank: Unknown
timestamp: July 14, 2025, 11:15:54 pm
content: Quote from: tggzzz on July 14, 2025, 11:08:50 pmThe thing is, it does not have any of those snippets in it's data. In other words, the data itself isn't embedded in the model. It being trained on the data does not mean any of it is present in AI model.

author: tggzzz
author_rank: Super Contributor
timestamp: July 14, 2025, 11:16:28 pm
content: Quote from: wraper on July 14, 2025, 11:06:06 pmAh yes, Grok.https://www.politico.com/newsletters/digital-future-daily/2025/05/15/groks-white-genocide-glitch-and-the-ai-black-box-00352709https://gizmodo.com/grok-ais-funniest-tweets-about-white-genocide-in-south-africa-2000602452https://www.bbc.co.uk/news/articles/c4g8r34nxenoMusk's response: retrain Grok using sources Musk thinks are clean and correct. Yeah right.https://www.businesstoday.in/technology/news/story/too-much-garbage-elon-musk-plans-to-retrain-grok-ai-to-retrain-human-knowledge-481486-2025-06-23

author: Pkoff123
author_rank: Contributor
timestamp: July 14, 2025, 11:18:19 pm
content: I have been uploading datasheet PDFs to the most exspesive chat gtp version and it still hallucinates all the time

author: Pkoff123
author_rank: Contributor
timestamp: July 14, 2025, 11:19:13 pm
content: I have been uploading pdfs to the 20p doller chat gtp version, even tried the deep resetch button..

author: Pkoff123
author_rank: Contributor
timestamp: July 14, 2025, 11:19:50 pm
content: How do u know it can do it?

author: wraper
author_rank: Unknown
timestamp: July 14, 2025, 11:20:29 pm
content: Quote from: tggzzz on July 14, 2025, 11:16:28 pmAh yes, answering with something absolutely irrelevant to the issue, not to say about a previous much dumber version that was asked to perform this way by a particular user, it was not a glitch but not enough filtering of what it's allowed to say when requested to say certain things.

author: tggzzz
author_rank: Super Contributor
timestamp: July 14, 2025, 11:21:56 pm
content: Quote from: wraper on July 14, 2025, 11:15:54 pmWell.. yes and no. Yes: strictly you are right. No: the snippets are there as anunknowablecombination of weighting factors.It is a bit like a grain of sand not containing any specific property that causes the sand in the bottom of an egg timer to have a half angle of ?35? degrees. Nonetheless, the half angle emerges somehow.

author: tggzzz
author_rank: Super Contributor
timestamp: July 14, 2025, 11:23:27 pm
content: Quote from: wraper on July 14, 2025, 11:20:29 pmI'm not really sure what you are claiming, but the fundamental operation hasn't changed.

author: tggzzz
author_rank: Super Contributor
timestamp: July 14, 2025, 11:26:00 pm
content: Quote from: Pkoff123 on July 14, 2025, 11:19:50 pmHow does who know what can do what?Hint: use the "quote" button above each post.

author: wraper
author_rank: Unknown
timestamp: July 14, 2025, 11:26:25 pm
content: Quote from: tggzzz on July 14, 2025, 11:23:27 pmI'm not sure how some unrelated sensationalist clickbait is supposed to mean anything in regard of AI's capability of reading the datasheets. About which I said it needs to read the actual datasheet, not hallucinate datasheet's contents out of itsassAI model.

author: Pkoff123
author_rank: Contributor
timestamp: July 15, 2025, 12:57:14 am
content: Why cant it read datasheets though? I really dont get it, I upload them and everything..

author: Smokey
author_rank: Super Contributor
timestamp: July 15, 2025, 01:19:09 am
content: Quote from: wraper on July 14, 2025, 11:15:54 pmwell, it sort of does have all the data embedded in the model in one way or another. The model is a sort of lossy compression of all the data it trained on.

author: temperance
author_rank: Super Contributor
timestamp: July 15, 2025, 01:56:18 am
content: I discovered white text on a white background in a recent data sheet so that AI can't read it.

