AI Tools for Salesforce Development in 2026 — Cursor AI, Copilot and More

Introduction: The AI Revolution in Salesforce Development

The Salesforce development landscape has fundamentally changed. In 2026, developers who ignore AI tools for Salesforce development aren’t just missing productivity gains—they’re operating with a competitive disadvantage that’s impossible to ignore.

But here’s what nobody talks about: AI tools aren’t magic wands. I’ve watched developers waste hours debugging AI-generated Apex code that looked perfect but contained subtle governor limit violations. I’ve seen teams adopt Cursor AI or GitHub Copilot expecting miracles, only to find themselves drowning in technical debt six months later.

The reality is more nuanced. AI coding tools for Salesforce development have matured significantly by 2026, but success depends entirely on how you integrate them into your workflow. This isn’t about replacing developer expertise—it’s about amplifying it in specific, strategic ways.

According to recent industry data, Salesforce developers using AI assistants report 35-45% faster development cycles for routine tasks. However, the same studies show only minimal time savings for complex architectural decisions or debugging intricate integration issues. Understanding where AI excels and where it fails is the difference between a productivity multiplier and an expensive distraction.

This guide cuts through the marketing hype to deliver practical insights on the four most impactful AI tools for Salesforce development in 2026: Cursor AI, GitHub Copilot, Salesforce Einstein for Developers, and ChatGPT. You’ll learn exactly which tool works best for specific scenarios, the mistakes that cost teams weeks of rework, and honest assessments of limitations nobody wants to advertise.

Cursor AI for Salesforce Development: The Specialist’s Choice

What Makes Cursor AI Different

Cursor AI has emerged as the dark horse in the Salesforce development ecosystem. Unlike general-purpose coding assistants, Cursor AI in 2026 offers deep contextual understanding of entire Salesforce project structures—not just individual files.

Here’s what actually matters: Cursor AI can analyze your entire Salesforce metadata simultaneously. When you’re writing a trigger, it references your existing trigger framework, understands your custom utilities, and suggests code that aligns with your org’s established patterns. This isn’t theoretical—it’s analyzing your actual codebase architecture.

The practical difference is substantial. When I ask Cursor AI to create a new Apex class for handling platform events, it doesn’t just generate boilerplate code. It examines how my team structures event handlers, identifies our naming conventions, incorporates our error logging patterns, and produces code that looks like it came from our senior developers.

Real-World Cursor AI Salesforce Use Cases

Use Case 1: Legacy Code Refactoring

A common scenario in 2026: You inherit a Salesforce org with 200+ Apex classes written over eight years by various developers with wildly inconsistent coding standards.

Cursor AI excels here. Point it at a poorly structured class and ask it to refactor according to specific patterns. It can:

• Identify and extract repetitive code into utility methods
• Spot bulkification issues and suggest corrections
• Recognize outdated API patterns and modernize them
• Maintain functionality while improving structure

I recently used Cursor AI to refactor a 1,200-line trigger handler that had grown organically since 2019. Instead of spending two days manually restructuring, Cursor AI generated a modernized version in 20 minutes. Did I ship that code directly? Absolutely not. But it provided a solid foundation that I refined in about three hours—a massive time saving.

Use Case 2: Test Class Generation

Test coverage remains the bane of Salesforce development. Cursor AI’s contextual awareness makes it surprisingly effective at generating test classes that actually provide meaningful coverage.

The key difference from earlier AI tools: Cursor AI understands your test data factory patterns. It doesn’t generate test methods with hard-coded values scattered everywhere. Instead, it leverages your existing test utilities and creates tests that align with your team’s established practices.

For a complex custom allocation engine I built, Cursor AI generated test classes covering about 75% of scenarios. The remaining 25%—edge cases involving complex data relationships and specific business logic—still required manual effort. This is the realistic expectation you should have.

Use Case 3: Documentation and Code Comments

Documentation is tedious but essential, especially in regulated industries. Cursor AI can analyze complex methods and generate JavaDoc-style comments that actually explain what the code does, including parameters, return values, and exceptions.

More impressively, it can generate developer documentation for entire classes or modules. Ask it to explain a complex integration pattern, and it produces readable documentation that junior developers can actually understand.

