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> > Hey Jim! Tomorrow’s packed on my end, back-to-back all day. Thursday AM free if that works for you? Sent an invite, lmk if it works.
Feel free to send generated AI responses like this if you are a sociopath.
I thought this entire premise was obvious? Does it really take an article and a venn diagram to say you should only provide the relevant content to your LLM when asking a question?
"Relevant content to your LLM when asking a question" is last year's RAG. If you look at how sophisticated current LLM systems work there is so much more to this. Just one example: Microsoft open sourced VS Code Copilot Chat today (MIT license). Their prompts are dynamically assembled with tool instructions for various tools based on whether or not they are enabled: https://github.com/microsoft/vscode-copilot-chat/bl…
The goal is to design a probability distribution to solve your task by taking a complicated probability distribution and conditioning it, and the more detail you put into thinking about ("how to condition for this?" / "when to condition for that?") the better the output you'll see.
(what seems to be meant by "context" is a sequence of these conditioning steps :) )
Earlier quoted context omitted.
I am not a fan of this banal trend of superficially comparing aspects of machine learning to humans. It doesn't provide any insight and is hardly ever accurate.
I've seen a lot of cases where, if you look at the context you're giving the model and imagine giving it to a human (just not yourself or your coworker, someone who doesn't already know what you're trying to achieve - think mechanical turk), the human would be unlikely to give the output you want. Context is often incomplete, unclear, contradictory, or just contains too much distracting information. Those are all thi…
It's easy to forget that the conversation itself is what the LLM is helping to create. Humans will ignore or depriotitize extra information. They also need the extra information to get an idea of what you're looking for in a loose sense. The LLM is much more easily influenced by any extra wording you include, and loose guiding is likely to become strict guiding
Isn't "context" just another word for "prompt?" Techniques have become more complex, but they're still just techniques for assembling the token sequences we feed to the transformer.
Almost. It's the current prompt plus the previous prompts and responses in the current conversation. The idea behind "context engineering" is to help people understand that a prompt these days can be long , and can incorporate a whole bunch of useful things (examples, extra documentation, transcript summaries etc) to help get the desired response. "Prompt engineering" was meant to mean this too, but the AI influencer…
> Conclusion Building powerful and reliable AI Agents is becoming less about finding a magic prompt or model updates. It is about the engineering of context and providing the right information and tools, in the right format, at the right time. It’s a cross-functional challenge that involves understanding your business use case, defining your outputs, and structuring all the necessary information so that an LLM can “a…
I am not a fan of this banal trend of superficially comparing aspects of machine learning to humans. It doesn't provide any insight and is hardly ever accurate.
This new stillpointlab hacker news account is based on the company name I chose to pursue my Context as a Service idea. My belief is that context is going to be the key differentiator in the future. The shortest description I can give to explain Context as a Service (CaaS) is "ETL for AI".
There is no engineering involved in using AI. It's insulting to call begging an LLM "engineering".
This. Convincing a bullshit generator to give you the right data isn’t engineering, it quackery. But I guess “context quackery” wouldn’t sell as much. LLMs are quite useful and I leverage them all the time. But I can’t stand these AI yappers saying the same shit over and over again in every media format and trying to sell AI usage as some kind of profound wizardry when it’s not.
Definitely mirrors my experience. One heuristic I've often used when providing context to model is "is this enough information for a human to solve this task?". Building some text2SQL products in the past it was very interesting to see how often when the model failed, a real data analyst would reply something like "oh yea, that's an older table we don't use any more, the correct table is...". This means the model was…
"Make it possible for programmers to write in English and you will find that programmers cannot write in English."
It's meant to be a bit tongue-in-cheek, but there is a certain truth to it. Most human languages fail at being precise in their expression and interpretation. If you can exactly define what you want in English, you probably could have saved yourself the time and written it in a machine-interpretable language.
We are entering a new era of gamification of programming, where the power users force their imaginary strategies on innocent people by selling them to the equally clueless and gaming-addicted management.
Earlier quoted context omitted.
This. Convincing a bullshit generator to give you the right data isn’t engineering, it quackery. But I guess “context quackery” wouldn’t sell as much. LLMs are quite useful and I leverage them all the time. But I can’t stand these AI yappers saying the same shit over and over again in every media format and trying to sell AI usage as some kind of profound wizardry when it’s not.
It is total quackery. When you zoom out in these discussions you begin to see how the AI yappers and their methodology is just modern-day alchemy with its own jargon and "esoteric" techniques.