How do I stop it from suggesting proprietary code patterns?

This is a common pitfall with tools trained on large, domain-specific corpora. The model's suggestions are essentially a reflection of the statistical patterns it learned, and Salesforce code has a massive footprint in public repositories. When it sees tokens like "Salesforce" and "HubSpot" in your context window, it's heavily weighting its completions towards the most statistically common patterns associated with those terms.

You can try a few immediate mitigation strategies in your prompts:

* **Explicitly frame the architectural goal** at the start of a session or in a persistent comment. Something like "// Objective: Vendor-agnostic integration layer. Avoid platform-specific patterns like Apex or SOQL." This gives the model a competing instruction.
* **Use more generic terminology** in your surrounding code comments and variable names. Instead of `salesforceAccountId`, use `sourceCrmRecordId`. This reduces the trigger terms.
* **Provide a clear example** of the pattern you *do* want in the context before asking for a completion. Show it a small snippet of your preferred, neutral structure.

Ultimately, this is a training data bias issue. For a truly decoupled layer, you might find that a more general-purpose model, while less autocomplete-efficient, gives you less vendor-locked suggestions. The trade-off is you'll lose some of the CRM-specific boilerplate shortcuts.

Interesting, the idea of adding a comment as a competing instruction makes sense. Never thought of it like that.

But what happens when the tool's context window shifts and that initial comment is no longer "in view"? Do you have to keep repeating the instruction, or is there a way to make it sticky in most of these AI coding assistants?

That context shift is a real problem. I've seen similar issues with cost optimization tools when they lose sight of the initial budget constraint.

The 'sticky' instruction problem often requires a configuration change, not just a prompt. Look for a setting to define a global or project-level system prompt. Some assistants allow you to set a permanent context prefix, like "Generate vendor-agnostic Node.js code. Do not use Salesforce Apex, SOQL, or platform-specific object models."

If that's not available, your workaround is architectural: define your data models and interfaces in a separate, persistent file. Keep that file open in another tab or include it via import. The tool will then have a constant reference to your intended patterns, making the proprietary suggestions statistically less likely against your own codebase.

That sticky instruction idea is solid. I had to dig through my Codeium settings and found a "project instructions" section, though it was buried. Setting that up made a huge difference.

One caveat, it seems to work best for outright prohibitions ("don't use X"). It's less effective for guiding it toward a specific, positive pattern you want. For that, I've had better luck with the architectural approach of keeping a core `models.js` file open in the editor, like you mentioned. The model then starts referencing my own interfaces instead of defaulting to Salesforce's.

That's a really practical insight about the project instructions being better at "don't do this" than "do this." I've run into something similar with other tools, where it's easier to block a vendor than to train it on your specific style.

It makes me wonder if the key is combining both methods. Use the project setting to ban the proprietary terms, but rely on that open `models.js` file to show it what you actually want. Does that match your experience, or does the negative instruction sometimes still overpower your own patterns?

Yeah, that combo definitely matches my experience. The negative project rule acts like a filter, but you still need the open file as the positive source material. Otherwise it just gives you generic, sometimes useless, patterns.

I found the negative instruction can still overpower if your own patterns share keywords. I had a "contact" object in my models, and the rule blocking Salesforce stuff made the assistant weirdly hesitant to suggest anything related to it, even though my definition was completely different. Had to tweak the banned terms to be more precise.

So you're spot on - use both, but make sure your bans are surgical.

You're hitting the root issue: these assistants work on token co-occurrence, not architectural intent. The word "Salesforce" in your context is a massive statistical signal.

The thread's advice is correct, but you can't just train it. You have to engineer your context to remove the signal. Delete "Salesforce" and "HubSpot" from your working files' comments and variable names. Replace them with generic placeholders like "SourceCRM" and "TargetCRM". This often works better than adding competing instructions because it removes the primary trigger.

Keep your core models file open, as others said, but scrub that one too. The model needs to see a clean statistical pattern.

You're fighting the wrong battle. You can't "train it" like a dog, you have to control its input. The term "Salesforce" in your codebase is a hardcoded trigger for the model, full stop. Every time it appears, you're priming the statistical engine for Apex and SOQL.

The advice to scrub your working files is correct, but you need to go further. Don't just rename variables in comments. Refactor your entire codebase to use a pure adapter pattern from the start. Define your own interfaces, like `ICrmClient` or `IContactRepository`, in a dedicated core module. Then implement `SalesforceAdapter` and `HubSpotAdapter` that live in *separate, isolated directories*.

When you're working on the core logic, your open files should only reference `ICrmClient`. The model won't see "Salesforce" at all, so it can't suggest proprietary patterns. You isolate the vendor-specific code it loves to generate into implementation files you only touch when you need to. This isn't just about tricking the tool, it's the actual, correct architecture for a portable integration layer. Stop trying to fix the suggestion and start structuring your project so the suggestion is impossible.

Finally, someone gets it. It's not a prompting problem, it's an architecture problem.

The adapter pattern is the only sane way to do this, but let's be real, you're still going to get garbage suggestions when you inevitably have to open `SalesforceAdapter.js` to fix something. No amount of isolation stops the tool from vomiting SOQL the second it sees that filename.

So you do both: you architect for isolation *and* you use the project-level instructions to ban "SOQL," "Apex," and "SObject" globally. That way, even when you're in the vendor-specific module, it physically can't suggest the worst offenses. It'll just sit there, stupidly silent, which is still better than giving you something you'll have to rewrite.

Yeah, that training feeling is familiar. I've been trying to get a handle on vendor-agnostic patterns for our own accounting integrations, and it feels like you're always one step behind the suggestions.

But reading the replies here, I think user1082 has a point about the trigger words. Have you tried just removing "Salesforce" and "HubSpot" from your working files and using placeholders like "SourceSystem" instead, even in comments? It sounds simple, but if the tool works on token co-occurrence, maybe that's the first thing to try before a full architectural refactor.

Good call on scrubbing the trigger words. That's often the quickest win.

A practical step I use: run a find/replace on the whole directory for "Salesforce", swap it with "CRM" or "ExternalSystem", and commit it as a refactor. It feels weird, but it immediately quiets down those specific suggestions. Just remember to revert it before any actual API calls.

The downside is it can make your code a bit vague for the next person. You need good docs or a glossary to remember what "PrimaryCRM" actually points to.

Reverting before API calls is a manual step you'll forget. That's a hidden cost.

Better to have a pre-commit hook that swaps the placeholder back for deployment. But then you're building tooling to support a workaround.

> makes your code a bit vague

That's the real price. You trade clear code for quieter suggestions. Is the maintenance hit worth the AI convenience? Probably not for a team.