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If you were building a Q&A feature (or chatbot) based on very long documents (like books), what evals would you focus on?
chatbot evals documents
The the words it uses that this site has seen
least often elsewhere. Posts are matched on those words alone —
nothing here is a summary of this one.
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18 September
Machine learning engineer and independent AI consultant. He writes about LLM evaluation, tooling and applied ML at hamel.dev, and previously worked on machine learning at GitHub.
If you are a Data Scientist, you have never had more alpha than right now. Data Scientists email me all the time. It is no mistake that I'm focused on evals as a former DS. I talk more about this here: Related
Professor of economics at George Mason University and Bartley J. Madden Chair at the Mercatus Center. Co-writes the blog Marginal Revolution and co-authors the textbook Modern Principles of Economics with Tyler Cowen.
Jev uses AI to classify. Yes or No. A, B, C or D. For a lot of applications that beats a chatbot, and it appears much safer. A classifier has no agenda to push. Related
17 September
Non-fiction writer, podcaster and occasional TV host. Author of thirteen books including The Ghost Map, Where Good Ideas Come From and Extra Life, host of the PBS series How We Got To Now, and author of the Adjacent Possible newsletter on innovation.
The camera feature in the @Gemini_Notebook mobile app is so transformative for on-the-go research. I was up in the Sierras working on a new project, and I just took photos of everything, like this museum display. Then I asked for a detailed report of all the info in the image. The text below is what I got back. (I fact-checked it myself and it was 99% accurate -- and some of the text it transcribed is so blurry in the image that it was hard for me to read it.) Next step is to generate documents like this for all the photos I took, and then ask Notebook to highlight all the information that ad… Google Related
16 September
Product discovery coach; wrote Continuous Discovery Habits and writes Product Talk.
Technology writer of Spyglass, a newsletter about technology and media. Previously a reporter at TechCrunch and an investor at GV.
15 September
Machine learning engineer and independent AI consultant. He writes about LLM evaluation, tooling and applied ML at hamel.dev, and previously worked on machine learning at GitHub.
Just updated our AI evals FAQ with 5 new questions, 48 questions & answers total! New FAQs just added: - Do I need a reference answer or rubric before annotating data? - How can I do evals when traces contain sensitive data? - How do you review a trace that is really large? - How much context should I give a LLM judge? - What should I do when my “gold” eval dataset becomes stale? It's all here: hamel.dev
LLMs Related
14 September
Co-founder and CEO of Linear; previously principal designer at Airbnb, where he led the Design Language System, and founding designer at Coinbase.
With this update we prioritized product management use cases: @linear Loops can now respond to project, initiative, cycle, and issue changes, update Linear documents, and share tailored Slack updates automatically. Quoting @linear New capabilities for Linear Loops: • Trigger from project, initiative, cycle, and issue changes • Edit documents automatically • Send tailored Slack updates 🔗: Related
11 September
Usability pioneer; co-founder of Nielsen Norman Group and founder of UX Tigers; author of the ten usability heuristics and of Jakob's Law.
9 September
Software engineer in Chicago; writes the cassidoo.co blog and a weekly developer newsletter.
I'm really excited about @fireworksAI_HQ new event: Forge. AI teams are owning the system: models, data, evals, infrastructure, and policies. So they created an event around elevating that. For builders, model shapers, systems engineers, and leaders building production AI. It's free on Nov 3 at Pier 27 in SF, application only. fandf.co
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6 September
Co-founder and CEO of Linear; previously principal designer at Airbnb, where he led the Design Language System, and founding designer at Coinbase.
Agents 🤝 @linear Linear documents are just documents meant for writing down things. Projects, issues, views and other entities have different types. I think the structure makes it easier for both agents and humans alike to use the right one. Quoting @faruk_parhat Our coding agents did something interesting… We are both a @NotionHQ and @linear shop, but over time our coding agents seem to have preferred writing docs in Linear over Notion (since they were already creating tickets). And this kind of natural bias and proclivity of coding agents toward Linear sl… Related
5 September
Philosophy professor at UC Berkeley and creator of pandoc, the universal document converter, and of the CommonMark markdown specification.
