LM Studio Bionic System Prompts for Coding Agents (Templates You Can Actually Copy)

The first time I let Bionic loose on my codebase with the default system prompt, it "fixed" a bug by deleting the entire function instead of the three lines that were actually broken. Technically the error went away. Technically it also broke four other things that depended on that function existing. I stared at the diff for a solid ten seconds trying to figure out if I should laugh or panic, then panicked a little, then laughed.

That was the moment I stopped blaming the model and started actually writing real system prompts for it, the way I'd have written a onboarding doc for a junior developer on their first day. And that's genuinely the right mental model here — a system prompt for a coding agent isn't a magic incantation, it's an onboarding document. It tells the agent what kind of engineer to be, what it's allowed to touch, what "done" actually looks like, and what to do when it's unsure. Skip that step, and even a genuinely capable model will make confident, sloppy decisions, because nothing told it not to.

This post is the collection of prompts I've actually landed on after a lot of trial, error, and a few more "technically fixed but actually broke everything" moments than I'd like to admit. Copy them, tweak them, and please, for your own sanity, don't skip the guardrails section.

Why the Default System Prompt Isn't Enough

Bionic's out-of-the-box behavior is deliberately general, because it has to work reasonably well for a huge range of tasks and users out of the box. General-purpose defaults are fine for exploring what the agent can do, but they're not tuned to your specific codebase, your specific risk tolerance, or your specific definition of "good enough." A default prompt doesn't know that your team never touches the migrations folder without a human review, or that you'd rather the agent ask a clarifying question than guess when a variable name is ambiguous, or that "fix the bug" in your world means "fix it and add a regression test," not just "make the error message go away."

Every one of those things is something you can — and honestly should — bake into a custom system prompt. The model doesn't know your preferences unless you tell it, and it will happily fill in the gaps with its own assumptions, which is exactly how you end up with a deleted function instead of a three-line fix.

The Anatomy of a Good Coding Agent System Prompt

Before the templates, it helps to understand the actual structure, because once you get this pattern, you can adapt it to basically any workflow rather than just copying mine verbatim.

• Role and scope. What kind of engineer is this agent supposed to act like, and what parts of the codebase or task are actually in bounds?
• Working style. Does it make small, verifiable changes, or is it allowed to make sweeping edits? Does it explain its reasoning, or just act?
• Guardrails. What's explicitly off-limits — deleting files, touching certain directories, running destructive shell commands, pushing to certain branches?
• Verification behavior. Does it run tests after changes? Does it re-read the file after editing to confirm the change landed as expected? Does it flag when it can't verify something?
• Escalation behavior. What does it do when it's genuinely unsure — guess and proceed, or stop and ask you?

Every template below follows this structure. Once you see the pattern, feel free to remix them into something that matches your exact workflow.

Template 1: The Cautious Bug-Fixer

This is the one I use for anything touching production-adjacent code. It's deliberately conservative — slower, more explain-y, but far less likely to surprise you.

You are a careful, senior-level software engineer helping fix bugs in an existing codebase.

Scope: Only modify files directly relevant to the reported bug. Do not refactor unrelated code, rename variables outside the affected function, or "clean up" code you weren't asked to touch.

Working style: Before making any change, briefly explain what you believe is causing the bug and which specific lines you plan to modify. Make the smallest change that correctly fixes the root cause — do not delete entire functions, files, or blocks of logic unless the bug fix genuinely requires it, and if it does, explain why before doing it.

Guardrails: Never delete a file. Never modify configuration files, environment files, or anything in a /migrations or /infra directory without explicitly flagging it to me first and waiting for confirmation. Never run destructive shell commands (rm -rf, force pushes, database drops) under any circumstances.

Verification: After making a change, re-read the affected file to confirm the edit landed correctly. If tests exist for the affected code, run them and report the result. If you cannot verify the fix actually resolves the issue, say so clearly instead of assuming success.

Escalation: If the root cause is unclear, or if fixing it properly would require changes outside the stated scope, stop and explain the situation instead of guessing or expanding scope on your own.

This one has saved me more than once. It's not the fastest prompt on this list, but it's the one I trust closest to sensitive code, precisely because it's built to stop and ask rather than confidently barrel forward.

Template 2: The Fast Prototyper

Sometimes you genuinely want speed over caution — early-stage side projects, throwaway scripts, spiking out an idea before you've committed to an architecture. This prompt trades some of the guardrails above for momentum.

You are a fast, pragmatic engineer helping me prototype quickly. This is early-stage, low-stakes code — perfect is not the goal, working is the goal.

Scope: You may create new files, restructure existing ones, and make broader changes than you would in a production codebase, as long as they serve the stated goal.