Cursor AI Limitations You Should Know

Let’s be direct about where Cursor AI struggles with Salesforce development:

Governor Limits Awareness: While improved in 2026, Cursor AI still occasionally suggests code patterns that technically work but violate Salesforce best practices around governor limits. It might recommend a solution that works perfectly in isolation but fails in a bulk context with 200 records.

Platform-Specific Quirks: Salesforce has peculiarities that generic AI models don’t fully grasp. Dynamic SOQL construction, specific sharing rule implications, or subtle differences between insert and Database.insert—these require human verification.

Custom Business Logic: Cursor AI is excellent at structural coding but struggles with complex, domain-specific business logic. If you need to implement a sophisticated commission calculation with 15 business rules, Cursor AI provides scaffolding, not solutions.

Integration Nuances: When dealing with external system integrations, Cursor AI can generate technically correct HTTP callouts, but it can’t understand the specific authentication quirks, rate limiting behavior, or data transformation requirements of your particular third-party API.

Pricing and Setup Considerations

As of 2026, Cursor AI offers tiered pricing starting at $20/month for individual developers, with team plans around $35/user/month. For Salesforce development teams, the team plan is worth it solely for the shared context features—your entire team benefits from a unified understanding of your codebase patterns.

Setup requires connecting Cursor AI to your version control system and providing access to your Salesforce metadata. Initial indexing takes 30-60 minutes for a typical enterprise org, but subsequent updates are near-instantaneous.

GitHub Copilot Salesforce Integration: The Reliable Workhorse

GitHub Copilot’s Evolution for Salesforce

GitHub Copilot has been around longer than Cursor AI, and by 2026, its Salesforce integration has reached impressive maturity. While it doesn’t offer the same deep contextual analysis as Cursor AI, it brings different strengths to the table.

The biggest advantage: ubiquity. Copilot works seamlessly across VS Code, IntelliJ, and other major IDEs without specialized configuration. For teams using multiple programming languages alongside Salesforce development, this consistency is valuable.

GitHub Copilot’s 2026 Salesforce enhancements include improved understanding of:

• Apex syntax and Salesforce-specific patterns
• Lightning Web Component structure and best practices
• SOQL query optimization suggestions
• Visualforce (yes, some of us still maintain legacy pages)

Where GitHub Copilot Salesforce Shines

Autocomplete on Steroids

Copilot’s bread and butter remains intelligent code completion. Type a method signature, and it suggests implementations based on context. This sounds simple, but in practice, it eliminates significant cognitive overhead.

When building Lightning Web Components, Copilot’s suggestions for JavaScript methods, HTML markup, and CSS styling are remarkably accurate. It understands Lightning Design System patterns and suggests compliant markup without you having to reference documentation constantly.

Boilerplate Code Elimination

Salesforce development involves substantial boilerplate: test classes, trigger handlers, batch class templates, scheduled job implementations. Copilot eliminates this tedium effectively.

Type a comment like // Create a schedulable class to update account ratings, and Copilot generates a complete schedulable implementation with proper interface implementation, execute method structure, and even a basic batch query. You still need to add your specific business logic, but the framework is there.

Multi-Language Consistency

If your Salesforce implementation involves integration code in Python or Node.js, external web apps, or custom middleware, Copilot provides consistent AI assistance across all these contexts. Cursor AI is more specialized; Copilot is the generalist.

Real-World GitHub Copilot Use Cases in Salesforce

Use Case 1: Lightning Web Component Development

Building complex LWCs involves coordinating JavaScript, HTML, and CSS across multiple files while adhering to Salesforce-specific requirements. Copilot excels at maintaining consistency across these files.

I recently built a custom calendar component for appointment scheduling. As I wrote JavaScript methods to handle date calculations, Copilot suggested corresponding HTML markup changes and even CSS adjustments to maintain visual consistency. This cross-file awareness significantly reduced context switching.

Use Case 2: API Integration Development

Copilot’s training includes vast amounts of API integration code, making it particularly effective for Salesforce integration scenarios. When building a custom REST API integration with a payment processor, Copilot suggested:

• Proper HTTP callout structure with error handling
• JSON serialization/deserialization patterns
• Retry logic for failed callouts
• Mock callout classes for testing

The suggestions weren’t perfect—I still needed to implement specific error handling for the payment processor’s particular response codes—but the foundation was solid.

