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AI Engineer Reading List
- nickpsecurity 2y agoThis is a great survey. Combine it with the courses below for best results: https://www.trybackprop.com/blog/top_ml_learning_resources https://www.trybackprop.com/blog/top_ml_learning_resources
- joshdavham 2y agoAwesome list!
- kamikazeturtles 2y agoI don't know what an "AI Engineer" is, but, is reading research papers actually necessary if the half life of the relevancy of many of these papers is only a few months until the next breakthrough happens? I have a feeling, unless you're dabbling at the cutting edge of AI, there's no point in reading research papers. Just get a feel for how these LLMs respond then build a pretty and user friendly app on top of them. Knowing the difference between "multi head attention" and "single head attention" isn't very useful if you're just using OpenAI or Groq's API. Am I missing something here? I'd love to know where I'm wrong
- feznyng 2y agoThere’s research papers on system design involving llms that would definitely be useful in practical contexts.
- eKIK 2y agoI consider myself to be an (occasional) user of AI services like the ones OpenAI and others provide. I've learned how to consume the services reasonably effectively, and make good use of them, but that's about it. I am not an AI engineer. Similarly I know how to call cryptography libraries to get my passwords hashed using a suitable cipher before storing them. I don't understand the deep math behind why a certain cipher is secure, but that's fine. I can still make good use of cryptographic functions. I'm not a cryptography engineer either :). My take on it is that if you should call yourself any kind of "XYZ Engineer", you should be able to understand the inner workings of XYZ. This reading list is most likely (mostly) for those who want to get a really deep understanding and eventuellt work on contributing to the "foundational systems" (for a lack of a better word) one day. Hope that helps.
- swyx 2y agoi mostly agree w you, but theres a wide spectrum of “understand the inner workings” given rising complexity. consider: - does a React/frotnend engineer need to know everything about react internals to be good at their job? - does a commercial airline pilot need to know every single subsystem in order to do their job? - do you, a sophisticated hackernewsian, really know how your computer works? more knowledge is always (usually) better but as a thing diffuses into practice and industry theres a natural stopping point that “technician” level people reach that is still valuable to society bc of relative talent supply and demand.
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- mettamage 2y agoI personally found that people need to understand the layer of the stack they're working on (e.g. a frontend dev should understand React). Going a layer higher or lower (or two) seems only to be handy for troubleshooting, debugging or you're simply having an expanded role.
- torginus 2y ago> - does a React/frotnend engineer need to know everything about react internals to be good at their job? Yes? Well, not everything (which I define as being able to implement React from scratch). But if you want to do good work, and be able to fix those pesky bugs which result from the arcane behavior of the framework itself, then you better know your stuff. Besides, in practice very few people understand the most basic stuff about React. Just recently I had to explain to a veteran frontend dev what list virtualization was and why it's not a good idea to display a list of 100k items directly.
- bronco21016 2y ago>- does a commercial airline pilot need to know every single subsystem in order to do their job? Haha explain this one to the APDs (aircrew program designee, the people signing off training at airlines) please. Every airline pilot has their horror stories of being asked how many holes are in the alternate static port of some aircraft they've flown. Or through bolts on the wheel hub, or how many plys of glass on the side cockpit window, or the formula for calculating hydroplane speed, or the formula for calculating straight line distance to the horizon from altitude of X... it just goes on endlessly. I do agree with your post overall though.
- FanaHOVA 2y agoWe covered that too: https://www.latent.space/p/ai-engineer https://www.latent.space/p/ai-engineer
- randcraw 2y agoA nice overview. In short, I would describe an AI Engineer as someone who integrates existing AI tools and libraries into a reliable system that is fielded in a production environment. They also must know how to tune AI components and assess them for performance, decline/drift, and failure. Most AEs have a MS or less (CS, data science, statistics, etc), since it's not really a research role. Finally, AI Engineers don't invent AI tools or fix what's missing/broken within them. That's the role of an AI scientist.
- _ea1k 2y agoI think you need to have a pretty good understanding of the underlying workings of at least a couple of modern models. You should also follow along closely enough to see if major changes happen in the way they are built. But watching every new paper? Nah, that's mostly only useful if you have a large enough amount of compute to try them out. And most of us don't have that anyway.
- lolinder 2y ago> I don't know what an "AI Engineer" is, but, is reading research papers actually necessary Let's put it this way: if even half the people who call themselves "AI Engineers" would read the research in the field, we'd have a lot less hype and a lot more success in finding the actual useful applications of this technology. As is, most "AI Engineers" assume the same thing you do and consider "AI Engineering" to be "I know how to plug this black box into this other black box and return the result as JSON! Pay me!". Meanwhile most AI startups are doomed from the start because what they set out to do is known to be a bad fit.
- wnmurphy 2y ago> I know how to plug this black box into this other black box and return the result as JSON! To be fair, most of software engineering is this.
- otteromkram 2y agoOkay, now take a slightly imbalanced stance: What is most software engineering?
