AI for Salesforce Developers: Requirements, Code, and Tests

AI for Salesforce developers means using an AI assistant to understand business requirements, write Apex classes and triggers, build Lightning Web Components, prepare test classes, and work through deployment planning. The challenge is that Salesforce development is org-specific: your org has custom objects, governor limits shaped by existing usage, integration patterns that affect what new code can do, and architecture decisions that were made before the current project. An AI that does not know those constraints will suggest code that would work in a clean org but fails in yours. Tools like fenbs keep org constraints, approved requirements, and architecture decisions in one record that every session reads before writing a line.

Salesforce developers also work under strict data governance rules. Production org data includes customer records, financial information, and personal data that must never be pasted into an AI conversation. The right boundary is clear: use AI with schemas, requirement descriptions, and anonymized examples; never with actual org data.

What AI handles well for Salesforce development

AI is most useful for the implementation work that follows a clear specification. Writing an Apex class that implements a described interface, generating a Lightning Web Component from a layout brief, writing test classes for a trigger with specified test scenarios, or producing boilerplate for a batch job or a scheduled class: these are tasks where a complete brief produces usable output.

AI is also useful for Salesforce documentation and code review. Explaining what a complex SOQL query returns, describing what a legacy trigger does before refactoring it, or identifying potential governor limit issues in an existing class: these tasks benefit from AI when you provide the relevant code.

Where AI is less reliable: knowing your org's specific configuration, which custom objects and fields exist, what integrations are active, what governor limit headroom is actually available in your specific workloads, and what the current deployment pipeline requires. Those facts must come from you.

Briefing the AI on your org's constraints

Before asking an AI to write Salesforce code, tell it the constraints that affect what you can build. The Salesforce API version your org uses. Whether you are on Classic or Lightning Experience. Which SOQL relationships and custom objects exist in the relevant domain. Whether you have any known governor limit pressures in the areas the new code will touch.

Architecture decisions matter too. Whether your org uses a service layer pattern for business logic. Whether trigger logic goes into trigger handlers or directly into the trigger. How integration callouts are structured. Whether there are sharing rules or territory management configurations that affect what SOQL returns for a given user context.

An AI given those constraints will produce code that fits the org. An AI without them will produce textbook Apex that may introduce patterns inconsistent with the rest of the codebase, or that hits governor limits under load that the simple test scenario does not reveal.

Writing Apex code with AI

The most useful Apex prompts combine what the code must do, how it fits into the existing architecture, and any governor limit constraints it must respect. A request to "write an Apex trigger handler for the Opportunity object that updates related Contact records when the stage changes" is more useful with the addition of "using the service layer pattern we use for all business logic, with no SOQL in loops, and respecting the existing before-trigger logic in OpportunityTriggerHandler."

Review AI-generated Apex for governor limit issues even when the code looks clean. AI may write a SOQL query inside a loop when there is a less obvious way to structure the same logic without it, or may not account for bulk execution patterns that your data volumes require. Test with a representative data volume in a sandbox, not just a single record in a developer org.

For complex business rules that came out of a requirements process, paste the relevant requirement text rather than summarizing it. An AI given the actual requirement wording is less likely to introduce an interpretation that differs from what the business specified.

Building Lightning Web Components

LWC development benefits from AI for component scaffolding, event handling patterns, and wiring to Apex methods. Give the AI the component's purpose, the data it needs, how it gets that data (wire service or imperative call), and any interaction events it needs to fire or handle.

LWC and Aura have different patterns. If your org still uses Aura components in parts of the codebase, specify which you are building. An AI that assumes LWC will produce code that does not work in an Aura context, and vice versa.

For components that need to be accessible, tell the AI. Salesforce's accessibility requirements for Lightning components are well-documented, but the AI will not apply them unless asked. Accessibility review should be part of the component review process.

Writing test classes

Test class coverage in Salesforce development is not optional: the platform requires a minimum coverage percentage for production deployments. AI can generate a first set of test scenarios quickly, but the scenarios need to cover the specific business rules and failure paths that matter for your requirements.

Give the AI the class or trigger under test, the business rules it implements, and the edge cases you already know about. Ask it to write test methods that cover the positive path, the known edge cases, and the error paths. Review the generated tests for whether the assertions actually verify the behavior the requirement specifies, not just that the code ran without an exception.

Data safety and what never to include in AI prompts

Salesforce orgs hold customer data that is regulated under your organization's data governance policies, and potentially under CCPA, HIPAA, GDPR, or other frameworks depending on your industry and customer base. Do not paste actual records, query results with real data, or org-specific identifiers into an AI conversation.

Describe schemas using field names and types, not actual values. Use anonymized examples for test data descriptions. If you need to show the shape of a data problem, construct a minimal anonymized example that illustrates the issue without including any real customer or account data.

The same applies to the fenbs project context: write requirement summaries, architecture decisions, and technical constraints. Never write account names, contact data, opportunity values, or any information that identifies real customers or deals.

How to do this with fenbs

1. Create a free account and add a project for your Salesforce development work.
2. Write org constraints as context notes: API version, architecture patterns, relevant custom objects and their relationships, and known governor limit considerations.
3. Add architecture decisions as Decisions and rules: trigger handler approach, service layer conventions, test data factory pattern, and any integration constraints.
4. Create tasks for each feature, bug, or technical improvement. Include the requirement text and the relevant objects in the task notes, with no actual customer data.
5. Connect a supported AI app from Integrations. Ask it to read the project context before writing any Apex, LWC, or test code.

For more on how individual developers use persistent AI context for coding projects, the solo developer AI coding agents guide covers the session workflow. For the broader picture of AI coding tools, what is Cursor AI covers one popular option.

What AI does not replace in Salesforce development

AI cannot run your code, observe how it behaves in a sandbox with real configuration, or determine whether a deployment to production will succeed. Those require access to the org. Test in a sandbox with representative data volumes and configuration before deploying to production.

Business requirements that are ambiguous need clarification from the business analyst or the stakeholder, not an AI interpretation. An AI that fills in an ambiguity with a plausible default will produce code that passes tests but does not implement what the business actually wanted. Clarify the requirement first.

Security review for sensitive operations, such as code that handles financial data or access-sensitive records, should be done by a qualified Salesforce architect or security specialist. AI code review is a first pass, not a security audit.