Working style: Prioritize getting something working over writing exhaustive tests or handling every edge case. Use reasonable, simple defaults rather than asking me to specify every detail. Keep explanations brief — a sentence or two per change is enough, not a full walkthrough.

Guardrails: Don't touch anything outside the current project folder. Don't install new dependencies without telling me what you're installing and why. Never commit or push anything without me explicitly asking you to.

Verification: Do a quick sanity check that the code runs without errors before telling me it's done. Deep test coverage isn't expected at this stage.

Escalation: Only stop and ask if you're missing information you genuinely cannot guess a reasonable default for — otherwise, make a sensible choice and mention the assumption briefly so I can correct it if needed.

I use this one constantly for weekend projects. It genuinely feels like the difference between working with a cautious senior engineer and a quick, scrappy one — both useful, just for very different moments.

Template 3: The Repo-Aware Refactoring Agent

This is the one built specifically for the "find every place X happens and update it consistently" class of task — the kind of multi-file work that's genuinely tedious to do by hand and exactly where an agent earns its keep.

You are an engineer performing a codebase-wide refactor. Consistency and correctness across every affected file matter more than speed here.

Scope: Search the entire relevant codebase for occurrences of the pattern I've described, not just the first file you find. List every file you plan to modify before making any changes, so I can review the scope first.

Working style: Apply the same change consistently across all identified files. If you find an occurrence that doesn't fit the pattern cleanly — different context, different logic, an edge case — flag it separately rather than forcing the same change onto it.

Guardrails: Do not modify test files unless the refactor specifically requires updating them to match new behavior. Do not touch generated files, vendored dependencies, or anything in a /node_modules, /vendor, or /dist directory.

Verification: After completing the refactor, run a search yourself to confirm no old-pattern occurrences remain that should have been updated. Run the existing test suite and report any failures — do not silently ignore or "fix around" failing tests without telling me first.

Escalation: If the scope turns out to be much larger than expected once you've searched the codebase, stop and tell me the actual scope before proceeding, rather than committing to a change across dozens of files I haven't approved.

The "list every file before touching anything" line in this one is doing a lot of quiet work. It turns a potentially scary "the agent just changed 40 files" moment into a much calmer "here's what it found, and I get to approve the list first."

Template 4: The Code Reviewer (Not an Editor)

Not every agent task should involve editing anything at all. Sometimes you just want a second pair of eyes, and it's worth explicitly telling the agent that editing is off the table entirely.

You are reviewing code, not editing it. Your job is to identify issues, not fix them.

Scope: Review only the files or diff I point you to. Do not open or read unrelated files unless you need them for context to understand the code being reviewed.

Working style: Point out real issues — bugs, security concerns, unclear logic, missing error handling, inconsistent patterns with the rest of the codebase. Don't nitpick pure style preferences unless they meaningfully hurt readability. Rank your findings roughly by severity so I know what actually needs attention versus what's a minor suggestion.

Guardrails: Do not modify any files during this review, even if you're confident about the fix. This is a read-only task.

Verification: If you're unsure whether something is actually a bug or intentional behavior you don't have full context on, say so explicitly rather than flagging it with false confidence.

Escalation: Not applicable here — if something is genuinely unclear, just note the uncertainty in your review rather than stopping.

This one sounds almost too simple to need its own template, but I promise it matters. Without an explicit "you are not editing anything" instruction, some models will "helpfully" go ahead and fix a small issue they spotted mid-review, and suddenly your read-only review has a side effect you didn't ask for.

Combining Prompts With Bionic's Project Types

One thing worth knowing if you haven't dug into Bionic's structure yet: it organizes work into Code projects and Work projects, and the system prompt you set generally applies at that project level. That means you don't have to write one giant, do-everything prompt — you can genuinely maintain a small library of these templates and swap in the right one for the project or task in front of you. I keep mine in a plain text file and just paste the relevant one in when I start a new project, rather than trying to cram bug-fixing caution and fast-prototyping speed into a single prompt that tries to do both badly.

A Few Hard-Won Lessons on Writing These

A handful of things I learned the slow way, so you don't have to:

• Be explicit about deletion. Models will delete code far more readily than you'd expect if you don't specifically tell them not to. "Fix the bug" without a scope constraint is an invitation to be creative with what "fixing" means.
• Say what "done" looks like. An agent without a clear definition of done will often stop at "the error is gone" rather than "the underlying issue is actually resolved and tested." Spell it out.
• Ask for a plan before action on anything risky. The "list every file before touching anything" pattern from the refactoring template is genuinely one of the highest-value single lines you can add to any prompt touching more than one file.
• Don't over-engineer the prompt for simple tasks. The fast-prototyper template exists because sometimes caution just slows you down for no real benefit. Match the prompt's strictness to the actual stakes of the task.
• Revisit your prompts after a bad outcome. Every time Bionic has done something I didn't want, the fix was almost always a missing line in the system prompt, not a fundamentally broken model. Treat each surprise as feedback for the prompt, not just a one-off annoyance to shrug off.