- dietr1ch 2y agoI don't know, but if I say it's about working with things you don't fully understand people seem to trust me.
- bumby 2y agoTbf- most of [any] engineering is like this.
- richardw 2y agoThe terms are being diluted hard. Everyone from a plumber with a no-code RAG automation tool to a FAANG phd can and have claimed the role “AI engineer”, and it can be true depending on the context.
- jimbokun 2y agoThe new “Data Scientist”!
- richardw 2y agoI think AI blurs it more than ever, as it becomes easier to use. A chat makes any rando a superhero in the right context, if others don’t know it. Eg a 90 year old in a care home. You learn the ins and outs of any service, you’re the local expert. You don’t even need Excel, just a phone. Very many people who have never heard of deep learning have built chatbots for small retail shops. Drag in a few FAQ docs to the context store and click “go”.
- swyx 2y agohi! author here. > Just get a feel for how these LLMs respond then build a pretty and user friendly app on top of them. as you know, "just" is a very loaded word in software engineering. The entire thesis of AI Eng is that this attidude of "just slap a UI on an LLM bro whats so hard" is a rapidly deepening field, with its own stack and specialization (which, yes, some if not much of which is unnecessary, vc funded hypey complexity merchantism, but some of which is also valid), and if you do not take it seriously, others will, and do so running rings around those who have decided to not even try to push this frontier, passively waiting for model progress to solve everything. i've seen this play out before in underappreciated subfields of engineering that became their own thing, with their own language, standard stack, influencers, debates, controversies, IPOs, whole 9 yards.... frontend eng, mobile eng, SRE, data eng, you name it. you just have to see the level and quality of work that these people are doing that is sufficiently distinct from MLE and product/fullstack webdev to appreciate that it probably deserves its own field of study, and while it will NEVER be as prestigious as AI research, there will be a ton more people employed in these roles than there can be in research and thats a perfectly fine occupation too. I'm even helping instruct a course about it this week as it happens if you want to see what a practical syllabus for it looks like https://maven.com/noah-hein/ai-engineering-intro https://maven.com/noah-hein/ai-engineering-intro
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- samstave 2y agoKnow the terminology so you can discern the flow... knowing the flow wil helo you orchestrate agentic compute, to anticipative memory.. for precursor compute
- belter 2y agoAt least for 60% of those papers you can't reproduce the results....
- thecupisblue 2y agoThere is multiple levels of AI engineers: 1. The actual deep ML researchers that work on models 2. The "AI engineer" who creates products based on LLM's 3. The "AI researchers" who basically just stack LLM's together and call it something like Meta-Cognitive Chain-of-Thought Advanced Reasoning Inteligence or whatever it is.
- otteromkram 2y agoWhy are there two research branches? Seems like there's one AI engineer, which is b. The other two are researchers, one doesn't even focus on AI since ML covers a broader swath of disciplines.
- jhanschoo 2y ago> 1. The actual deep ML researchers that work on models > 3. The "AI researchers" who basically just stack LLM's together and call it something like Meta-Cognitive Chain-of-Thought Advanced Reasoning Inteligence or whatever it is. I actually think that working purely within the traditional neural nets model is starting to hit against its limits and the most fruitful directions for research are systems that incorporate and modify LLMs on-line, among other systems, despite your unserious characterization of this class of research.
- beanjuiceII 2y agoall those PM's and middle layers are going to be an "AI Engineer" soon, so now you know
- ocular-rockular 2y agoYou're not necessarily wrong but maybe a bit naive about what cutting edge research entails.
- paulddraper 2y agoAn AI Engineer is someone who builds AI products, just as a Software Engineer is someone who builds software products. Hope that helps!
- swyx 2y agohi! author here! putting together a list like this is intimidating - for every thing i pick there are a dozen other suitable candidates so please view this as a curriculum with broadly prescriptive weightings, with the understanding that the CURRENTLY_RELEVANT_PAPER is always a moving pointer rather than fixed reference. we went thru this specific reading list in our paper club: https://www.youtube.com/watch?v=hnIMY9pLPdg https://www.youtube.com/watch?v=hnIMY9pLPdg if you are interested in a narrative version.
- andrekorol 2y agoI appreciate the effort put into curating and maintaining this list, good job on that! I’m curious, is there also some specific existing “AI Researcher Reading List” you would personally recommend? Or do you plan on making and maintaining one?
- swyx 2y agoim no researcher so no, but i would start with the llm courses at stanford, uc berkeley, stanford, and princeton.
- adamgordonbell 2y agoGreat work! I am out of my depth when it comes to reading papers, but I second 'The Prompt Report' from your list. It gives a great taxonomy that helped me understand the space of prompting techniques better.
- swyx 2y agohe was great on the pod too https://www.latent.space/p/learn-prompting https://www.latent.space/p/learn-prompting
- fancyfredbot 2y agoHey Swyx, this list of papers is really interesting - I like that it's an opinionated list. Why isn't there more about model implementation though? I'd have expected engineers in this field want to know pytorch, cuda, JAX, maybe even XLA?