author: Ian.M
author_rank: Super Contributor
timestamp: July 15, 2025, 02:06:19 am
content: Quote from: Smokey on July 15, 2025, 01:19:09 amWhat its got is a whole lot of probabilities that a word or phrase will be associated with another one, on some scale ranging from directly following to vaguely nearby + some algorithms to regurgitate words and phrases in a reasonably grammatically correct format. Believing that meaningfully represents the whole training data is like believing the Oxford English Dictionary is a compressed form of the Complete Works of Shakespeare!

author: UnijunctionTransistor
author_rank: Frequent Contributor
timestamp: July 15, 2025, 03:39:07 am
content: AI still follows that famous IT dictum: garbage in, garbage out.

author: Buriedcode
author_rank: Super Contributor
timestamp: July 15, 2025, 04:39:25 am
content: Counter point (that admittedly isn't helpful for this specific topic)... why do you need an AI to read a datasheet when you can just read it yourself? Datasheets are references - you refer to it when you require specific information about a device. Every document reader has a search function, and by the time you've requested one of the LLM's to do it, surely you can just as quickly and easily find the relavent info yourself?I do beleive they have their place in searching certain texts, and summarising things, but datasheets aren't verbose historical documents, and finding the releveant information is often very easy, as that is thepointof a datasheet.Edit: typos

author: Kleinstein
author_rank: Super Contributor
timestamp: July 15, 2025, 08:23:03 am
content: One may want something like an AI to scan datasheets for something like building data-bases, like the parametric search options at distribtors. So this would be something like looking for the main properties for some 10000 chips.It may need a somewhat special trained AI as most of the data are in tables and not normal text.With a now reduced number of main chip manufacturers it would however be better to get most of the data directly in a machine readable form from the manufacturers.

author: booscrawl
author_rank: Contributor
timestamp: July 15, 2025, 08:56:19 am
content: Quote from: Pkoff123 on July 15, 2025, 12:57:14 amI threw my datasheet in the garbage can. Why didn't the garbage can eject a working PCB?

author: BadeBhaiya
author_rank: Regular Contributor
timestamp: July 15, 2025, 08:59:03 am
content: No, none can do it well enough. The only case where they are usable is when you have a small text only datasheet.In many datasheets the pure text portions are quite long which go beyond the context length of LLMs, making it not consider the given text. Moreover all the images in the datasheets must be converted to a format an LLM can understand (called a multimodal projector), which takes orders of magnitudes more context space.So when you upload your PDF datasheet to an online service, they usually process as much text as they can, then discard the rest. They don't even try to process the images because they take a lot of processing power and context space.Maybe one day...but not right now

author: tszaboo
author_rank: Super Contributor
timestamp: July 15, 2025, 09:12:14 am
content: Quote from: Buriedcode on July 15, 2025, 04:39:25 amSo you can upload maybe 50 datasheets and it can sort through the vendor specific crap and provide data in a nice structured way. There are valid reasons to do it. I had limited success with some of these tools.Best would be to have an actual workspace where you can upload stuff, and run the same analysis multiple times, instead of this really bad "chat" itnerfaces.

author: Siwastaja
author_rank: Super Contributor
timestamp: July 15, 2025, 09:12:38 am
content: Quote from: Analog Kid on July 14, 2025, 10:25:46 pmBut what value does this bring to the table? If you need to know, say, voltage rating of a MOSFET or page size of microcontroller flash, then surely you can just look it up in that reputable, reliable technical source - the datasheet.OTOH, by digesting all random Reddit discussions etc. LLM gets more practical knowledge. It is not unheard at all that the answer is found in some of those discussions. Large datasets are advantageous for complex questions because you don't know beforehand where the answer lies.Which, again, makes AI a "better Google". And it's very usable when you are unable to find the answer yourself, as a last resort, hoping that your prompt to LLM triggers it to output that tiny critical piece of information found in a random Reddit thread, combined with another critical piece of information found on ST's forum, which you were unable to find with a normal Google search, because, to be frank, Google search sucks.And just like Google always returns 10000000 random pages which do not contain your answer, AI hallucinates. But maybe the answer is there; your call to find it.If you try to apply this pattern all the time, asking questions you should be solving yourself, checking AI's answers and fixing them, you are going to see a huge productivity drop. This is how most people seem to use AI now, with decreased productivity as a result.

