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Claude Cookbook

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Re: Claude Cookbook

#71

Post by NichoPaolucci »

embedding-shape wrote: post #37
cloakandswagger wrote: post #24

Earlier quoted context omitted.

Remember in 2023 when people thought "prompt engineering" would be the new software engineering and invested tons of time into learning CoT, ReAct, thread-of-thoughts, etc? Those were mostly obviated by reasoning models and harness updates by 2024. It seems pointless to invest energy into the latest/greatest AI technique or framework when they're going to either be absorbed or replaced on a 3 month cycle.

Isn't it clear that some people are better at working with/prompting LLMs than other people? Or is the idea that what you write to them and how you use them doesn't matter, it's all up to the model/harness? To me this seems clear, so then clearly this is a skill, which typically is called "prompt engineering". Specifically CoT or the other things you mention wasn't referred to as "prompt engineering" as far as I know…

I actually think that good prompting MOSTLY comes from good writing skills in general. Being able to more clearly state things to an agent, knowing what pieces of context are entirely unnecessary and which are important, having a larger vocabulary helps too.

Of course, there are other areas that can improve model output (Direction rather than open-ended assistance requests, using keywords + plugins that help, the "your output should include: " style prompting).

A few of us run almost the same exact setup at my shop (Base Claude Code w/ SuperPowers + a context repository) and the models are somewhat unhelpful to some, and give meaningful output to others. The only correlation I notice is that their prompts are no-good. Not from a meta "prompt" engineering standpoint, but from a general English 101 standpoint.

"dudde no i wanted the function to return 3 things. not like that. do it again"

VS something like

"Modify the "renderThreeVars()" function signature to accept another variable called "z" and add it to the return statement at line 64."

Obvious exaggeration, but you get the point.

Re: Claude Cookbook

#72

Post by tedggh »

mindwok wrote: post #8

Gotta be honest, almost every "how to use AI" resource seems pointless to me. I'm either going to ask the AI how to do it, or if it's about using the AI then we can just bake it into the harness or wait for Anthropic/OpenAI to do it for me because they're always trivial. All of these resources on agentic workflows, managing agent memory, harness engineering, etc. appear to just be theatre to me.

There was an article by Vercel on how ineffective tool calling is compared to just a single md file with clearly defined instructions. They showed a clever way on how to use compressed indexes. My experience with tool calling was similar to what Vercel described, and I spent hours trying to perfect it. Since reading Vercel’s findings, Claude.md and Agents.md, maybe a Project.md it’s all I use.

Re: Claude Cookbook

#73

Post by NichoPaolucci »

cloakandswagger wrote: post #63
embedding-shape wrote: post #37

Earlier quoted context omitted.

Isn't it clear that some people are better at working with/prompting LLMs than other people? Or is the idea that what you write to them and how you use them doesn't matter, it's all up to the model/harness? To me this seems clear, so then clearly this is a skill, which typically is called "prompt engineering". Specifically CoT or the other things you mention wasn't referred to as "prompt engineering" as far as I know…

As part of a previous job I needed to audit internal AI usage from a largely non-technical employee population. The prompts were, predictably, really bad. Broken English, sentence fragments, vague requests, lack of context. Yet somehow, the users always got the answer they were looking for. It might have taken a few extra turns with questions from the model, but the end result was the same. It's humbling, but a flowe…

You call that a peasant prompt, but it's actually almost perfect. Couple notes, but it's 95% of the way there. "Can a dog eat sunflower seed" is probably the perfect version, just 1 extraneous word in this version.

Unless the user wanted to know if a cat could eat sunflower seed or something.

Re: Claude Cookbook

#74

Post by bigfishrunning »

weird-eye-issue wrote: post #21
behnamoh wrote: post #9

So like OpenAI's Cookbook which has existed for years and no one reads?

Have you ever thought that just because you don't read something doesn't mean that nobody reads it?

I would assume people read LLM generated summaries of it, almost nobody reads it directly.

Re: Claude Cookbook

#75

Post by user43928 »

NichoPaolucci wrote: post #71
embedding-shape wrote: post #37

Earlier quoted context omitted.

Isn't it clear that some people are better at working with/prompting LLMs than other people? Or is the idea that what you write to them and how you use them doesn't matter, it's all up to the model/harness? To me this seems clear, so then clearly this is a skill, which typically is called "prompt engineering". Specifically CoT or the other things you mention wasn't referred to as "prompt engineering" as far as I know…

I actually think that good prompting MOSTLY comes from good writing skills in general. Being able to more clearly state things to an agent, knowing what pieces of context are entirely unnecessary and which are important, having a larger vocabulary helps too. Of course, there are other areas that can improve model output (Direction rather than open-ended assistance requests, using keywords + plugins that help, the "yo…

Why not open-ended assistance requests?

I ask it all the time about whether X is feasible, how we can get started on Y, and to investigate issue Z.

It is working great for me in a >100k LOC project.

Perhaps this works less well with weaker models. I suspect the people who say Qwen 3.6 27B is working well, are using prompts like "modify the renderThreeVars() function in rendering.py".