- bbor 2y agoHeh, always fascinating to see how the term “AI” has been swallowed nigh-completely by the recent exciting developments in DL. All those papers and not a single mention of Russell & Norvig, Minsky, Shannon, Lenat, etc.! I’m sure it’s a great list for what it is, I just wanted to be pedantic for a bit ;). If you’re interested in an introduction to AI as a broader topic, most graduate courses use the same book (Russel & Norvig) and others may publish their syllabi online.
- swyx 2y agoi’ll go even further, i dont include attention is all you need in my list. i think theres always a necessary intellectual history, but sometimes when you just want to do a specific job, you just want the current thing, and you can backfill the older stuff later (which yes can bite you in the ass if you are ignorant)
- kevin0091 2y agoThe reading list is old about one year, for instance in 2025, one may use KTO for math, RLOO for CoT, DPO for function calling and optimization. In 2025 one should only focus should be distillation & optimization. In 2025 CoT is not new, the corrected CoT is the key and all you need.
- ofou 2y agoI believe that most of the papers presented here focus on acquiring knowledge rather than deep understanding. If you’re completely unfamiliar with the subject, I recommend starting with textbooks rather than papers. The latest Bishop’s "Deep Learning: Foundations and Concepts (2024)" [1] is an excellent resource that covers the "basics" of deep learning and is quite updated. Another good option is Chip Huyen’s "AI Engineering (2024)" [2]. Another excellent choice will be "Dive into Deep Learning" [3], Understanding Deep Learning [4], or just read anything from fast.ai and watch Karpathy's lectures on YouTube. [1]: https://www.bishopbook.com https://www.bishopbook.com [2]: https://www.oreilly.com/library/view/ai-engineering/9781098166298/ https://www.oreilly.com/library/view/ai-engineering/97810981... [3]: https://d2l.ai https://d2l.ai [4]: https://udlbook.github.io/udlbook/ https://udlbook.github.io/udlbook/
- ewuhic 2y agoIs there a textbook like [1] or [4], which also incorporates PyTorch into learning?
- ofou 2y agoDive into Deep Learning is implemented using various libraries such as PyTorch, NumPy/MXNet, JAX, and TensorFlow. Here’s an example: https://d2l.ai/chapter_natural-language-processing-pretraining/word2vec-pretraining.html https://d2l.ai/chapter_natural-language-processing-pretraini...
- ewuhic 2y agoThis resource is not as profound compared to the other 2, unfortunately.
- drdude 2y agoI thought the same too... I read Deep Learning by Goodfellow and Deep Learning with TensorFlow 2 and Keras for practical stuff. I am still thinking if I should do the D2L for additional practice in my free time, though.
- lxe 2y agoI think most of the instruction fine-tuning methods for oss models stem from Alpaca, so it should be included: https://crfm.stanford.edu/2023/03/13/alpaca.html https://crfm.stanford.edu/2023/03/13/alpaca.html And the one referenced in there on synthetic data generation: https://arxiv.org/abs/2212.10560 https://arxiv.org/abs/2212.10560
- qrsjutsu 2y agoIf you are not on that hype-train, yet, then don't waste time skimming over, reading and understanding any LLM and AI papers. Read about ELIZA. Build your own. Get Tensors, Vectors, Fields, Linguistics, Computer Architectures, Networks. Focus on the subjects themselves, not them in the context of Neural Networks, "Deep Learning" et al.
- fancyfredbot 2y agoThe list seems deliberately focussed on learning practical topics and not the foundations. I think that's what makes it interesting - there are any number of places recommending you start by learning linear algebra, stats, probability... Personally I like to learn the foundations but there's genuinely room for useful knowledge of SOTA techniques even without the foundations. To be honest I feel that any amount of learning about computer architecture and vector fields is unhelpful if you are trying to understand good eval benchmarks or prompt engineering techniques.
- qrsjutsu 2y ago> unhelpful if you are trying to understand good eval benchmarks or prompt engineering techniques. You are absolutely correct. I jumped to conclusions when I saw the list and read "AI Engineer". The reading list isn't addressing people who want to build AIs, but those who want to maximize and optimize their results with the existing ones. My bad.
- francomoon7 2y agowhat is ELIZA? github link?
- jamalaramala 2y agoFrom the article: > 1. GPT1, GPT2, GPT3, Codex, InstructGPT, GPT4 papers. Self explanatory. (...) > 2. Claude 3 and Gemini 1 papers to understand the competition. (...) > 3. LLaMA 1, Llama 2, Llama 3 papers to understand the leading open models. (...) I agree that you should have read most of these papers at the time, when they were released, but I wonder if it would be that useful to read them now. Perhaps it would be better to highlight one or two important papers from this section?
- agcobiledyalom 2y agoAm interested