author: sleemanj
author_rank: Super Contributor
timestamp: July 15, 2025, 09:15:29 am
content: What datasheet, what are you asking, andhoware you asking it.The how, the prompt, is a very important consideration to getting good results (in any interaction with an AI).

author: tggzzz
author_rank: Super Contributor
timestamp: July 15, 2025, 09:31:55 am
content: Quote from: UnijunctionTransistor on July 15, 2025, 03:39:07 amGiven LLMs are trained on whatever they can find/steal on the internet, and that many sites are now LLM generated slop, that dictum becomes "LLM slop in, LLM slop out".Yes, that does seriously worry expert researchers.

author: tggzzz
author_rank: Super Contributor
timestamp: July 15, 2025, 09:34:35 am
content: Quote from: Buriedcode on July 15, 2025, 04:39:25 amThat can usefully be generalised to note that searching for and reading original sources is better than reading LLM slop.Yes, original sources can be crap, but getting an LLM to digest that crap does not make it less crap.

author: tggzzz
author_rank: Super Contributor
timestamp: July 15, 2025, 09:36:45 am
content: Quote from: sleemanj on July 15, 2025, 09:15:29 amPlus the subsequent prompt(s) to tell the LLM that something it emitted is false and/or irrelevant.

author: Zero999
author_rank: Super Contributor
timestamp: July 15, 2025, 09:47:48 am
content: Google seems to be fairly decent:Q: "What is the masimum voltage rating of the NE555?"A: The NE555 timer IC has a maximum supply voltage (VCC) rating of 16V. While some sources specify a range of 4.5V to 16V, others state 2V to 18V, the absolute maximum rating for the VCC is 16V."Q: "What is the offset voltage of the LM358?A: "The input offset voltage of the LM358 operational amplifier is typically 2mV, but can be as high as 7mV. This is the voltage that needs to be applied to the input to bring the output to zero. Different grades of LM358 (like LM358A or LM358B) might have slightly different specifications, but the 3mV maximum offset is a common value."Q: "What is the input noise voltage of the TL072?"A: "The input noise voltage of the TL072 op-amp is typically 18 nV/√Hz at 1kHz. This value represents the noise voltage per root Hertz, indicating the noise level at a specific frequency."The last one regarding the TL072 is partly right. According to the TI data sheet:"18 nV/√Hz for All PS and NS packages, all TL07xM devices""The TL07xH and All other devices 37 nV/√Hz"https://www.ti.com/lit/ds/symlink/tl072.pdfSo be careful, especially when there are multiple variants of a part number, but that applies when looking at a data sheet, irrespective of whether doing do manually or with AI assistance.

author: tggzzz
author_rank: Super Contributor
timestamp: July 15, 2025, 10:00:42 am
content: Quote from: Zero999 on July 15, 2025, 09:47:48 amPresumably no better than actually reading the datasheet?Don't forget the general observed result that in order to be able to detect an LLM error/hallucination, you have to know more than the LLM.Don't forget the general result that LLM output looks good to people who don't know the subject, but people who do know the subject spot the flaws.Conclusion: LLMs are superficially good, but will lead to an idiocracy. Classic repeated example: lawyers suffering "career limiting" consequences because their submission included non-existent case law.

author: Siwastaja
author_rank: Super Contributor
timestamp: July 15, 2025, 10:12:30 am
content: Quote from: Zero999 on July 15, 2025, 09:47:48 amFinding the right datasheet, right variant, and right footnotes is already challenging enough for a human. Adding an intermediate step which loses information and just answers "something" is a disaster, and I really can't see upsides.Now the interesting prompt would be "please download datasheets of all opamps that cost less than 2USD in qty 100 at Digikey and Mouser and make a spreadsheet which lists offset voltage and maximum supply voltage for each". And this doesn't even need to be error-free as it would compete against the human-generated parametric distributor searches which are known to be full of false information anyway. You would be checking the data anyway just like you have to do now. Worst that can happen is ignoring otherwise good candidates, which already happens.More specifically, "find me a switch mode converter which has pulse-by-pulse current limit which can operate indefinitely without entering hickup or latching fault mode, enabling use as constant current supply without extra components". I spent like at least 8 hours with this task 10 years ago. When LLM can do this in minutes, it will be an actualrealuseful job.And they can already do some jobs like this. Choose wisely how to use them.Then again, going throughhundredsof switch mode converter datasheets made me learn something and get a "grasp" of the market. I trained my neurons with this data. So, choose wisely!