4 September
Machine learning engineer and independent AI consultant. He writes about LLM evaluation, tooling and applied ML at hamel.dev, and previously worked on machine learning at GitHub.
. @isaac_flath and I are going to see if the slop prompt is good and do some evals. (also first time trying to stream haha). Related
I love how Teresa is merging product discovery with evals. I think its a powerful combination! I started learning about product discovery b/c of Teresa and I think its a great skill for any engineer as it helps you focus on what to build. Highly recommend checking out her work. Quoting @ttorres AI evals have been the "it" skill for product teams for over a year. I've even called evals a new discovery habit. But I still meet product teams who only have a vague idea of what evals are. And it's not their fault. Most of the writing on this topic is intended for engineers or just isn't specifi… Related
2 September
Product discovery coach; wrote Continuous Discovery Habits and writes Product Talk.
1 September
Technical staff at METR, where she works on threat modelling and risk assessment for loss-of-control risks from advanced AI.
Their words
But from their perspective, they've just been trained for millions of subjective years to do as well as they possibly can on these evals. In many cases, the only way in which they've been able to perform well on that training is explicitly by cheating, right?
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31 August
Machine learning engineer and independent AI consultant. He writes about LLM evaluation, tooling and applied ML at hamel.dev, and previously worked on machine learning at GitHub.
It's fun posting about evals b/c I can reply like this to bots Related
Founder of Trilogy Software and principal of Alpha School, the private school network built around two hours a day of AI-delivered mastery learning.
From one piece
How to Accelerate Learning & Improve Education | Joe Liemandt
2 beliefs, in the piece's order there
America
Their words
The most positive use case if you said, you know, what's the number one thing we could use AI for? It's not giving kids chatbots that's going to work. It's giving them an individualized lesson plan based on their level to catch them up to grade level. And that would be the single best thing we could do to fix education in America.
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youtube.com
Their words
If you take a chatbot and put it into a standard school, 90% of the kids use it to cheat. Doesn't matter what grade level you're at.
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youtube.com
28 August
Investor and writer. Previously a partner at Andreessen Horowitz and a product leader at Twitter, Facebook, Snap and Microsoft. Writes essays and memos at sriramk.com.
really impressed with the quality of work from @RyanGreenblatt , @METR_Evals and @OpenAI to investigate the HuggingFace incident. Both reports are recommended reading. OpenAI HuggingFace Related
26 August
Software entrepreneur who bootstrapped and sold FeedbackPanda and now builds Podscan; writes The Bootstrapped Founder.
When AI codes and documents for AI. Quoting @ramonpiano_ claude opus 5 is the worst fucking model ever what the fuck is this Related
21 August
Moroccan senior frontend developer writing at smakosh.com; builds side projects and client work under Smakosh LLC.
Co-founder and CEO of Linear; previously principal designer at Airbnb, where he led the Design Language System, and founding designer at Coinbase.
What improvements would you like to see in @linear documents and team docs? Related
17 August
Founder and editor-in-chief of MacStories, writing about Apple software, iPad workflows and automation since 2009. He co-hosts the AppStories podcast.
How Should the Siri App Be Graded? This week on AppStories, John and Federico check in on Apple’s betas with a close look at the new Siri app and how it compares to past-Siri and other chatbot products. On AppStories+, John and Federico explain how they…
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Senior space editor at Ars Technica; wrote Liftoff and Reentry on SpaceX, and reports on launch and rocket programmes.
Their words
In financial documents, it’s clear that SpaceX views commercial launch as a rounding error compared to revenues from its own satellites in space.
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arstechnica.com
16 August
Machine learning engineer and independent AI consultant. He writes about LLM evaluation, tooling and applied ML at hamel.dev, and previously worked on machine learning at GitHub.
12 August
Machine learning engineer and independent AI consultant. He writes about LLM evaluation, tooling and applied ML at hamel.dev, and previously worked on machine learning at GitHub.
10 August
Writer on the intersection of technology and finance. Author of the Bits about Money newsletter and host of the Complex Systems podcast; previously at Stripe.
One of my accountants, in response to me hitting them with the usual packet of documents, sent back a very obviously LLM drafted list of 10 questions. First thought: What am I paying you for. Second thought: Actually no, my LLM digests your LLM's output into the context window. LLMs Related
27 July
Creator of Claude Code at Anthropic.
