RFD 002: Using LLMs in the JP Project

• Status: Implemented
• Category: Process
• Authors: Jean Mertz git@jeanmertz.com
• Date: 2025-07-17
• Required by: RFD 003

Summary

LLM usage is encouraged in the JP project. Contributors are free to use LLMs for reading, research, editing, debugging, code review, and code generation. All work product — code, prose, reviews, comments — is the responsibility of the person who submits it, regardless of how it was produced.

The Core Principle

You own what you ship. Every commit, comment, review, and message carries the name of the person who submitted it. That attribution is a statement of responsibility. If you use an LLM to help produce an artifact, the artifact is yours — you reviewed it, you stand behind it, you will maintain it. "An LLM wrote it" is not a defense, an excuse, or a disclaimer. It is irrelevant.

This is not a burden. It is the same standard that applies to any tool: a compiler, a linter, a code generator, a Stack Overflow answer, a colleague's suggestion. The tool helps; you decide.

Values

Several of our values inform how we think about LLM use. Listed here in priority order:

• Responsibility: Our lodestar. However powerful they may be, LLMs are a tool, acting at the behest of a human. Contributors bear responsibility for the artifacts they create, whatever automation was employed to create them. Human judgment remains firmly in the loop: even when an LLM generates an artifact we will use (code, tests, documentation, prose), the output is the responsibility of the human using it.

• Rigor: LLMs are double-edged with respect to rigor. Wielded carefully, they can sharpen our thinking by pointing out holes in reasoning or providing thought-provoking suggestions. Used recklessly, they replace crisp thinking with generated flotsam. LLMs are useful in as much as they promote and reinforce our rigor.

• Empathy: Be we readers or writers, there are humans on the other end of our communication. As we use LLMs, we must keep in mind our empathy for that human — the one consuming our writing, reviewing our code, or reading our commit messages.

• Teamwork: We are working together on a shared endeavor. LLM use must not undermine the trust we have in one another. In some contexts, LLM usage is expected (using an LLM to help debug a tricky issue is natural); in others, it can erode trust (submitting LLM-generated code for review without having reviewed it yourself shifts the burden to your reviewer). The distinction is not about disclosure — it is about whether you have taken responsibility for the work.

• Urgency: LLMs afford an opportunity to do work more quickly, but pace must not come at the expense of responsibility, rigor, empathy, and teamwork. Too many projects have treated LLMs as an opportunity to increase velocity over all else, without regard for direction or quality.

Uses of LLMs

LLM use varies widely, and the implications vary accordingly.

LLMs as Readers

LLMs are superlative at reading comprehension, able to process and meaningfully comprehend documents effectively instantly. This can be powerful for summarizing documents, answering specific questions about a specification, or understanding an unfamiliar codebase.

Using LLMs to assist comprehension has little downside. One caveat: when uploading a document to a hosted LLM (ChatGPT, Claude, Gemini, etc.), be mindful of data privacy. Ensure the model will not use the document to train future iterations. This is often opt-out and controlled via account preferences.

LLMs as Researchers

LLMs are well-suited for the kind of light research tasks for which one would historically use a search engine, especially as hallucination has been attenuated and sourcing has improved.

For more involved research, the quality of results can vary widely. One should be careful about drawing too much confidence from the lengthy, well-formatted nature of an LLM-powered report. Even if a report appears well-sourced, the sources themselves may be incorrect — a seemingly authoritative source found by an LLM may itself be an LLM hallucination.

When engaging LLMs as researchers, follow citation links to learn from the original sources. Treat LLM-researched content as a jumping-off point, not a finished product.

LLMs as Editors

LLMs can be excellent editors. Engaging an LLM late in the writing process — with a document already written and broadly polished — allows for helpful feedback on structure, phrasing, and consistency without danger of losing one's own voice.

A cautionary note: LLMs are infamous pleasers. The breathless praise from an LLM is often more sycophancy than analysis. This becomes more perilous the earlier one uses an LLM in the writing process: the less polish a document already has, the more likely the LLM will steer toward something wholly different — praising your genius while offering to rewrite it for you.

LLMs as Writers

While LLMs are adept at reading and can be terrific editors, their writing is more mixed. At best, writing from LLMs is hackneyed and cliché-ridden; at worst, it brims with tells that reveal the prose was automatically generated.

LLM-generated writing undermines the authenticity of not just one's prose but of the thinking behind it. If the prose is automatically generated, might the ideas be too? The reader can't be sure — and increasingly, the hallmarks of LLM generation cause readers to disengage.