author: HwAoRrDk
author_rank: Super Contributor
timestamp: July 15, 2025, 10:38:45 am
content: As an experiment, the other day I tasked GitHub Copilot (using GPT-4.1) with recommending me some buck regulators with some specific requirements (synchronous, SOT23-6, 0.8 Vref, particular pin-out, etc.). It utterly failed.Initially, it came back with a bunch of TI parts, a couple of Richtek, and a couple of MPS. Upon inspection,allof the TI parts did not in fact feature a 0.8V Vref. When queried about this mismatch with the stated requirements, it justified it by claiming "ambiguous requirements", that "such requirements can typically be relaxed" - as if I hadn't specifically stated it as a concrete requirement! - and that a 0.765V reference was "nominally 0.8V".Then, it turns out the first Richtek part had an incompatible pin-out, and the other straight up didn't exist and was a hallucination. Finally, the first MPS part was the wrong package (8 pin) and also NRND.Asked if it could provide any further recommendations, it went on to claim that "no currently available, mainstream synchronous buck regulator meets ALL these criteria". I had been looking over DigiKey, etc. and knew this to be false. When challenged on this, and "what about X, Y, or Z parts?" it gave some nonsense about how it can recall details for things when referenced, but because it doesn't have access to "exhaustive distributor and manufacturer databases", it can't provide a comprehensive overview of what's on the market, and that it must "prioritize vendors with large international presence and high global availability" - as if it hadn't just completely ignored major names like Diodes, ON Semi, Analog, A&O, etc!

author: tggzzz
author_rank: Super Contributor
timestamp: July 15, 2025, 10:43:27 am
content: Quote from: Siwastaja on July 15, 2025, 10:12:30 am... can't see any upsides. Spot on; agreed.... generate a list of possible options for me to manually assess. That is plausible, but I don't see it being better than the DigiKey parametric searches.... learning the territory by manually going through many datasheets. Again, agreed. I remember learning about capacitors that way.Unfortunately LLMs will not be used carefully and wisely.Unfortunately LLMs will be seen to be good by CEOs because (1) they aren't subject experts (2) they are financially incentivised to believe them, (3) they are used to conversing in generalised platitudes. Ironically that makes CEOs a prime target for replacement by LLMs

author: tggzzz
author_rank: Super Contributor
timestamp: July 15, 2025, 10:45:10 am
content: Quote from: HwAoRrDk on July 15, 2025, 10:38:45 amSummary: the LLM emitted bullshit. No surprise there.What is surprising is that it wasn't fawningly apologetic when you pointed out its errors.

author: HwAoRrDk
author_rank: Super Contributor
timestamp: July 15, 2025, 10:59:33 am
content: Quote from: tggzzz on July 15, 2025, 10:45:10 amOh it was, but that's par for the course these days, so I didn't even mention it.

author: tggzzz
author_rank: Super Contributor
timestamp: July 15, 2025, 11:06:37 am
content: Quote from: HwAoRrDk on July 15, 2025, 10:59:33 amI hate such bullshit.Waitresses nowadays have to ask if you have any allergies. I reply "only bullshit", which usually raises a smile or "you won't get that here"