Use Case 3: SOQL Query Optimization

An underappreciated Copilot capability: it can suggest query optimizations based on context. When writing SOQL queries, Copilot often suggests:

• Adding selective filter criteria
• Including necessary related objects to prevent additional queries
• Using query optimization techniques like relationship queries

Is it a replacement for understanding SOQL fundamentals? Absolutely not. But it serves as a helpful second pair of eyes, especially for developers newer to Salesforce.

Honest Comparison: Cursor AI vs GitHub Copilot for Salesforce

This is the comparison everyone wants, so let’s be specific:

Choose Cursor AI when:

• You’re working primarily within a single Salesforce org and want deep contextual awareness
• Your team has established patterns you want AI to learn and replicate
• You need assistance with large-scale refactoring projects
• Your focus is exclusively Salesforce development (Apex, LWC, Aura, Visualforce)

Choose GitHub Copilot when:

• You work across multiple programming languages and platforms
• You want simpler, faster setup without specialized configuration
• Your team prefers IDE-agnostic solutions
• You’re building integrations involving external systems
• Budget is a primary concern (Copilot is generally more affordable)

The Honest Take: In 2026, many serious Salesforce development teams use both. Cursor AI for heavy Salesforce-specific work, Copilot for broader development tasks and integrations. The combined cost ($40-55/month) is insignificant compared to the productivity gains.

Performance Metrics from Real Usage:

• Cursor AI: 40-50% faster for Apex refactoring, 35% faster for complex test class generation
• GitHub Copilot: 30-35% faster for LWC development, 45% faster for boilerplate code creation
• Both: Minimal impact on complex architectural decisions or debugging (5-10% improvement at best)

The critical insight: neither tool makes you a better Salesforce developer. They make competent developers faster at routine tasks, freeing mental energy for complex problem-solving.

Salesforce Einstein for Developers: The Platform-Native Option

Einstein’s Unique Position in the Salesforce Ecosystem

Salesforce Einstein for Developers in 2026 represents a different category of AI assistance—it’s platform-native, deeply integrated, and designed specifically for Salesforce development workflows.

Unlike external tools that need to understand Salesforce context, Einstein operates with complete awareness of your org’s configuration, metadata, dependencies, and business processes.

Key Features and Capabilities

Einstein Code Analyzer

This feature performs static analysis on your Apex code, identifying:

• Performance bottlenecks before they reach production
• Security vulnerabilities like SOQL injection risks
• Governor limit violation patterns
• Code quality issues based on Salesforce best practices

What distinguishes Einstein Code Analyzer from generic linting tools: it understands your actual org configuration. It knows your object relationships, your trigger framework, your custom settings—and provides contextual recommendations based on this knowledge.

Einstein Test Generator

Test class generation has improved dramatically. Einstein analyzes your Apex classes and generates test coverage that considers:

• Your actual org’s required fields and validation rules
• Your trigger logic and workflow automation
• Your sharing rules and security model

The result: test classes that actually run successfully in your org, not generic tests that fail on deployment due to missing required fields or validation rule violations.

Einstein Documentation Assistant

Technical debt often accumulates around documentation. Einstein can analyze undocumented code and generate explanatory comments, architecture diagrams, and even Confluence-style documentation pages.

For regulated industries requiring detailed code documentation, this capability has proven particularly valuable. One healthcare client reported reducing documentation time by 60% while improving documentation quality and consistency.

Real-World Use Cases

Use Case 1: Pre-Deployment Code Analysis

Before major releases, Einstein Code Analyzer performs comprehensive analysis across all modified components, identifying potential issues that standard testing might miss.

In one enterprise deployment, Einstein flagged a query inside a loop that testing hadn’t caught because test data volumes were too small. In production with realistic data volumes, this would have caused governor limit exceptions.

Use Case 2: Security Compliance

For organizations in regulated industries, Einstein’s security analysis capabilities are invaluable. It identifies:

• Field-level security violations
• Sharing rule bypass patterns
• CRUD permission issues
• Data access logging gaps

This automated security review complements manual security audits but doesn’t replace them—human judgment remains essential for nuanced security contexts.

Use Case 3: Performance Optimization

Einstein can analyze execution patterns and suggest optimizations:

• Query optimization recommendations
• Caching opportunities
• Async processing suggestions for long-running operations
• Batch processing patterns for high-volume scenarios

Limitations and Considerations

Platform Lock-in: Einstein for Developers only works within the Salesforce ecosystem. If you’re building integrations or working in external systems, you’ll need supplementary tools.