LLM-generated prose also undermines a social contract: absent LLMs, it is presumed that of the reader and writer, the writer has undertaken the greater intellectual exertion. If prose is LLM-generated, a reader cannot assume the writer understands their own ideas — they might not have read the output they tasked the LLM to produce.

The guideline is to generally not use LLMs to write prose that others will read as your own thinking — RFDs, design documents, substantive PR descriptions, important issue writeups. This is not an absolute. An LLM can be part of the writing process. Just consider your responsibility to yourself, to your own ideas, and to the reader.

For mechanical prose — commit messages, changelog entries, boilerplate documentation — LLM generation is fine, provided you review the output.

LLMs as Code Reviewers

LLMs can make for good code reviewers, especially when targeted to look for a particular kind of issue (error handling gaps, API misuse, missed edge cases). But they can also make nonsense suggestions or miss larger architectural problems. LLM review is a supplement, not a substitute for human review.

LLMs as Debuggers

LLMs can be surprisingly helpful debugging problems. They serve as a kind of animatronic rubber duck, helping inspire the next questions to ask. When debugging a vexing problem, one has little to lose by consulting an LLM — provided it does not displace collaboration with colleagues.

LLMs as Programmers

LLMs are remarkably good at writing code. Unlike prose (which should be handed in polished form to an LLM to maximize its efficacy as an editor), LLMs can be quite effective writing code de novo. This is especially valuable for code that is experimental, auxiliary, or throwaway.

The closer code is to the system we ship, the greater care needs to be shown. Even with tasks that seem natural for LLM contribution (e.g., writing tests), one should still be careful: it is easy for LLMs to spiral into nonsense on even simple tasks.

Where LLM-generated code is used, self-review is essential. LLM-generated code should not be submitted for peer review if the responsible engineer has not themselves reviewed it. Once in the loop of peer review, wholesale re-generation in response to review comments makes iterative review impossible — the reviewer cannot see what changed. Address review comments with targeted edits, not by re-prompting.

Using LLMs to aid in programming requires judgment and balance. They can be extraordinarily useful across the entire spectrum of software activity and should not be dismissed out of hand. But any implicit dependency on an LLM to comprehend or evolve a system must be resisted: we do not want to produce code that only an LLM can maintain.

Anti-Patterns

LLM Mandates

LLM use is encouraged but never required. As with any tool, contributors should feel empowered to use LLMs, but not obligated to do so. We trust each other to choose the best tool for the job.

LLM Shaming

Conversely, shaming others for using LLMs is equally unwelcome. As LLMs become the foundation for technologies like search and code completion, drawing a bright line of purity becomes increasingly impractical — and counterproductive. People will make different choices about when and how to use LLMs. That's fine.

LLM Anthropomorphization

An LLM is not a person. It cannot be held accountable, cannot take responsibility, and cannot be a team member. Treating it as one — giving it a persona, referring to its "opinions," deferring to its "judgment" — obscures the fact that a human is always responsible for what the LLM produces.

Yes, JP itself is an LLM-based tool with a name and a persona. The irony is not lost on us. The persona is a user interface choice, not an attribution of agency. JP's output is the user's responsibility.

Undisclosed Bulk Generation

Submitting large volumes of LLM-generated code or prose without review, then expecting others to debug, maintain, or untangle it, is a failure of responsibility. The problem is not that an LLM was used — it is that the submitter did not take ownership of the output. The result is the same as submitting any other unreviewed work: it creates a burden for the team.

Guidelines Summary

Activity	Guideline
Reading comprehension	Encouraged. Mind data privacy with
	hosted models.
Research	Useful starting point. Verify sources.
	Don't trust blindly.
Editing prose	Encouraged, especially late in the
	process.
Writing prose	Generally write it yourself. LLMs fine
	for mechanical prose.
Code review	Useful supplement. Not a substitute for
	human review.
Debugging	Use freely. Don't displace
	collaboration.
Writing code	Encouraged with care. Self-review before
	peer review. No wholesale re-generation
	during review.
Commit messages	Fine. Review the output.
Tests	Fine with care. Verify the tests
	actually test what they claim.

Determination

LLM use is encouraged in the JP project. The tools are powerful and getting better. We should use them.

But the fundamental contract does not change: you own what you ship. Your name on a commit, a review, a comment, or a document means you stand behind it. The tool you used to produce it is your business. The quality and correctness of the result is your responsibility.

References

• Oxide RFD 576: Using LLMs at Oxide — the direct inspiration for this document.
• RFC 3: Documentation Conventions — the original spirit of writing things down.
• RFD 001: The JP RFD Process — how we write and manage RFDs.