25 July
Programmer and writer on computer architecture, performance, and software reliability. He has worked on CPU design at Centaur Technology and on software at Google and Microsoft, and writes long-form technical essays at danluu.com.
In another variant of https://danluu.com/learn-what/, I caught up with a former colleague who worked on automated theorem proving. It turns out he's had an interesting career doing all sorts of interesting stuff using the skills he developed by spending a decade writing/using theorem provers. At one point, he said, "if you use X like a theorem prover, it works really well", which surprised me to hear, but of course this is a highly generalizable skill just like compilers or benchmarking/evals. Related
24 July
Programmer and writer on computer architecture, performance, and software reliability. He has worked on CPU design at Centaur Technology and on software at Google and Microsoft, and writes long-form technical essays at danluu.com.
x.com Exercises in evals and benchmarking: DeepSWE / Senior SWE-Bench, performance math, and cold weather tires Related
Bluesky Exercises in benchmarking and evals, part 7: performance napkin math, DeepSWE / Senior SWE-Bench, and winter tires danluu.com
Related
Mastodon Exercises in benchmarking and evals: performance math, winter tires, and DeepSWE / Senior SWE-Bench, danluu.com
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23 July
Professor at Wharton studying how people actually use AI, and author of Co-Intelligence. Writes One Useful Thing, where the claims are dated and testable because the thing they describe keeps changing under them.
Their words
Copilot, which uses a mix of AI models and is okay for working with office documents but lags badly in terms of its agentic abilities.
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oneusefulthing.org
21 July
Experimental psychologist and author of the blog Experimental History, on psychology, science reform and creativity.
Their words
Martsinovich argues, via code snippets, that there is no such thing as a "conversation" with a chatbot. The AI is born anew every time it speaks, and it simply reads the dialogue so far and then tries to write the next line:
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experimental-history.com
19 July
Web infrastructure engineer and standards editor; chaired the IETF HTTP working group and co-authored core HTTP and Atom specifications.
15 July
Founder of HumanLayer; wrote 12-Factor Agents, on how to build AI agents that hold up in production.
1 July
Member of technical staff at Anthropic. He has led ML/AI teams at Amazon, Alibaba and Lazada, and writes about LLMs, recommender systems and engineering at eugeneyan.com.
I’m at @aiDotEngineer and hanging out around the music corner on the 2nd floor from 1415 - 1515! Come by to chat about https://t.co/pdd8bk66Jz, https://t.co/eJSa2MISCW, https://t.co/51rtuo4PDO, evals, agents, memory, how to work effectively with claude code, claude tag, etc! Anthropic Related
29 June
Machine learning engineer and independent AI consultant. He writes about LLM evaluation, tooling and applied ML at hamel.dev, and previously worked on machine learning at GitHub.
26 June
AI research engineer working on large language models. He writes the Ahead of AI newsletter and is the author of Build a Large Language Model (From Scratch).
21 June
Member of technical staff at Anthropic. He has led ML/AI teams at Amazon, Alibaba and Lazada, and writes about LLMs, recommender systems and engineering at eugeneyan.com.
8 June
Programmer and writer on computer architecture, performance, and software reliability. He has worked on CPU design at Centaur Technology and on software at Google and Microsoft, and writes long-form technical essays at danluu.com.
x.com Exercises in benchmarking, evals, and experimental design, part 6: Related
Mastodon Exercises in benchmarking, evals, and experimental design, part 6: patreon.com
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4 June
Security technologist and author of books including Applied Cryptography and Data and Goliath; writes the Schneier on Security blog and the monthly Crypto-Gram newsletter.
Behavioural economist at Chicago Booth, professor of behavioural science and economics and director of AGI economics at Google DeepMind.
Their words
the ballerina and the kind of performer, that's the wrong reference class. Right now we have a lot of jobs where you have different tasks. So this is the task-based model jobs where you have like a lot of different tasks. So like a doctor, what is their job? They're filling out insurance documents. They're you know, going and like calling different pharmaceutical companies. And one of their tasks is to actually see the patient and talk to them, but that's like actually not the main part part of the job.