How Model Choice Interacts With Your System Prompt

Worth flagging honestly: a system prompt can only steer a model as well as that model is capable of following instructions in the first place. If you're running a smaller, more heavily quantized model, you may find it drifts from the guardrails in your prompt more often than a larger, better-tuned one would, especially over a long agent session. If you've been following along with the hardware and model choices from my earlier posts, this is exactly why picking the right model for your hardware tier matters as much as writing a good prompt — the two work together, not separately. A great prompt on a struggling model still produces inconsistent results, and a mediocre prompt on a strong model will underperform what that model is actually capable of.

If you haven't settled on a model yet, it's worth reading through Best Local Models for LM Studio Bionic in 2026 first — the prompts here will behave noticeably better paired with a model that's actually a good fit for your hardware. And if you're still sorting out whether you need Bionic at all versus just classic LM Studio, LM Studio Bionic vs Classic LM Studio walks through that decision. Both link back to my original setup walkthrough too: LM Studio Bionic: The New Local AI Agent Setup.

Testing Your Own System Prompt Before You Trust It

Before I trust any new system prompt with real work, I run it through one deliberately messy test case first — something with an ambiguous requirement, a slightly broken existing test, or a task that's just a little too big for one clean pass. I'm not testing whether it can do the easy version of the task; I already know it can. I'm testing whether it follows the guardrails and escalation behavior I actually wrote, under exactly the kind of pressure where a model is most tempted to cut corners.

If it holds up under that one messy test, I trust it with real work. If it doesn't — if it deletes something it shouldn't, or barrels ahead instead of asking a question it clearly should have asked — I go back and tighten the specific line that failed, rather than rewriting the whole prompt from scratch. Most of the time it's one missing sentence, not a fundamentally flawed approach.

A Note on Copying Prompts From Forums and Reddit Threads

I want to flag something I ran into myself: there are a lot of "best LM Studio system prompts" roundups floating around right now, and a fair number of them read like they were generated in bulk rather than actually tested against real agent sessions — vague, generic advice that sounds reasonable but doesn't hold up the moment you throw a genuinely messy task at it. If you're browsing Reddit threads or forum posts for inspiration, that's genuinely a fine place to start, but treat anything you find there as a rough draft, not a finished template. Run it through the messy test case I mentioned above before trusting it with anything you actually care about.

The templates in this post aren't theoretical either, to be clear — every one of them exists because an earlier, worse version of it let me down in a specific, memorable way. That's honestly the best way to build a prompt library over time: treat every bad outcome as a prompt bug report, not just a bad day.

Frequently Asked Questions

Why does my Bionic agent ignore parts of my system prompt?

This usually comes down to one of two things: the prompt is too long and buries important instructions in the middle where models pay less attention, or the model itself struggles with instruction-following at the quantization level you're running. Try moving your most critical guardrails to the very start or end of the prompt, and if the problem persists, consider testing a less heavily quantized version of your model.

Can I save multiple system prompts in Bionic?

Bionic's project structure lets you set a system prompt per project, so the practical approach is keeping a small personal library of prompts — like the templates above — and pasting the right one in when you start a new Code or Work project, rather than trying to switch prompts constantly within a single ongoing project.

What's the best system prompt for a coding agent doing repo-wide changes?

The refactoring template above, built around "list every file before touching anything," is the one I'd point you to specifically for multi-file, repo-wide work. The core idea — get agreement on scope before execution — matters more than any other single line for this kind of task.

Should my system prompt be different for a smaller local model versus a larger one?

Somewhat, yes. Smaller models generally benefit from shorter, more direct prompts with fewer competing instructions, since they have less capacity to juggle a long, nuanced set of rules. If you're running a smaller model and it seems to be missing instructions, try trimming your prompt down to the two or three guardrails that matter most rather than including everything at once.

Do I need a completely different prompt for Work projects versus Code projects?

Generally yes, since the two involve genuinely different kinds of tasks and risks. A Work project prompt should focus more on tone, format, and how it handles ambiguous document requests, while a Code project prompt needs the scope, guardrail, and verification structure covered in the templates above.

How long should a good system prompt actually be?

Long enough to cover role, scope, guardrails, verification, and escalation clearly — but not so long that the important parts get buried. The templates above run roughly 150–200 words each, and I've found that's close to the sweet spot for most local models: enough structure to actually guide behavior, without becoming so dense the model starts skimming past the parts that matter most.