[simonw]

LLMs are restricted by their output limits. Most LLMs can output 4096 tokens - the new Claude 3.5 Sonnet release this week brings that up to 8196.

As such, for one-shot apps like these there's a strict limit to how much you can get done purely though prompting in a single session.

I work on plenty of larger projects with lots of LLM assistance, but for those I'm using LLMs to write individual functions or classes or templates - not for larger chunks of functionality.

[tharant]

> As such, for one-shot apps like these there's a strict limit to how much you can get done purely though prompting in a single session.

That’s an important detail that is (intentionally?) overlooked by the marketing of these tools. With a human collaborator, I don’t have to worry much about keeping collab sessions short—and humans are dramatically better at remembering the context of our previous sessions.

> I work on plenty of larger projects with lots of LLM assistance, but for those I'm using LLMs to write individual functions or classes or templates - not for larger chunks of functionality.

Good to know. For the larger projects where you use the models as an assistant only, do the models “know” about the rest of the project’s code/design through some sort of RAG or do you just ask a model to write a given function and then manually (or through continued prompting in a given session) modify the resulting code to fit correctly within the project?

[simonw]

There are systems that can do RAG for you - GitHub Copilot and Cursor for example - but I mostly just paste exactly what I want the model to know into a prompt.

In my experience most of effective LLM usage comes down to carefully designing the contents of the context.

[tharant]

> There are systems that can do RAG for you

My experience with Copilot (which is admittedly a few months outdated; I never tried Cursor but will soon) shows that it’s really good at inline completion and producing boilerplate for me but pretty bad at understanding or even recognizing the existence of scaffolding and business logic already present in my projects.

> but I mostly just paste exactly what I want the model to know into a prompt.

Does this include the work you do on your larger projects? Do those larger projects fit entirely within the context window? If not, without RAG, how do you effectively prompt a model to recognize or know about the relevant context of larger projects?

For example, say I have a class file that includes dozens of imports from other parts of the project. If I ask the model to add a method that should rely upon other components of the project, how does the model know what’s important without RAG? Do I just enumerate every possible relevant import and include a summary of their purpose? That seems excessively burdensome given the purported capabilities of these models. It also seems unlikely to result in reasonable code unless I explicitly document each callable method’s signature and purpose.

For what it’s worth, I know I’ve been pretty skeptical during our conversations but I really appreciate your feedback and the work you’ve been doing; it’s helping me recognize both the limitations of my own knowledge and the limitations of what I should reasonably expect from the models. Thank you, again.

[simonw]

Yes, I paste stuff in from larger projects all the time.

I'm very selective about what I give them. For example, if I'm working on a Django project I'll paste in just the Django ORM models for the part of the codebase I'm working on - that's enough for it to spit out forms and views and templates, it doesn't need to know about other parts of the codebase.

Another trick I sometimes use is Claude Projects, which allow you to paste up to 200,000 tokens into persistent context for a model. That's enough to fit a LOT of code, so I've occasionally dumped my entire codebase (using my https://github.com/simonw/files-to-prompt/ tool) in there, or selected pieces that are important like the model and URL definitions.