Revolutionizing SOQL with AI: Transforming Developer Queries

Updated 30/04/2026

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Artificial Intelligence (AI) is no longer a supporting tool in software development — it is a central force powering code generation, testing, optimisation, and deployment. This shift is fundamentally changing how developers work on a daily basis, automating repetitive tasks and enabling higher-quality output.

For Salesforce developers, this transformation extends directly to SOQL (Salesforce Object Query Language). AI tools can now generate, optimise, and review SOQL queries from natural-language prompts, making the process faster, more accessible, and less error-prone. But AI-generated queries still need human review for selectivity, governor limits, and org-specific context.

For a broader look at how AI is reshaping the Salesforce developer role beyond just SOQL, see the companion article: AI & the Salesforce Developer.

Impact of AI on SOQL Usage

AI is significantly changing how developers approach writing and optimizing SOQL queries:

• Automated Query Generation: AI tools: including Salesforce’s own Agentforce for Developers (Einstein for Developers), general-purpose assistants like GitHub Copilot and Claude, and conversational models, can write accurate SOQL queries from simple natural-language prompts, saving time and reducing syntax errors for developers and admins.
• Query Optimization: AI can analyze existing queries to suggest optimizations, such as indexing fields or restructuring queries for better performance. This helps developers improve query efficiency and reduce execution time.
• Context-Aware Code Generation: Integrated AI assistants in Salesforce environments help compose SOQL as part of end-to-end solutions including Apex classes, Lightning components, and automation flows.
• Enhanced Developer Productivity: AI tools assist with error detection, code review, and documentation generation, allowing developers to focus on higher-level problem solving and design

The Changing Role of SOQL Expertise

With AI’s growing role in handling SOQL, the need for deep SOQL expertise is evolving:

• As AI handles more technical aspects of query writing and optimization, developers can focus more on strategic data analysis and decision-making.
• Developers now often start by prompting AI with query needs in natural language, review and tweak the generated SOQL, and then integrate it into their code.
• While traditional SOQL skills remain valuable, learning to use AI tools effectively is becoming increasingly important. This includes understanding how to interact with AI systems and interpret their outputs.
• AI makes SOQL more accessible to non-technical users, allowing a broader range of professionals to interact with Salesforce data without needing in-depth query language knowledge.

Example Prompts for AI-Driven SOQL

Developers can use AI to assist with SOQL through example prompts like:

• Generate a SOQL query using the standard Salesforce data model to retrieve all contacts located in California who have open cases. Include the following fields in the output: Contact Name, Contact Email, Case Number, Case Status, and Case Opened Date. Sort the results by the Case Opened Date in ascending order.
• Optimize this SOQL query to improve performance by suggesting indexing strategies and restructuring:

SELECT Name, Amount, Account.Name,
       (SELECT Contact.LastName FROM OpportunityContactRoles WHERE IsPrimary = true),
       (SELECT Product2.Name FROM OpportunityLineItems WHERE TotalPrice > 1000)
FROM Opportunity
WHERE CloseDate >= THIS_FISCAL_YEAR AND Probability > 70
AND StageName = 'Closed Won' AND Account.Industry = 'Technology'
AND (SELECT COUNT() FROM OpportunityLineItems) > 5

• Identify potential errors in this SOQL query:

SELECT Name, Amount, Account.Name
FROM Opportunity
WHERE CloseDate >= THIS_FISCAL_YEAR AND Probability > 70
AND StageName = 'Closed Won' AND Account.Industry = 'Technology'
AND (SELECT COUNT() FROM OpportunityLineItems) > 5

Exporting Data Models for AI Integration

To fully leverage AI, developers can integrate AI with Salesforce data models in several ways. Some AI tools are integrated into development environments like VS Code, allowing them to read the data model directly through the metadata. This integration provides AI with the necessary context to generate accurate queries by understanding object relationships, fields, and data types.

Alternatively, developers can use tools like Salesforce Inspector to export the data model. For example, a SOQL query such as:

SELECT QualifiedApiName, DeveloperName, EntityDefinition.DeveloperName, DataType, IsIndexed, Description
FROM FieldDefinition
WHERE EntityDefinition.DeveloperName IN ('Account', 'Contact')

can be used to export details of the data model needed for the query. By feeding this exported data model into AI systems, developers can train AI to understand the structure and relationships within the Salesforce environment, enabling more accurate query generation and insights.

AI Tools for SOQL

Below is a table of some popular AI tools that are transforming how developers work with SOQL

Challenges and Considerations

While AI offers significant benefits, there are some challenges and considerations to consider.

Potential Errors: Over-reliance on AI-generated queries can lead to errors if not properly reviewed by developers. Continuous learning is crucial to keep AI models aligned with evolving data structures and business needs, requiring regular updates and training. To effectively integrate AI with SOQL, maintain a balance between AI automation and human oversight. Regularly audit data quality and refine AI models to ensure accurate query generation.

Data Privacy: Do not feed data into AI to help create a query unless you know the data has no sensitive or identifying information and you fully understand the confidentiality policy of the AI tool. Most AI systems have a policy that theymay use the data provided as input by the user in order to “provide, maintain and improve” the service.

Ethical AI Use: As AI becomes more prevalent in software development, ethical considerations must be addressed. Ensure fairness by avoiding biased data and algorithms, and prioritize transparency by making AI decision-making processes understandable to users. Accountability is key; establish clear guidelines for AI use and regularly review systems to ensure compliance with ethical standards.

Best Practices: To effectively integrate AI into SOQL workflows, maintain a balance between AI and human oversight. Ensure data quality by regularly auditing datasets and refining AI models. Stay informed about AI advancements and update systems accordingly. Encourage collaboration between AI specialists and Salesforce developers to optimize query generation and performance.

Emerging Trends: The future of AI and SOQL is marked by advancements in machine learning algorithms, enabling more sophisticated query optimization and data analysis. As AI becomes more integrated into Salesforce, expect to see increased automation of complex data tasks, such as predictive analytics and real-time data processing. The rise of natural language processing (NLP) will further simplify query generation, allowing users to interact with data using conversational language.

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Conclusion

AI is reshaping how developers work with SOQL, making it faster to write, optimise, and review queries. AI tools automate query generation, improve performance, enhance accuracy, and enable natural-language interactions — collectively boosting developer productivity and freeing Salesforce developers to focus on architecture and strategic decisions rather than repetitive syntax.

But AI-generated SOQL still requires human judgment. Query selectivity, governor limit awareness, data model context, and security considerations are all areas where the developer’s platform knowledge remains essential. The most effective workflow combines AI speed with developer oversight: prompt, review, refine, deploy.

This evolution highlights the growing importance of AI literacy — prompt engineering, context curation, and integrating AI outputs into robust solutions — as an essential skill for modern Salesforce developers.