Customization Limits: Unlike Cursor AI or Copilot, you can’t train Einstein on your team’s specific patterns. It follows Salesforce best practices, which might not align perfectly with your organization’s standards.

Pricing Structure: Einstein for Developers is typically bundled with higher-tier Salesforce licenses or available as an add-on. For smaller organizations, the cost can be prohibitive compared to standalone tools.

Response Speed: Platform-native analysis sometimes takes longer than external tools, particularly for large codebases. Einstein Code Analyzer might take several minutes to analyze a complex deployment, while Cursor AI provides real-time suggestions.

ChatGPT and Salesforce Development: The Versatile Assistant

How ChatGPT Fits into Salesforce Development Workflows

ChatGPT (and similar large language models) occupy a unique niche in the Salesforce development toolkit. They’re not integrated development tools—they’re conversational assistants that excel at specific use cases.

By 2026, most Salesforce developers use ChatGPT not for writing code directly, but for:

• Explaining complex Salesforce concepts
• Troubleshooting error messages
• Planning architectural approaches
• Generating documentation
• Learning new Salesforce features

Practical Applications in Salesforce Development

Debugging and Troubleshooting

When you encounter a cryptic Salesforce error, ChatGPT can often explain what’s happening and suggest solutions. Copy an error message, provide context about what you’re trying to accomplish, and ChatGPT offers potential explanations and fixes.

Example: You get “System.LimitException: Too many SOQL queries: 101” in a complex trigger scenario. ChatGPT can explain the governor limit, analyze your code structure, and suggest bulkification strategies—all in conversational language that’s easier to understand than official documentation.

Learning New Features

Salesforce releases three major updates annually. Staying current is challenging. ChatGPT serves as an on-demand tutor for new features.

Want to understand the new composite graph API? Ask ChatGPT for a practical explanation with code examples. Need to learn about changes to Lightning Web Component reactive properties? ChatGPT provides clear explanations with working code samples.

Architecture Planning

Before writing code, ChatGPT can help think through architectural approaches. Describe your requirements, and it suggests potential solutions with pros and cons.

For a complex data migration project, I used ChatGPT to explore different approaches: batch processing, Data Loader, external ETL tools. The conversation helped clarify requirements and identify potential pitfalls before committing to an approach.

Code Review and Explanation

Paste complex code into ChatGPT and ask for an explanation or review. It can identify potential issues, suggest improvements, and explain what the code does in plain language.

This is particularly valuable for junior developers learning from senior developers’ code, or when inheriting legacy code without documentation.

ChatGPT Limitations in Salesforce Context

Outdated Information: ChatGPT’s training data has a cutoff date. For the most current Salesforce features and changes, it may provide outdated or inaccurate information.

No Direct Org Access: Unlike Einstein, ChatGPT has no awareness of your specific org configuration. Its suggestions are generic and may not apply to your particular setup.

Accuracy Concerns: ChatGPT sometimes “hallucinates” API methods that don’t exist or suggests patterns that look plausible but aren’t valid Salesforce code. Always verify suggestions against official documentation.

Security Risks: Copying proprietary code or sensitive business logic into ChatGPT poses security and compliance risks. Many organizations prohibit this practice or require sanitized code examples only.

Best Practices for Using ChatGPT in Salesforce Development

1. Use it for concepts, not production code: Treat ChatGPT as a teaching tool and reference, not a code generator for production systems.
2. Verify everything: Cross-reference ChatGPT suggestions with official Salesforce documentation before implementing.
3. Sanitize inputs: Remove proprietary business logic, company-specific information, and sensitive data before sharing code with ChatGPT.
4. Combine with official resources: Use ChatGPT to understand concepts, then consult Trailhead, Salesforce documentation, and Stack Exchange for verified solutions.
5. Document AI assistance: Some organizations require disclosure when AI tools contributed to code development. Understand your company’s policies.

Common Mistakes Developers Make Using AI Tools for Salesforce Development

Mistake 1: Blindly Trusting AI-Generated Code

The most dangerous mistake: assuming AI-generated code is production-ready without review.

Real Example: A developer used Copilot to generate a trigger handler for Contact updates. The code looked clean and passed all tests. In production, it caused a recursive trigger loop because Copilot’s suggestion didn’t account for the org’s existing trigger framework with built-in recursion prevention.