author: Siwastaja
author_rank: Super Contributor
timestamp: July 15, 2025, 11:32:48 am
content: Quote from: tggzzz on July 15, 2025, 10:43:27 amThis is where I have to disagree. Parametric searches by Digikey et al are not only full of wrong data, they have absurdly stupid classifications. Either the key parameter you are interested in simply isn't listed as all, or it's combined with 10 other parameters into one column with 1000 combinatorial options. LLM could read the datasheets, collect the obvious key numbers with at least the same accuracy as we currently do (the track record is so poor it's easy to improve upon), but also collect any arbitrary piece you are interested in which could appear under a slightly different name, in slightly different format.Data already in machine-readable standardized form -> no AI neededNot much data (e.g. <10 datasheets) -> no AI neededData in wildly varying forms, a lot of data -> preselection by AI is sensible; preselection and post-verification is neededanyhow.I have seen examples outside of electronic component selection where it can improve productivity already in such pre-selection process, but it seems that people have poor experience on pre-selection on electronics. Probably the training sets have had way too little electronics-related datasheets and appnotes such that the models do not "understand" the domain at all.

author: Zero999
author_rank: Super Contributor
timestamp: July 15, 2025, 11:52:46 am
content: Quote from: Siwastaja on July 15, 2025, 11:32:48 amPerhaps someone aught to make thier own parametric search engine. If they do it well, then they'll be able to make plenty of add revenue from semiconductor suppliers and distributors and perhaps even offer an add-free subscription service.

author: tggzzz
author_rank: Super Contributor
timestamp: July 15, 2025, 12:19:49 pm
content: Quote from: Siwastaja on July 15, 2025, 11:32:48 amDigikey and Mouser parametric searches arefarfrom perfect (and let's not consider RS/Farnell/CCL!). But you canseeandunderstandthe ways in which they are imperfect, unlike LLMs.That's an important benefit.

author: sleemanj
author_rank: Super Contributor
timestamp: July 16, 2025, 01:23:00 am
content: Quote from: HwAoRrDk on July 15, 2025, 10:38:45 amI asked Gemini, but I didn't ask for a specific pinout since you didn't say what you were looking for.Every one of it's recommendations are indeed 0.8v reference when I look at the datasheets.https://g.co/gemini/share/76ed3c0ffddb

author: HwAoRrDk
author_rank: Super Contributor
timestamp: July 16, 2025, 02:19:05 pm
content: Quote from: sleemanj on July 16, 2025, 01:23:00 amInteresting. I guess Gemini is probably more likely to have a broader dataset from which to draw on, given they probably trained it on content from Google's search index.I gave it the exact same prompt I did CoPilot, and it did a little better. All but one of its half-dozen initial suggestions were suitable, except one MPS part which had 0.923 Vref and was NRND. But it still flubbed and missed a whole bunch of parts from several manufacturers.When I asked it to consider several other manufacturers, it kind of went off the rails, first suggesting parts that were other packages (DFN mostly), then catching itself and saying they're not suitable, then suggesting other parts with non-matching pin-outs, and for one hilariously giving a datasheet link of "[suspicious link removed]".I further asked "what about...?" one part it so far hadn't mentioned that I know matches the requirements, and it incorrectly told me it wasn't suitable as the pin-out didn't match, because it had mis-interpreted the pin table in the datasheet.

