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Honda: 2 years of ml vs 1 month of prompting - heres what we learned
- elzbardico 10mo agoAnd yet, the source problem still remains. The company has a shitty way of reporting quality issues in relation to parts and assemblies. Being an automaker, I can almost smell the silos where data resides, the rigidly defended lines between manufactures, sales and post-sales, the intra-departmental political fights. Then you have all the legacy of enterprise software. And the result is this shitty warranty claims data.
- drob518 10mo agoSilos are the root of all evil.
- serjester 10mo agoAs someone that also worked at a large automakers, I think you’re making large, unfounded assumptions. Warranty data flows up from the technicians - good luck getting any auto technician to properly tag data. Their job is to fix a specific customer’s problem, not identify systematic issues. There’s a million things that make the data inherently messy. For example, a technician might replace 5 parts before they finally identify the root cause. Therefore, you need some sort of department to sit between millions of raw claims and engineering. I would be curious what kind of alternative you have in mind?
- robocat 10mo ago> cut-chip “cut-chip” usually describes a fault where the engine cuts out briefly—as if someone flicked the ignition off for a split second—and the driver hears or feels a little “chip” or sharp interruption in power.
- a-dub 10mo agointuitively it has seemed that these kinds of "fuzzy text search" applications are an area where llms really shine. it's cool to see evidence of it working. i'm curious about some kind of notion of "prompt overfitting." it's good to see the plots of improvement as the prompts change (although error bars probably would make sense here), but there's not much mention of hold out sets or other approaches to mitigate those concerns.
- juancn 10mo agoIt could have been done via topic analysis without an LLM. In fact there are companies such as Medallia which specialize in CX and have really strong classification solutions for specifically these use cases (plus all the generative AI stuff for closing the loop).
- nostrebored 10mo agoThe topic modeling of every major vendor, mostly awful LDA implementations, is horrendous. On the order of +/-20% absolute percentage points per topic. It would make my life easier if it weren’t so shit. As is, in every customer we have to go in and do legitimate topic modeling and taxonimization. Their AI implementations are also awful. Just sample 100 contacts with someone who actually understands the business and see their reaction.
- swyx 10mo agothe blog doesnt actually say the word "honda" anywhere on here. would probably advise that
- elmigranto 10mo agoOr you can have a single checkbox “Problem with the vehicle”.
- Gormanu 10mo ago[dead]
- shubham_zingle 10mo agodayumnnn, this is interesting to say the least
- w10-1 10mo agoIt's worth highlighting the conditions under which this can help: > in domains where the taxonomy drifts, the data is scarce, or the requirements shift faster than you can annotate It's not actually clear if warranty claims really meet these criteria. For warranty claims, the difficulty is in detecting false negatives, when companies have a strong incentive and opportunity to hide the negatives. Companies have been trusted to do this kind of market surveillance (auto warranties, drug post-market reporting) largely based on faith that the people involved would do so in earnest. That faith is misplaced when the process is automated (not because the implementors are less diligent, but because they are too removed to tell). Then the backlash to a few significant injuries might be a much worse regime of bureaucratic oversight, right when companies have replaced knowledge with automation (and replacement labor costs are high).
- greazy 10mo agoI found this fun fact really fascinating: > Translating French and Spanish claims into German first improved technical accuracy—an unexpected perk of Germany’s automotive dominance. It brings up an interesting idea that some languages are better suited for different domains.
- 1970-01-01 10mo agoSo you're still ignoring that problem of putting the oil filter in places that cause excessive spilled oil? The point of artificial intelligence is to quickly have an unbiased check of all signal within the noise.
- NumberCruncher 10mo agoIt would have been interesting to see how an Elasticsearch like system performs on this task.
- yahoozoo 10mo agoI wonder if text embeddings and semantic similarity would be effective here?
- davidsainez 10mo ago> We tried multiple vectorization and classification approaches. Our data was heavily imbalanced and skewed towards negative cases. We found that TF-IDF with 1-gram features paired with XGBoost consistently emerged as the winner.
- killerstorm 10mo agoWell, "vectorization" can be anything. BERT is in same capability class as GPT, very different from LSA people did in 1980s...
- andai 10mo agoAnthropic found a similar result for retrieval: embeddings + BM25 keyword search (variant of TF-IDF) produced significantly better results. https://www.anthropic.com/engineering/contextual-retrieval https://www.anthropic.com/engineering/contextual-retrieval They also found improvements from augmenting the chunks with Haiku by having it add a summary based on extra context. That seems to benefit both the keyword search and the embeddings by acting as keyword expansion. (Though it's unclear to me if they tried actual keyword expansion and how that would fare.) --- Anyway what stands out to me most here is what a Rube Goldberg machine it is. Embeddings, keywords, fusion, contextual augmentation, reranking... each adding marginal gains. But then the whole thing somehow works really well together (~1% fail rate on most benchmarks. Worse for code retrieval.) I have to wonder how this would look if it wasn't a bunch of existing solutions taped together, but actually a full integrated system.
