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
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 good practical tips and it's not on the author but man I really wish we'd stop abusing the term "engineering" in a desperate attempt to stroke our own egos and or convince people to give us money. It's pathetic. Coming up with good inputs to LLMs is more art than science and it's a craft. Call a spade a spade.
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.
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!
There was a good series interviewing people that worked in both software engineering and traditional engineering: https://www.hillelwayne.com/post/are-we-really-engineers/. The conclusion was that yes, a lot of what we do as software engineers is engineering.
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...
The only thing that passed the test of time,so far is specificity: if you ask for multiple things or vague things, you receive half-baked answers trying to cover all bases. If you ask for specific one thing and describe it, the answer quality goes up;e.g. LLMs creating multi-part content mix up the parts and qualities of them, so e.g. asking for Part 1*specific, will always get a better answer than "list all parts of X"(quality drops with length of list).
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[ 3.2 ms ] story [ 49.2 ms ] threadThe article has some good practical tips and it's not on the author but man I really wish we'd stop abusing the term "engineering" in a desperate attempt to stroke our own egos and or convince people to give us money. It's pathetic. Coming up with good inputs to LLMs is more art than science and it's a craft. Call a spade a spade.
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.
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!
There is no evidence offered. No attempt to measure the benefits.
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...