author: meshtron
author_rank: Regular Contributor
timestamp: July 16, 2025, 02:38:35 pm
content: Interesting discussion! Some truth, semi-truth, and flat-out wrong stuff in here so far.There are 2 big issues with LLMs accurately processing datasheets:1) At their core, LLMs deal in TEXT, not images or tables or charts or diagrams. So, the first step when you upload a datasheet is it gets converted to raw text. This conversion process has been surprisingly bad for quite some time. There are LOTS of companies working on new, novel, or just largely improved ways to ingest PDFs with the goal of more completely and accurately converting PDF (and other mixed text/image documents) to LLM food.2) LLMs are all bound by their "context window" which is the maximum number of tokens (you can essentially think of a token as a word - not quite right but close enough) an LLM will let you process at once. Most models advertise to some degree their large context window, but it's kind of like fuel mileage - you can't actually use, in a productive way, all the context window that's advertised. Every time you send a request to an LLM, it re-ingests the entire conversation including all your inputs and all its outputs. So, in any given chat, the amount of context window you're using just keeps growing until you end that chat and start a new one. The result of this is that the quality of answers goes down the longer the conversation goes - often substantially so. And, by uploading a PDF of even tens of pages, you're immediately jumping pretty deep into that context window right out of the gate.So, when you drop a 50-page datasheet into an LLM, it first misinterprets what's in it because it gets tripped up on images/layouts/charts/etc., then you find yourself at the waning end of the useful context window the first time you ask a question. Long and short of it - it's MORE useful typically to use the AI baked into Adobe Acrobat to find/get specific information from a document (for now) than to try to have your LLM tool ingest the document unless it's trivially short. And, of course, the shorter and simpler the datasheet, the less value there is to having the LLM help you with it.All that said, both of these issues (and many more) are actively being solved. In a year (likely less) we'll have good, local, persistent memory (look up Google TITANS or RAG). I know I've seen (but can't find right now) several compelling improvements in PDF document ingestion to improve LLM accuracy as well.Where I see this going eventually (this is me speculating, nothing more) is that in the future, chip manufacturers will train and expose access to smaller LLMs trained specifically on their products. So, your datasheets will be (at least available as) AI models that can answer, guide, or output whatever information you need accurately. While it's true that LLMs are just doing probability math behind the scenes, the architecture is absolutely capable of producing responses with a level of accuracy and consistency that meet or exceed the average clarity of today's datasheets. But tripping over the 2 items above means it's all but impossible to usefully access that with consumer-grade LLMs today except in specific, limited circumstances.EDIT: Fixed typo

author: HwAoRrDk
author_rank: Super Contributor
timestamp: July 16, 2025, 03:01:37 pm
content: Yeah, part of the problem is the nature of PDFs, and nothing to do with LLMs.Extracting structured and semantically meaningful text from a PDF has always been an exercise in frustration, due to how text is represented in PDF files. PDFs excel at only one thing: representing how and where raw content should be laid out on a page. Where text is located in the structure of the PDF can have absolutely no relation to where it appears on the page. Text can be embedded and encoded in numerous different ways, many of which are non-trivial to parse. Text can appear differently than how it is embedded, due to embedded fonts, etc. Some text isn't even text, because it's in an embedded image or represented only as the geometric shapes of the letters.For example, there's no such thing as a "table" in a PDF. A table will be just a bunch of "place text here, here, and here, draw lines here", etc. That this represents a table can only be deduced through context and/or analysing the final rendered output.

author: Conrad Hoffman
author_rank: Super Contributor
timestamp: July 16, 2025, 04:59:32 pm
content: Quote from: ejeffrey on July 14, 2025, 11:06:22 pmI've had good results from Copilot, at least it sounds convincing.What is the output offset voltage for OP-27 opamp?Output Offset Voltage for the OP-27Input-Referred Offset VoltageThe OP-27 datasheet only specifies the input offset voltage, Vₒₛ, which—when used in a unity-gain configuration—is directly your output offset.OP27A/E grade: • Vₒₛ (typ) = 25 µV, Vₒₛ (max) = 30 µVOP27C/G grade: • Vₒₛ (typ) = 60 µV, Vₒₛ (max) = 100 µVClosed-Loop Output OffsetIn any feedback configuration the output offset is simply the input offset scaled by the closed-loop gain, G_CL:latexV_{out\_offset} = V_{os} \times G_{CL}Unity-gain buffer (G_CL = 1): V_out_offset = Vₒₛ → 25 µV typ, 30 µV max (A/E grade)Higher closed-loop gains multiply that figure accordingly.Additional ConsiderationsTemperature drift of Vₒₛ is specified at 0.2 µV/°C typical (max 1 µV/°C for A/E), so output drift = driftₒₛ × G_CL.If you need to trim out residual offset, the OP-27 provides Vₒₛ-adj pins and a 10 kΩ pot (±2.5 mV range) for manual nulling.For sub-microvolt output offset and near-zero drift, consider chopper-stabilized amplifiers (e.g., LTC2050, AD8628) which offer output offsets in the single-digit microvolt range.Edit in a pageWhat effects does output offset voltage have on circuit performance?How is output offset voltage affected by temperature?What are other opamps with low output offset voltage?

