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Context engineering

chrisloy.dev

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Re: Context engineering

#11

Post by skeeter2020 »

satisfice wrote: post #6
voidhorse wrote: post #3

There is nothing precise about crafting prompts and context—it's just that, a craft. Even if you do the right thing and check some fuzzy boundary conditions using autoscorers, the model can still change out from beneath you at any point and totally alter the behavior of your system. There is no formal language here. After all, mathematics exists because natural language is notoriously imprecise. The article has some…

My thoughts exactly. The author is saying we should think strategically about the use of context. Sure. Yes. But for that to qualify as engineering we need solid theory about how context works. We don’t have that, yet. For instance experiments show that not all parts of the context window are equally well attended. Imagine trying to engineer a bridge when no one really knows how strong steel is.

or how wide the river is year round

Re: Context engineering

#13

Post by aeve890 »

skeeter2020 wrote: post #10
aeve890 wrote: post #7

Are we still calling this things engineering?

"professionally trained & legally responsible for the results" is definitely not the same thing as what we used to just call "good at googling".

I'd say this shit is even worse that "good at googling". Literal incantation for stochastic machines is like just two notches above checking the horoscope.

Re: Context engineering

#14

Post by calebkaiser »

aeve890 wrote: post #13
skeeter2020 wrote: post #10

Earlier quoted context omitted.

"professionally trained & legally responsible for the results" is definitely not the same thing as what we used to just call "good at googling".

I'd say this shit is even worse that "good at googling". Literal incantation for stochastic machines is like just two notches above checking the horoscope.

Based on the comments, I expected this to be slop listing a bunch of random prompt snippets from the author's personal collection.

I'm honestly a bit confused at the negativity here. The article is incredibly benign and reasonable. Maybe a bit surface level and not incredibly in depth, but at a glance, it gives fair and generally accurate summaries of the actual mechanisms behind inference. The examples it gives for "context engineering patterns" are actual systems that you'd need to implement (RAG, structured output, tool calling, etc.), not just a random prompt, and they're all subject to pretty thorough investigation from the research community.

The article even echoes your sentiments about "prompt engineering," down to the use of the word "incantation". From the piece:

> This was the birth of so-called "prompt engineering", though in practice there was often far less "engineering" than trial-and-error guesswork. This could often feel closer to uttering mystical incantations and hoping for magic to happen, rather than the deliberate construction and rigorous application of systems thinking that epitomises true engineering.

Re: Context engineering

#15

Post by calebkaiser »

elteto wrote: post #2

Are there any open source examples of good context engineering or agent systems?

Any of the "design patterns" listed in the article will have a ton of popular open source implementations. For structured generation, I think outlines is a particularly cool library, especially if you want to poke around at how constrained decoding works under the hood: https://github.com/dottxt-ai/outlines

Re: Context engineering

#16

Post by calebkaiser »

sgt101 wrote: post #9

Why would I believe that any of this works? This is just some blokes idea of what people should do. There is no evidence offered. No attempt to measure the benefits.

Most of the inference techniques (what the author calls context engineering design patterns) listed here originally came from the research community, and there are tons of benchmarks measuring their effectiveness, as well as a great deal of research behind what is happening mechanistically with each.

As the author points out, many of the patterns are fundamentally about in-context learning, and this in particular has been subject to a ton of research from the mechanistic interpretability crew. If you're curious, I think this line of research is fascinating: https://transformer-circuits.pub/2022/in-context-learning-an...

Re: Context engineering

#17

Post by [deleted] »

calebkaiser wrote: post #14
aeve890 wrote: post #13

Earlier quoted context omitted.

I'd say this shit is even worse that "good at googling". Literal incantation for stochastic machines is like just two notches above checking the horoscope.

Based on the comments, I expected this to be slop listing a bunch of random prompt snippets from the author's personal collection. I'm honestly a bit confused at the negativity here. The article is incredibly benign and reasonable. Maybe a bit surface level and not incredibly in depth, but at a glance, it gives fair and generally accurate summaries of the actual mechanisms behind inference. The examples it gives for…

[deleted]

Re: Context engineering

#18

Post by simonw »

aeve890 wrote: post #7

Are we still calling this things engineering?

Yes, and we've also decided that they deserve the title "engineering" more than software engineering does.

Most engineering disciplines have to deal with tolerances and uncertainty - the real world is non-deterministic.

Software engineering is easy in comparison because computers always do exactly what you tell them to do.

The ways LLMs fail (and the techniques you have to use to account for that) have more in common than physical engineering disciplines than software engineering does!

Re: Context engineering

#19

Post by calebkaiser »

voidhorse wrote: post #3

There is nothing precise about crafting prompts and context—it's just that, a craft. Even if you do the right thing and check some fuzzy boundary conditions using autoscorers, the model can still change out from beneath you at any point and totally alter the behavior of your system. There is no formal language here. After all, mathematics exists because natural language is notoriously imprecise. The article has some…

I think it's fair to question the use of the term "engineering" throughout a lot of the software industry. But to be fair to the author, his focus in the piece is on design patterns that require what we'd commonly call software engineering to implement.

For example, his first listed design pattern is RAG. To implement such a system from scratch, you'd need to construct a data layer (commonly a vector database), retrieval logic, etc.

In fact I think the author largely agrees with you re: crafting prompts. He has a whole section admonishing "prompt engineering" as magical incantations, which he differentiates from his focus here (software which needs to be built around an LLM).

I understand the general uneasiness around using "engineering" when discussing a stochastic model, but I think it's worth pointing out that there is a lot of engineering work required to build the software systems around these models. Writing software to parse context-free grammars into masks to be applied at inference, for example, is as much "engineering" as any other common software engineering project.

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