Re: Claude Cookbook

#76

Post by saberience »

defrost wrote: post #48
saberience wrote: post #44

Earlier quoted context omitted.

It's not really a skill. The models are at this point smarter than you are, so the idea that you can prompt them "better" is laughable really when discussing frontier models. It's like imagining you could "prompt" Richard Feynman to be smarter at Physics. That is, for 99.9% of engineers, if you want the model to do a code review of your project, the best solution is to just ask Fable, "Hey Fable, do a code review of…

FWiW > it's like thinking you can prompt Linus Torvalds into writing better C++ than he already can do. Linus Torvalds, the inventor (and beloved dictator) of Linux, has always been quite harsh about C++ and why he rejects it for Linux kernel development. He’s not just been very vocal about it, but also brought up some arguments against the use of C++ that are worth reviewing in detail. ~ https://medium.com/@jankamme…

I mean, the point stands.

The models are beyond expert level in many areas at this point.

Do you really believe that adding extra junk to your prompt is going to make the model write code better than it does already?

Again, imagine going to Terrence Tao and "prompting" him to get better at Maths, do you think you can do it? What prompt would you give to him to make him produce better maths. Unless you're already a world-leading Mathematician I think you would find it hard.

Re: Claude Cookbook

#77

Post by embedding-shape »

cyclopeanutopia wrote: post #57
embedding-shape wrote: post #37

Earlier quoted context omitted.

Isn't it clear that some people are better at working with/prompting LLMs than other people? Or is the idea that what you write to them and how you use them doesn't matter, it's all up to the model/harness? To me this seems clear, so then clearly this is a skill, which typically is called "prompt engineering". Specifically CoT or the other things you mention wasn't referred to as "prompt engineering" as far as I know…

Nah, you might be confusing prompt engineering with having domain knowledge. :)

I think some people who are better at "prompting" even without domain knowledge could be better at getting LLM agents to produce good results than people with good domain knowledge but without the skills to prompt well. Just a hypothesis though, would be fun to try it out for real sometime :)

Re: Claude Cookbook

#78

Post by simonw »

cloakandswagger wrote: post #26
simonw wrote: post #17

Earlier quoted context omitted.

I've been cooking with the help of LLMs a few times a month for over a year now. It's worked out well 9/10 times. It turns out the "average" version of a recipe that's baked into the weights is usually a solid recipe, and they're really good at offering substitutions for things like "I don't have ingredient X" or "make it vegetarian". It's also fun promoting "make it tastier" once or twice after each recipe just to s…

One of the most satisfying and useful applications for AI is handing it the URL for a recipe (replete with 8 paragraphs of useless introductory text, 2 paragraphs dedicated to how to refrigerate the meal, 9 inline advertisements and a mid-scroll ad) and having it return the actual recipe in Markdown in an Obsidian folder.

The other day I did that with a TikTok video! It was infuriatingly vague, so I extracted the mp4 using yt-dlp and fed the video file into Gemini Flash and told it to fill in the missing information and give me back a proper recipe.

Re: Claude Cookbook

#79

Post by mexicocitinluez »

deepfriedbits wrote: post #11
mindwok wrote: post #8

Gotta be honest, almost every "how to use AI" resource seems pointless to me. I'm either going to ask the AI how to do it, or if it's about using the AI then we can just bake it into the harness or wait for Anthropic/OpenAI to do it for me because they're always trivial. All of these resources on agentic workflows, managing agent memory, harness engineering, etc. appear to just be theatre to me.

Not only that, but all of this tooling around models has such a short shelf life as the models themselves grow in capabilities, they absorb the tooling. We've already seen it over and over again.

Amen on both your and OP's comments.

These tools work pretty well out-of-the-box. I'm sure I could squeeze out better token usage or streamline some tool calls, but it's not something I really want to focus on. Just like I don't want to endlessly configure my IDE, I don't really have patience with spending time on anything besides actually building something.

Re: Claude Cookbook

#80

Post by ryandvm »

embedding-shape wrote: post #37
cloakandswagger wrote: post #24

Earlier quoted context omitted.

Remember in 2023 when people thought "prompt engineering" would be the new software engineering and invested tons of time into learning CoT, ReAct, thread-of-thoughts, etc? Those were mostly obviated by reasoning models and harness updates by 2024. It seems pointless to invest energy into the latest/greatest AI technique or framework when they're going to either be absorbed or replaced on a 3 month cycle.

Isn't it clear that some people are better at working with/prompting LLMs than other people? Or is the idea that what you write to them and how you use them doesn't matter, it's all up to the model/harness? To me this seems clear, so then clearly this is a skill, which typically is called "prompt engineering". Specifically CoT or the other things you mention wasn't referred to as "prompt engineering" as far as I know…

I feel the same way about "prompt engineering" as I feel regarding the term "parkour" - you know, running and jumping on stuff.

Are people really putting on their resumes that they are capable of reading and writing and appropriately defining and limiting context? That's all prompt engineering is - it's being able to communicate effectively and elucidate your objectives.

Congratulations to all you English majors out there, you're about to make $350K/year.

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