- davidsainez 10mo agoThanks for sharing! I am working on a rag engine and that document provides great guidance. And, agreed, each individual technique seems marginal but they really add up. What seems to be missing is some automated layer that determines the best way to chunk documents into embeddings. My use case is mostly normalized mostly technical documents so I have a pretty clear idea of how to chunk to preserve semantics. But I imagine that for generalized documents it is a lot trickier.
- stego-tech 10mo agoAnd this is where the strengths of LLMs really lie: making performant ML available to a wider audience, without requiring PHDs in Computer Science or Mathematics to build. It’s consistently where I spend my time tinkering with these, albeit in a local-only environment. If all the bullshit hype and marketing would evaporate already (“LLMs will replace all jobs!”), stuff like this would float to the top more and companies with large data sets would almost certainly be clamoring for drop-in analysis solutions based on prompt construction. They’d likely be far happier with the results, too, instead of fielding complaints from workers about it (AI) being rammed down their throats at every turn.
- Veliladon 10mo ago^ This. I'm waiting for an LLM where I can just point it to a repo, slurp it up, and let me ask questions about it.
- etothet 10mo agoThis is exactly what Devin (https://devin.ai https://devin.ai) is designed to do. Their deepwiki feature is free. I’ve personally had decent success with it, but YMMV.
- bildung 10mo agoApparently it's also shit. There was a discussion about it a few days ago that contains multiple project maintainers pointing out deepwiki didn't get their repos at all https://news.ycombinator.com/item?id=45884169 https://news.ycombinator.com/item?id=45884169
- cpursley 10mo agogithub copilot somewhat does this.
- ryandvm 10mo agoCopilot is too stingy with context. In my experience Claude Code is much better at seeing the big picture.
- pjc50 10mo agoCrucially, this is: - text classification, not text generation - operating on existing unstructured input - existing solution was extremely limited (string matching) - comparing LLM to similar but older methods of using neural networks to match - seemingly no negative consequences to warranty customers themselves of mis-classification (the data is used to improve process, not to make decisions)
- nerdponx 10mo agoI agree with you that the headline really needs to be qualified with these details. So there's an aspect of being unsurprising here, because that particular set of details is exactly where LLMs perform very well. But I think it's still an interesting result, because related and similar tasks are everywhere in our modern world, and they tend to have high importance in both business and the public sector, and the older generation of machine learning techniques for handling these tasks we're both sophisticated and to the point where very capable and experienced practitioners might need an R&D cycle just to conclude if the problem was solvable with the available data up to the desired standard. LLM's represent a tremendous advancement in our ability as a society to deal with these kinds of tasks. So yes, it's a limited range of specific tasks, and success is found within a limited set of criteria, but it's a very important tasks and enough of those criteria are met in practice that I think this result is interesting and generalizable. That doesn't mean we should fire all of our data scientists and let junior programmers just have at it with the LLM, because you still need to put together a good day to say, makes sense of the results, and iterate intelligently, especially given that these models tend to be expensive to run. It does however mean that existing data teams must be open to adopting LLMs instead of traditional model fitting.
- Moto7451 10mo agoWhich is good because a lot of such matching and ML use cases for products I’ve worked on at several companies fit into this. The problem I’ve seen is when decision making capabilities are inferred from/conflated with text classification and sentiment analysis. In my current role this seems like a very interesting approach to keep up with pop culture references and internet speak that can change as quickly as it takes the small ML team I work with to train or re-train a model. The limit is not a tech limitation, it’s a person-hours and data labeling problem like this one. Given I have some people on my team that like to explore this area I’m going to see if I can run a similar case study to this one to see if it’s actually a fit. Edit: At the risk of being self deprecating and reductive: I’d say a lot of products I’ve worked on are profitable/meaningful versions of Silicon Valley’s Hot Dog/Not Hot Dog.
- pards 10mo ago> Over multiple years, we built a supervised pipeline that worked. In 6 rounds of prompting, we matched it. That’s the headline, but it’s not the point. The real shift is that classification is no longer gated by data availability, annotation cycles, or pipeline engineering.
- stogot 10mo agoThis was fun to read “ Fun fact: Translating French and Spanish claims into German first improved technical accuracy—an unexpected perk of Germany’s automotive dominance.”