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youtube.com
24 May
Co-founder and CEO of Every. Writes the Chain of Thought column about working with AI tools and hosts the podcast AI & I.
From one piece
AI predictions: Job markets, Codex beats Claude, and the death of org charts | Dan Shipper
2 beliefs, in the piece's order there
Anthropic
Their words
The second is that most of the work that you do is actually going to happen on your computer in an environment like Codex or Cloud Co-work that becomes the sort of operating system for it becomes the sort of operating system for how how you do all of your work, whether that's your email, the documents you create, like all that kind of stuff.
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youtube.com
Their words
I think that we will be reading way more AI generated writing in documents and emails and we will like it.
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youtube.com
13 May
Member of technical staff at Anthropic. He has led ML/AI teams at Amazon, Alibaba and Lazada, and writes about LLMs, recommender systems and engineering at eugeneyan.com.
Mythos evals from XBOW and UK AISI: • UK AISI: Mythos completed a 32-step network attack (est. at ~20 hrs for experts) in 6/10 tries. First model to solve their e2e cyber ranges! • XBOW: "token-for-token, unprecedented precision" Read more: https://t.co/hD6G6DvALg, xbow.com
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10 May
Author of The Lean Startup and Incorruptible, and founder of the Long-Term Stock Exchange, who advises companies on corporate governance and mission protection.
From one piece
How Anthropic, Costco, and Patagonia all build incorruptible companies | Eric Ries
2 beliefs, in the piece's order there
Their words
did you know that according to the legal documents you yourself signed, your company's literal charter that you have right now, and this is not some hypothetical future thing, you've already put in motion, a rule that says you have a fiduciary duty to say yes in this situation.
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youtube.com
startups
Their words
According to Harvard Law School, among venturebacked companies that have the standard best practices set up that you got from your lawyer, okay, only 20% of founders are still the CEO 3 years after going public.
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youtube.com
10 April
Ruby and Rails core committer known online as "tenderlove"; maintains Nokogiri and works on Ruby performance.
2 April
Founding member of OpenAI and former director of AI at Tesla; creator of nanoGPT and the term "vibe coding".
LLM Knowledge Bases Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So: Data ingest: I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki… LLMs Related
29 March
Engineer and writer on machine-learning systems; author of Designing Machine Learning Systems and AI Engineering. "I work to bring AI into production. I write about AI system design."
Their words
But in Vietnam and also in a lot of other Asian countries, people are on the move all the time. Like people are on the motorbike all the time. So, they actually really don't like typing. So, the voice like a lot of the companies in Vietnam actually deploy voice bots before they do uh do chatbot.
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youtube.com
20 March
Software entrepreneur who bootstrapped and sold FeedbackPanda and now builds Podscan; writes The Bootstrapped Founder.
Founding member of OpenAI and former director of AI at Tesla; creator of nanoGPT and the term "vibe coding".
documentation
Their words
It used to be that you have documentation for other people who are going to use your library, but like you shouldn't do that anymore. Like you should have instead of HTML documents for humans, you have markdown documents for agents.
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youtube.com
4 March
Creator of Claude Code at Anthropic.
2 March
Machine learning engineer and independent AI consultant. He writes about LLM evaluation, tooling and applied ML at hamel.dev, and previously worked on machine learning at GitHub.
Evals Skills for Coding Agents Today, Shreya Shankar and I are publishing evals skills, a set of skills for AI product evals1. Eval tools often get in the way. They nudge you toward generic off-the-shelf metrics and fully automated evals before you’v…
coding agents Related
13 February
Co-founder and CEO of Anthropic; previously VP of research at OpenAI.
Their words
The the chatbot is already running into limitations of, you know, making it smarter doesn't really help the average consumer that much. But I don't think that's a limitation of AI models. I don't think that's evidence that, you know, the models are are the models are good enough and they're they're, you know, them getting better doesn't matter to the economy.
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youtube.com
5 February
Co-founder of Superlogical, started in 2026 to build server-side terminal infrastructure; creator of Ghostty. Co-founded HashiCorp and created Vagrant and Terraform before that.
23 January
Software developer and entrepreneur; co-founder of Heroku, author of The Twelve-Factor App, and a researcher at Ink & Switch.