author: Siwastaja
author_rank: Super Contributor
timestamp: July 16, 2025, 06:17:31 pm
content: Quote from: HwAoRrDk on July 16, 2025, 03:01:37 pmBut exactly this crappy non-machine-readable nature of PDF documents is why weneeda freaking AI to parse them.It seems impossible that manufacturers ever could supply just the numerical and graph data in, for example, a csv file. It is probably easier to build a real AGI dystopy and 1000 fusion reactors to power it, locate in in Mars, and move there, than to get part manufacturers to supply opamp offset voltages in CSV file.

author: tggzzz
author_rank: Super Contributor
timestamp: July 16, 2025, 06:27:16 pm
content: Quote from: Siwastaja on July 16, 2025, 06:17:31 pmThe numerical offset voltage - and other specifications - is only part of the spec; just as important are the conditions under which the magic number is valid. Those conditions are likely to be too complex to fit into a standard format.

author: meshtron
author_rank: Regular Contributor
timestamp: July 16, 2025, 06:45:27 pm
content: Just as another data point, here is o3-pro's answer to the OP-27 op-amp question. I don't know anything about that part, but I did watch it pull the datasheet while answering. o3-pro generally gives great answers (in most cases) with the downside being it's expensive and it takes a while to answer. For my use cases, those are perfectly acceptable tradeoffs versus the time (and errors) it saves me.(Link is directly to the conversation so you can see my question and the answer - nothing weird)https://chatgpt.com/share/6877f2d5-bb4c-8008-b611-976729cfc574

author: TimFox
author_rank: Super Contributor
timestamp: July 16, 2025, 07:01:17 pm
content: Quote from: meshtron on July 16, 2025, 06:45:27 pmNote that in the real world, the output offset voltage is the (noise gain) x (input offset voltage), not the closed-loop gain.

author: meshtron
author_rank: Regular Contributor
timestamp: July 16, 2025, 09:30:13 pm
content: Quote from: TimFox on July 16, 2025, 07:01:17 pmFor completeness, I asked about Noise Gain too and updated the conversation in the link with o3's answer. I know nothing about Op Amps (nor are they directly relevant to anything I'm currently working on) but interesting discussion. I did enjoy that it subtly scolded me for not asking more well-formed questions in its answer about noise gain!

author: Siwastaja
author_rank: Super Contributor
timestamp: July 17, 2025, 06:13:07 am
content: Quote from: tggzzz on July 16, 2025, 06:27:16 pmI know, and this would be one of the excuses manufacturers would use if questioned about why they can't just dump the data as CSV.Yet, real electronics design is often "finding the needle from haystack". Grepping for parts with e.g. suitable supply voltage range and maximum output current (for example) would reduce the number of suitable parts from 10000 to 100, from which you can then go on looking for specifics (which need longer explanations than just numbers), or part-specific parameters.And we use parametric search tools for that. And poor human beings who classify these parts are now performing the enormous task of looking at PDF files and guessing something, quite often getting it wrong.I'm not even suggesting a "standard format" (which would be great of course, but I don't believe in miracles). Even just the dump of all tabulated data in whatever form it is in the PDF would be a start - low effort for manufacturer, big savings in tooling and computing power requirements for not needing to do graphical parsing. If some manufacturer said Vdd and other Vcc, you could still grep "Vdd\|Vcc".

author: tggzzz
author_rank: Super Contributor
timestamp: July 17, 2025, 09:05:35 am
content: Quote from: Siwastaja on July 17, 2025, 06:13:07 amIt would be nice, wouldn't it.Apart from parametric searches, probably the best hope is that the manufacturer has highlighted a particular use or exceptional parameter in the text description in the datasheet. That wouldn't be captured by a parametric search, but is the kind of thing an LLMoughtto be able to extract. (Me thinking an LLM might bebetter? What is the world coming to?)Mind you, gurgle search might also be able to spot such text, so maybe the world isn't ending

author: Alex Eisenhut
author_rank: Super Contributor
timestamp: July 17, 2025, 04:01:26 pm
content: Quote from: Siwastaja on July 16, 2025, 06:17:31 pmOr footprint dimensions referenced to pin centers...