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Simplicity is essential in a generative world

* "less is more" (proverb) * "perfection is finally attained not when there is no longer anything to add, but when there is no longer anything to take away" (Antoine de Saint Exupéry) * "omit needless words" (Strunk & White) * "It is vain to do with

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Diverge-converge cycles in LLMs

The Double Diamond is, roughly, a design framework consisting of 4 steps: 1. Diverging on problem (Discover). Explore widely to gather a broad range of insights and challenges related to the problem. 2. Converging on problem (Define). Analyze and synthesize the gathered insights to define a clear and specific problem

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LLM latency is linear in output token count

All top LLMs, including all GPT-family and Llama-family models, generate predictions one token at a time. It's inherent to the architecture, and applies to models running behind an API as well as local or self-deployed models. Armed with this knowledge, we can make a very accurate model of

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RAG is more than just embedding

90% of time when people say "Retrieval-augmented generation" they mean that the index is built using an embedding model like OpenAI's text-embedding-002 and a vector database like Chroma, but it doesn't have to be this way. Retrieval is a long-standing problem in computer science

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Retrieval-augmented generation

Retrieval-augmented generation, or RAG, is a fancy term hiding a simple idea: Problem: LLMs can reason, but they don't have the most relevant facts about your situation. They don't know the location of your user, or the most relevant passage from the knowledge base, or what

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Properties of a good memory

Apps with no Memory are boring. Compare a static website from the 90s with any SaaS or social network or phone app: the former knows nothing about you, the latter knows a lot. From UI preferences (dark or light mode?) to basic personal data (name?) to your friend list to

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Things I've underestimated - Sep 2023

After attending the Ray Summit in San Francisco this week, I realized I had previously discounted several interesting things. Here's what I now want to explore more. Semantic Kernel I've gotten so used to langchain that I haven't really considered switching... all the while

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Launching OpenCopilot

Across the many experiments I've made this year (and which I've written about here) I've felt the need for better tools. Specifically, for the past few months I have been building copilots, and doing so from scratch takes a bunch of work every time.

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Context engineering is information retrieval

The stages of an LLM app seem to go like this: * Hardcode the first prompt, get the end-to-end app working. * Realise that the answers are bad. * Do some prompt engineering. * Realise the answers are still bad. * Do some more prompt engineering. * Discover vector databases!!!1 * Dump a ton of data

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Making GPT API responses faster

GPT APIs are slow. Just in the past week, the OpenAI community has had 20+ questions around that. And not only is it rare for users to tolerate 30-second response times in any app, it is also extremely annoying to develop when even basic tests take several minutes to run.

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agentreader - simple web browsing for your Langchain agent

This is a short link-post to a new repo I just released. While working on Why AutoGPT fails and how to fix it I created a handy web-browsing Tool for langchain Agents and now finally got around to open-sourcing it. Here is the repository: github.com/taivop/agentreader.

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Why AutoGPT fails and how to fix it

A couple weeks after AutoGPT came out we tried to make it actually usable. If you don't know yet, it looks amazing on first glance, but then completely fails because it creates elaborate plans that are completely unnecessary. Even asking it to do something simple like "find

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Core innovations of AutoGPT

AutoGPT (repo) went viral on Github and looks impressive on Twitter, but almost never works. In the process of trying to improve it I dug into how it works. Really there are two important parts to AutoGPT: a plan-and-execute workflow, and looped Chain-of-thought (CoT). Plan-and-execute Say the user input is

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GPT-3.5 and GPT-4 response times

Some of the LLM apps we've been experimenting with have been extremely slow, so we asked ourselves: what do GPT APIs' response times depend on? It turns out that response time mostly depends on the number of output tokens generated by the model. Why? Because LLM latency

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How data is used for LLM programming

Software 1.0 -- the non-AI, non-ML sort -- extensively uses testing to validate things work. These tests are basically hand-written rules and assertion. For example, a regular expression can be easily tested with strings that should and should not give a match. In software 2.0, and specifically supervised

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Hacky multimodality

GPT-4 supports images as an optional input, according to OpenAI's press release. As far as I can tell, only one company has access. Which makes you wonder: how can you get multimodality support already today? There are basically two ways for adding image support to an LLM: 1.

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First thoughts on AI moratorium

Context: first thoughts on Pause Giant AI experiments. I will refine my thinking over time. * I had not thought about AI safety much since ~2017, after thinking a lot about it in 2014-2017. In 2017, I defended my MSc thesis on an AI-safety-inspired topic (though very narrow and technical in

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Agents are self-altering algorithms

Chain-of-thought reasoning is surprisingly powerful when combined with tools. It feels like a natural programming pattern of LLMs: thinking by writing. And it's easy to see the analogy to humans: verbalizing your thoughts in written (journalling) or spoken ("talking things through") form is a good way

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Index to reduce context limitations

There is a very simple, standardized way of solving the problem of too small GPT context windows. This is what to do when the context window gets full: Indexing: 1. Chunk up your context (book text, documents, messages, whatever). 2. Put each chunk through the text-embedding-ada-002 embedding model, and store

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Langchain frustration

I was building an agent with langchain today, and was very frustrated with the developer experience. It felt hard to use, hard to get things right, and overall I was confused a lot while developing (for no good reason). Why is it so hard to use? Here are some of

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Office 365 copilot is extremely impressive

Microsoft recently held an event where they announced the "Office 365 Copilot". It was extremely impressive to me, to the extent that (when this launches) I will consider switching from Google's work suite to Microsoft's. Why is this announcement so impressive? In one word,

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Your non-AI moat

"What's the Moat of your AI company?" That seems to be top of mind for founders pitching their novel idea to VCs -- and the most common question they get. But I think it might not be the right question. Right now, nobody seems to have

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GPT-4, race, and applications

GPT-4 came out yesterday and overshadowed announcements, each of which would have been bombshell news otherwise: * Anthropic AI announcing their ChatGPT-like API -- likely the strongest competitor to OpenAI today (only waitlist) * Google announcing PaLM API (currently it's just marketing -- no public access yet) * Adept AI announcing

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Alpaca is not as cheap as you think

"Alpaca is just $100 and competitive with InstructGPT" -- takes like this are going around Twitter, adding to the (generally justified!) hype around AI models. It is indeed a very encouraging result. Specifically, that it took so little compute to train something that achieves competitive results on benchmarks

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Chain-of-thought reasoning

Matt Rickard has a concise overview of Chain-of-thought: the design pattern of having an LLM think step by step. To summarize, the four mentioned approaches from simpler to more nuanced are: 1. Add "Let's think step-by-step" to the prompt. 2. Produce multiple solutions, have the LLM