- lfx 10mo agoIt really puzzles me how this is helping and how it was done? Does it make text more clear? How exactly? Does the German language is more descriptive? Does it somehow expands context? So many questions in this fun fact.
- happimess 10mo agoI wonder how they came up with that. Was it a human idea, or did the AI stumble upon it? Given that it was inside a 9-step text preprocessing pipeline, it would be surprising if the AI had that much autonomy.
- embedding-shape 10mo agoI think it's fairly known among "LLM practitioners" (or what to call it), that some languages are better at solving specific tasks. Generally if you find yourself in a domain dominated by research in language X, shifting your prompts to that language will give you better results.
- Upvoter33 10mo agoDid the author exactly define "Nova Lite" somewhere in there?
- xfalcox 10mo agoIt's the Amazon own model. I'm baffled someone would pick it, even more that someone would test Llama 4 for a task in an age where Sonnet 4.5 is already out, so in the last 45 days. Looks like they were limited by AWS Bedrock options.
- killerstorm 10mo agoHmm, why was their starting point not something like BERT: * already known as SotA for text classification and similarity back in 2023 * natively multi-lingual
- elzbardico 10mo agoBecause Enterprise Development only moves forward based on hyped technologies.
- deepsquirrelnet 10mo agoI love using encoder models, and they are generally a better technology for this kind of application. But the price of GPU instances is too damn high. I won’t lie that I’ve been unreasonably annoyed that I have to use a lot more compute than I need, for no other reason than an LLM API exists and it’s good enough in a relatively small throughput application.
- embedding-shape 10mo agoPeople generally sleep when you start talking about fine-tuned BERT and CLIP, although they do a fairly decent job as long as you have good data and know what you're doing. But no, they want to pay $0.1 per request to recognize if a photo has a person in it by asking a multimodal LLM deployed across 8x GPUs, for some reason, instead of just spending some hours with CLIP and run it effectively even on CPU.
- keeda 10mo ago>... as long as you have good data and know what you're doing. I think you've just identified, in a set-theoretic complementary manner, the TAM for GenAI.
- throwaway314155 10mo agoWhat's TAM?
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- datax2 10mo agoWarranty data is a great example of where LLMs have evolved bureaucratic data overhead. What most people do not know is because of US federal TREAD regulation Automotive companies (If they want to land and look at warranty data) need to review all warranty claims, document, and detect any safety related issues and issue recalls all with an strong auditability requirement. This problem generates huge data and operations overhead, Companies need to either hire 10's if not hundreds of individuals to inspect claims or come up with automation to make this process easier. Over the past couple of years people have made attempts with NLP (lets say standard ML workflows) but NLP and word temperature scores are hard to integrate into a reliable data pipeline much less a operational review workflow. Enter LLM's, the world is a data gurus oyster for building an detection system on warranty claims. Passing data to Prompted LLM's means capturing and classifying records becomes significantly easier, and these data applications can flow into more normal analytic work streams.
- jwong_ 10mo agoWish there was a bit more technical details in how the prompt iterations looked like. > We didn’t just replace a model. We replaced a process. That line sticks out so much now, and I can't unsee it.
- nerdponx 10mo agoIt didn't stick out to me because "corporate success story" articles already tend to sound like that, which is at least in part where I imagine the popular LLMs get it from. (The other part being pop nonfiction books.)
- keeda 10mo agoI dunno, ending with a short, punchy insight is a common way to make an impactful conclusion. It's the equivalent of a "hook" for concluding an article instead of opening. I do it often and see others (e.g. OpEds) use that tactic all the time. I think we're getting into reverse slop discrimination territory now. LLMs have been trained on so much of what we consider "good writing", that actual good writing is now attributed by default to LLMs.
- prasoonds 10mo agoRight? This one is also very clear ChatGPTese > That’s not a marginal improvement; it’s a different way of building classifiers. They've replaced an em-dash with a semi-colon.
- klabb3 10mo agoThey are really getting to the heart of the problem!
- Maxion 10mo agoYou're absolutely right! They didn't just replace an em dash with a colon, they invented a whole new way of speaking. /s if it wasn't obvious
- notanastronaut 10mo ago
- PaulHoule 10mo agoI'll note that they had a large annotated data set already that they were using to train and evaluate their own models. Once they decided to start testing LLMs it was straightforward for them to say "LLM 1 outperforms LLM 2" or "Prompt 3 outperforms Prompt 4". I'm afraid that people will draw the wrong conclusion from "We didn’t just replace a model. We replaced a process." and see it as an endorsement of the zero-shot-uber-alles "Prompt and Pray" approach that is dominant in the industry right now and the reason why an overwhelming faction of AI projects fail. If you can get good enough performance out of zero shot then yeah, zero shot is fine. Thing is that to know it is good enough you still have to collect and annotate more data than most people and organizations want to do.