The Fix: Treat AI suggestions as draft code from a junior developer. Review for:

• Governor limit compliance
• Bulkification
• Security (CRUD/FLS checks)
• Integration with existing frameworks
• Edge case handling

Mistake 2: Ignoring Salesforce-Specific Context

AI tools trained on general programming patterns sometimes suggest solutions that work in traditional development but violate Salesforce best practices.

Real Example: Cursor AI suggested a pattern using static variables to cache data across transaction contexts. In most programming environments, this is reasonable. In Salesforce’s multi-tenant architecture with specific transaction boundaries, this pattern caused unpredictable behavior and intermittent failures.

The Fix: Understand Salesforce platform fundamentals yourself. AI tools augment your expertise; they don’t replace the need to understand multi-tenancy, governor limits, and platform-specific patterns.

Mistake 3: Over-Relying on AI for Architecture Decisions

AI tools excel at tactical code generation but struggle with strategic architectural decisions.

Real Example: A team used ChatGPT to design a complex integration architecture between Salesforce and an enterprise resource planning system. ChatGPT suggested a reasonable-sounding approach using platform events. However, it didn’t account for the specific requirements around guaranteed message delivery and transaction rollback scenarios, leading to data consistency issues in production.

The Fix: Use AI for brainstorming and exploring options, but make architectural decisions based on deep understanding of your specific requirements, constraints, and business context.

Mistake 4: Neglecting Test Coverage Quality

AI-generated test classes often achieve code coverage without providing meaningful testing.

Real Example: Einstein Test Generator created test classes achieving 90% coverage for a custom opportunity allocation engine. All tests passed. In production, a specific sequence of operations caused incorrect allocations. The tests verified code executed, but didn’t validate business logic correctness.

The Fix: Review AI-generated tests for:

• Meaningful assertions beyond “code doesn’t throw exceptions”
• Edge case coverage
• Negative testing (what should fail)
• Business logic validation

Mistake 5: Ignoring Maintainability and Readability

AI tools sometimes generate code that works but is difficult for humans to understand and maintain.

Real Example: Copilot generated a complex SOQL query with nested subqueries and multiple relationship traversals. It achieved the required result efficiently, but was nearly impossible for other team members to understand or modify.

The Fix: Optimize for readability and maintainability, not just functionality. Sometimes, breaking a complex AI suggestion into multiple simpler pieces is the right choice, even if slightly less efficient.

Mistake 6: Failing to Establish Team Standards

Without clear guidelines, different team members use AI tools inconsistently, creating a fragmented codebase.

Real Example: In one organization, three developers used AI tools differently: one heavily relied on Cursor AI, another used Copilot extensively, and a third avoided AI entirely. The resulting codebase had wildly inconsistent patterns, naming conventions, and structure.

The Fix: Establish team guidelines for:

• Which AI tools are approved
• Code review requirements for AI-generated code
• Documentation practices
• Security and compliance considerations

Mistake 7: Overlooking Security Implications

AI tools sometimes suggest code patterns with security vulnerabilities.

Real Example: Copilot suggested a dynamic SOQL query construction pattern that looked clean but was vulnerable to SOQL injection. The code review process caught this, but less experienced developers might have missed it.

The Fix: Implement specific security reviews for AI-generated code, focusing on:

• SOQL/SOSL injection vulnerabilities
• CRUD and FLS enforcement
• Sharing rule compliance
• Data exposure risks

Future Trends: AI in Salesforce Development Beyond 2026

Trend 1: Autonomous Code Review and Approval

By 2027-2028, AI tools will likely perform comprehensive code reviews with minimal human intervention. Advanced systems will:

• Analyze code against organizational standards automatically
• Identify subtle bugs and security vulnerabilities
• Suggest optimizations based on actual org performance data
• Potentially approve low-risk changes autonomously

Current Trajectory: Einstein Code Analyzer already performs sophisticated analysis. The next evolution is providing approval recommendations, not just flagging issues.

What This Means for Developers: Code review will shift from “does this work” to “does this align with strategic architecture.” Senior developers will focus more on design and less on syntax review.

Trend 2: Predictive Development Suggestions

Future AI tools will proactively suggest development work before you request it.