12 December 2025
British senior engineering manager at Netlify, previously a team lead at the BBC; writes about JavaScript testing, chatbots and serverless at marclittlemore.com.
Mastodon Been trying to use https://conductor.build/ to run some Claude Code agents in the background while I write some documents. Seems pretty great so far and has a really nice UI/UX for creating PRs. Anthropic Related
Bluesky Been trying to use https://conductor.build/ to run some Claude Code agents in the background while I write some documents. Seems pretty great so far and has a really nice UI/UX for creating PRs. Anthropic Related
25 November 2025
Co-founder and chief scientist of Safe Superintelligence Inc., and previously co-founder and chief scientist of OpenAI.
Their words
And one of the one thing you could do, and I think that's something that is done inadvertently, is that people take inspiration from the evals.
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youtube.com
Medieval historian at the LSE; writes Going Medieval and The Middle Ages: A Graphic History, and spends a lot of it correcting what people think the period was like.
23 November 2025
China economy researcher based in Beijing who writes on his personal blog about Chinese political economy, history, books and music.
Member of technical staff at Anthropic. He has led ML/AI teams at Amazon, Alibaba and Lazada, and writes about LLMs, recommender systems and engineering at eugeneyan.com.
27 October 2025
Writer and former Substack product manager; publishes essays and interviews on technology, culture and China at jasmi.news.
1 October 2025
Machine learning engineer and independent AI consultant. He writes about LLM evaluation, tooling and applied ML at hamel.dev, and previously worked on machine learning at GitHub.
Selecting The Right AI Evals Tool Over the past year, I’ve focused heavily on AI Evals, both in my consulting work and teaching. A question I get constantly is, “What’s the best tool for evals?”. I’ve always resisted answering directly for two reasons.…
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19 September 2025
German writer and historian; wrote Blitzed, on drug use in Nazi Germany, and Tripped.
Their words
So the Navy hired the shoe runners unit from the SS, paid them money, and then gave them drugs, different kinds of drug combinations, methamphetamine combined with cocaine and chewing gum and all kinds of things. So this is a big thing, you know. And there are documents to it.
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youtube.com
2 September 2025
Technology analyst whose weekly email on tech reaches over 150,000 readers. Publishes essays and an annual presentation on where the industry is going.
Their words
It's like something around 10% give or take three or four percent of people depending on the survey are using this say they're using this every day. Another sort of 15 to 20% of people say they're using it every week.
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youtube.com
29 August 2025
Economist specialising in global income inequality; formerly lead economist in the World Bank research department, now at the CUNY Graduate Center and author of the Global Inequality and More newsletter.
Their words
As Quinn Slobodian documents in his excellent book Globalists: The End of Empire and the Birth of Neoliberalism
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branko2f7.substack.com
5 August 2025
Design engineer and illustrator; makes visual essays on programming, anthropology and what language models do to the way people write.
25 June 2025
Member of technical staff at Anthropic. He has led ML/AI teams at Amazon, Alibaba and Lazada, and writes about LLMs, recommender systems and engineering at eugeneyan.com.
Wrote an intro to evals for long-context Q&A systems: • How it differs from basic Q&A • What dimensions & metrics to eval on • How to build llm-evaluators • How to build eval datasets • Benchmarks: narratives, technical docs, multi-docs eugeneyan.com
LLMs benchmarks Related
22 June 2025
Member of technical staff at Anthropic. He has led ML/AI teams at Amazon, Alibaba and Lazada, and writes about LLMs, recommender systems and engineering at eugeneyan.com.
LLMs
Their words
This is why model-based evaluation is increasingly popular-it offers more reliable and nuanced evals than traditional metrics.
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eugeneyan.com
28 May 2025
Machine learning engineer and independent AI consultant. He writes about LLM evaluation, tooling and applied ML at hamel.dev, and previously worked on machine learning at GitHub.
12 May 2025
Co-founder of Sundial; former vice president of product design at Facebook; author of The Making of a Manager; writes The Looking Glass.
30 April 2025
Member of technical staff at Anthropic. He has led ML/AI teams at Amazon, Alibaba and Lazada, and writes about LLMs, recommender systems and engineering at eugeneyan.com.