- ghm2180 10mo agoI would offer a stronger more pointed observation, ofen the problem in building a good classifier is having good negative examples. More generally how a classifier identify good negatives is a function of: 1. Data collection technique. 2. Data annotation(labelling). 3. Classfier can learn on your "good" negatives — quantitaively depending on the machine residuals/margin/contrastive/triplet losses — i.e. learn the difference between a negative and positive for a classifier at train time and the optimization minima is higher than at test time. 4. Calibration/Reranking and other Post Processing. My guess is that they hit a sweet spot with the first 3 techniques.
- jacquesm 10mo agoI think the biggest problem with such classifiers is to actually know what is good data and what is bad data. To take a sample of the data and to recognize whether or not this dataset is a general enough representation of both true and false examples (for a binary classifier) to be able to use it to train a model. Because it isn't rare at all to have data sets that are biased 100 to 1 or more for one of the classes, which contain hints about what class the object is in that isn't in the object itself and so on. You can train until the cows come home on such data but it will never lead to satisfactory results.
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- mcdonje 10mo agoI get that SQL text searches are miserable to write, but it would have flagged it properly in the example. The text says, "...no leaks..." The case statement says, "...AND LOWER(claim_text) NOT LIKE '%no leak%...'" It would've properly been marked as a "0".
- _ea1k 10mo agoI thought the same. Having said that, the parenthesis in the example are really wrong for what they were trying to convey. I suspect that they built this sql sample for the document and made some mistakes in its generation. Perhaps I could say, it isn't just generated--it is also hallucinated!
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- Aniket-N 10mo agoOnce you start to recognize AI written, rewritten or even edited articles, it’s hard to stop. It’s not X it’s Y. We didn’t just do A we did B. There’s definitely a lot of hard work that has gone in here. It’s gotten hard to read because of these sentence patterns popping up everywhere.
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- kazinator 10mo agoIt is some kind of new Law (that ought to be named after someone) that people who write about AI are likely using it to do that writing. (Even ironically sometimes observed in cases when the writing is disparaging of AI and the use of AI). If the subject matter is AI, you should instantly pay attention and look for the signs it was AI assisted or generated outright.
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- StefanBatory 10mo agoTrue now. At the same time, as a nonnative speaker of English, this is literally how we were taught to write eye-catching articles and phrases. :P A lot of formulaic writing is what we were taught to do, especially with more formal things. (This is more of a sidenote to this example) So in a hunt for LLMs, we also get hit.
- Ancapistani 10mo agoI believe this is likely a consequence of how RLHF is done. I’ve not verified it, but I suspect the frontier model labs are outsourcing it to companies employing primarily non-native English speakers.
- StefanBatory 10mo agoBack in the days I've heard it's why delve is so popular; as it's common in Nigerian English. I learned it from MtG and I do believe it's a very cool word and I hate that I can't use it without people raising their eyebrows.
- esafak 10mo agoThe old model was capable of running on a CPU. The new one requires a GPU. This might be a consideration for some.
- PaulHoule 10mo agoHonda probably spends $100-$10,000 on a warranty claim in terms of technician time and parts. [1] Even at the low end they can afford to spend 10 cents on an LLM to analyze a claim. [1] specifically https://www.warrantyweek.com/archive/ww20230817.html https://www.warrantyweek.com/archive/ww20230817.html claims the expectation value of warranty claims for a car is around $650.
- Aurornis 10mo agoRunning big LLMs is expensive, but not nearly expensive as hiring people. Employees are very expensive, well beyond their wages that you see. Everything from employment taxes (employer paid) to hiring additional people to manage the people and their HR needs. Even if it took $10 to run everything to handle each request, that’s far cheaper than even a minimum wage employee when you consider all of the employment overhead.
- suddenlybananas 10mo agoI didn't read this very carefully so maybe I missed it, but I'm surprised they didn't try using a classifier on top of a BERT-style encoder model?
- DeathArrow 10mo agoTLDR: "old" ml techniques like XGBoost can beat LLMs and neural networks for some tasks.
- lmz 10mo ago...when given a 2 year head start.
- Workaccount2 10mo agoWould have been nice to have seen this done with the top models rather than something like Nova.
- cwmoore 10mo ago“hundreds, if not thousands…thousands”
- djoldman 10mo agoThree points to note: * "2 years vs 1 month" is a bit misleading because the work that enabled testing the 1 month of prompting was part of the 2 years of ML work. * xgboost is an ensemble method... add the llm outputs as inputs to xgboost and probably enjoy better results. * vectorize all the text data points using an embedding model and add those as inputs to xgboost for probably better results.