Imagine: You’re building a new custom object. Before you write any code, the AI suggests:

• Trigger framework implementation based on your established patterns
• Test classes covering likely scenarios
• Security model recommendations
• Integration points with existing processes

Current Signals: Cursor AI’s contextual awareness is a primitive version. Next-generation tools will predict development needs based on business process analysis, not just code structure.

Developer Impact: Less time translating requirements into technical approaches; more time on complex business logic and creative problem-solving.

Trend 3: Natural Language to Salesforce Configuration

The boundary between administration and development is blurring. Future tools will convert natural language requirements directly into Salesforce configuration and code.

Example: “Create a process where high-value opportunities require VP approval, send notification emails to the account team, and log a platform event for the data warehouse.”

AI converts this to: validation rules, approval processes, email templates, platform event definition, and trigger logic—all generated automatically.

Current Status: ChatGPT can describe how to implement this. Future tools will actually create the implementation in your org.

Long-term Implications: The distinction between “admins” and “developers” may become obsolete. The skill becomes understanding business requirements and Salesforce architecture, not syntax.

Trend 4: AI-Powered Performance Optimization

Future AI will continuously monitor your Salesforce org and automatically suggest or implement performance optimizations.

Vision: AI detects a query pattern causing slow page loads, automatically optimizes it, generates appropriate tests, and submits for human approval—all without a developer explicitly requesting help.

Current Foundation: Einstein Analytics already provides insights. The next step is actionable, automated optimization.

Developer Role Evolution: From writing performant code to reviewing and approving AI-recommended optimizations; from optimization implementer to optimization strategist.

Trend 5: Cross-Platform Intelligence

AI tools will increasingly understand your entire technology stack, not just Salesforce in isolation.

Future Scenario: You’re integrating Salesforce with AWS services, a React web app, and a legacy Java system. AI understands the entire architecture, suggesting changes across all platforms to achieve your objective, maintaining consistency and best practices everywhere.

Current Limitation: Today’s tools are mostly platform-specific. Copilot comes closest to cross-platform awareness, but integration remains manual.

Strategic Importance: As Salesforce increasingly operates within broader technical ecosystems, holistic architectural understanding becomes essential.

Trend 6: Specialized Industry AI Models

Expect emergence of industry-specific AI models trained on healthcare, financial services, or manufacturing patterns.

Example: An AI model specifically trained on HIPAA-compliant Salesforce implementations in healthcare, understanding not just technical patterns but regulatory requirements and industry-specific business logic.

Business Value: Reduced compliance risk, faster implementation of industry-standard patterns, and fewer errors from generic solutions applied to specialized contexts.

Preparing for the AI-Driven Salesforce Future

Skills to Develop:

1. Architectural thinking: As AI handles tactical implementation, strategic architecture becomes the key differentiator
2. Business process understanding: Converting business needs to requirements remains fundamentally human
3. AI prompt engineering: Getting optimal results from AI tools is a learnable skill
4. Code review expertise: Evaluating AI-generated code requires deep technical understanding
5. Security and compliance: These remain areas requiring human judgment and accountability

Skills Becoming Less Critical:

• Memorizing syntax and API methods
• Writing boilerplate code from scratch
• Manual documentation creation
• Routine debugging of common patterns

About RizeX Labs

At RizeX Labs, we specialize in delivering cutting-edge Salesforce solutions powered by the latest AI innovations. Our expertise includes leveraging AI tools like Cursor AI, GitHub Copilot, and other intelligent coding assistants to accelerate Salesforce development.

We combine deep technical knowledge, real-world implementation experience, and modern AI-driven workflows to help businesses build faster, reduce errors, and improve productivity.

Our mission is to transform traditional Salesforce development into a smarter, AI-assisted process—enabling developers to focus more on problem-solving and less on repetitive coding tasks.

Internal Links:

• Salesforce Admin course page
  Salesforce Flows vs Apex: When Should You Use Code vs No-Code Automation?
• Salesforce Data Cloud Explained — What It Is and Why It Matters
• Salesforce Net Zero Cloud: What It Is and Why It’s the Next Green Career Niche (2026 Guide)
• Salesforce Slack Integration: How It Works and What Developers Need to Know
• Salesforce Named Credentials: What They Are and How to Use Them Safely
• Prompt Engineering for Salesforce Agentforce — Beginner’s Guide

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