@hamel.bsky.social & @sh-reya.bsky.social are two of the world's best on evals. They've built evals for 35+ AI apps & helped teams ship confidently. Now they'll teach everything they know on building evals that work. Enrollment closes in 4 days. Secret 35% discount code: maven.com
Related
23 April 2025
Member of technical staff at Anthropic. He has led ML/AI teams at Amazon, Alibaba and Lazada, and writes about LLMs, recommender systems and engineering at eugeneyan.com.
Product evals are misunderstood. Many teams think that adding another tool, metric, or llm-as-judge will solve all their problems and save their product. But that just dodges the hard truth and avoids the real work. Here's how to fix your process instead. eugeneyan.com
LLMs Related
16 April 2025
Member of technical staff at Anthropic. He has led ML/AI teams at Amazon, Alibaba and Lazada, and writes about LLMs, recommender systems and engineering at eugeneyan.com.
@hamel.bsky.social & his wisdom on evals, error analysis, looking at your data is what we need. Here are his 10 Don'ts: • Don't skip error analysis • Don't skip looking at your data • Don't gatekeep who can write prompts • Don't let zero users be a roadblock • Don't be blindsided by criteria drift Related
26 March 2025
Indian software engineer and co-founder of Skcript; builds web apps and writes at varunraj.in.
Their words
My everyday office gear that helps me debug, code, write documents seamlessly.
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varunraj.in
25 March 2025
Co-founder of Sundial; former vice president of product design at Facebook; author of The Making of a Manager; writes The Looking Glass.
3 February 2025
Machine-learning researcher on open language models; writes the Interconnects newsletter and the RLHF Book, after leading post-training at Ai2.
Their words
And the important thing to say is that no matter how you want the model to behave, these RLHF and preference-tuning techniques also improve performance. So, on things like math evals and code evals, there is something innate to these, what is called contrastive loss functions.
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youtube.com
29 October 2024
Machine learning engineer and independent AI consultant. He writes about LLM evaluation, tooling and applied ML at hamel.dev, and previously worked on machine learning at GitHub.
Their words
You might be skeptical of using synthetic data. After all, it’s not real data, so how can it be a good proxy? In my experience, it works surprisingly well. Some of my favorite AI products, like Hex use synthetic data to power their evals
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hamel.dev
3 July 2024
Software engineer who writes the blog Made of Bugs about performance, debugging and understanding computer systems. Previously worked at Anthropic on interpretability, at Stripe on Sorbet, and at Ksplice.
21 June 2024
Co-creator of Django and creator of Datasette; writes daily at simonwillison.net.
Their words
Your AI Product Needs Evals by Hamel Husain remains my favourite piece of writing on how to go about putting these together.
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simonwillison.net
19 June 2024
Co-founder and CEO of Perplexity, an AI answer engine; previously a research scientist at OpenAI and a PhD student at UC Berkeley.
Their words
The principle in Perplexity is you’re not supposed to say anything that you don’t retrieve, which is even more powerful than RAG because RAG just says, “Okay, use this additional context and write an answer.” But we say, “Don’t use anything more than that too.” That way we ensure a factual grounding.
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youtube.com
3 June 2024
CEO of Vercel; creator of Next.js and Socket.IO. Writes at rauchg.com.
Nothing matches. Show everything
A korrent is a belief a person has stated in their own words: one
sentence stating the claim, backed by a quote and a source, kept at
korrents.com .
Under a name here, the quoted block is what they actually said.
The korrent beneath it is the claim those words support, in
korrents' wording — tap it to see the record, its source, and who
else holds it.
Nobody here wrote their own korrents. They are compiled from public
statements, and a person can change their mind, which is recorded too.
Got it
About the English under a post
Some people here publish in a language other than English. Where they
do, this site shows a machine translation beneath the post, in
this typeface — the site's own, not theirs.
The post itself is never changed, moved or hidden: what is set in the
serif above is exactly what the person published, and it is what to
quote them on. A translation can be wrong in ways that matter,
especially about tone.
Only the post's own words are translated. A quoted post, a linked
article and a belief on korrents.com
are left in their original language.
Got it