How Bold BI AI Reduces Hallucinations and Protects Customer Data?
Overview
Bold BI AI uses multiple techniques to improve response accuracy and protect customer data when interacting with AI-powered features.
This article explains:
- How Bold BI AI reduces hallucinations.
- How schema optimization improves accuracy.
- How validation mechanisms improve reliability.
- How customer data is protected when using Azure OpenAI.
Understanding Hallucinations in AI
A hallucination occurs when an AI model generates information that is inaccurate, unsupported by the available data, or based on an incorrect interpretation of the dataset.
For example, if multiple tables contain similarly named columns such as Country, Region or State, an AI model may incorrectly identify which field should be used when generating a visualization or answering a natural language query.
Although hallucinations cannot be completely eliminated, they can be significantly reduced through schema optimization, structured prompting, and validation mechanisms.
Schema Optimization Features:
Bold BI provides several schema optimization capabilities that help AI models better understand dataset structures and generate more accurate responses.
Synonym Support:
Administrators can define synonyms for tables and columns to map business terminology to dataset fields.
For example:
| Business Term | Dataset Field |
|---|---|
| Revenue | SalesAmount |
| Customer | CustomerName |
| Region | SalesTerritory |
This enables the AI model to better interpret user requests that use business terms instead of technical field names.
Benifits:
- Improves interpretation of natural language queries.
- Reduces ambiguity across datasets.
- Increases the likelihood of selecting the correct tables and columns.
- Improves overall response accuracy.
Column Selection:
For datasets containing a large number of tables and columns, administrators can choose which columns are exposed to AI through the Query Designer.
Restricting the available schema helps:
- Reduce token consumption.
- Improve response time.
- Reduce ambiguity.
- Improve response accuracy.
This is especially beneficial for enterprise datasets with complex schemas.
Managing Large Schemas and Token Limits:
Large Language Models (LLMs) process information within a limited context window.
Providing unnecessary tables and columns can:
- Increase token usage.
- Increase response latency.
- Reduce the amount of useful context available to the model.
- Increase the likelihood of inaccurate responses.
To address this challenge, Bold BI allows administrators to expose only the required schema elements. Hidden or unused fields are excluded from AI requests, reducing prompt size and improving response quality.
Validation Mechanisms in Bold BI AI
In addition to schema optimization, Bold BI AI uses multiple validation layers to improve reliability and consistency.
Layered Validation:
Bold BI validates requests before they are sent to the language model and validates generated responses before presenting results to users.
This validation process helps identify potential issues early and reduces the likelihood of incorrect responses.
Structured Prompting:
Bold BI classifies requests into predefined task categories, including:
- Visualization creation
- Visualization updates
- Dashboard modifications
- Data summarization
- Question answering
Each task category uses structured prompts that guide the model toward the intended outcome and reduce ambiguity.
Benefits:
- More predictable responses.
- Better task alignment.
- Reduced hallucination risk.
- Improved consistency across AI interactions.
Controlled Output Generation:
Bold BI constrains AI-generated responses using available dataset metadata.
This helps ensure that generated outputs:
- Reference existing tables.
- Use valid columns.
- Follow expected response structures.
- Produce more predictable results.
Secondary Response Validation:
After a response is generated, Bold BI performs an additional validation step.
For visualization requests, the system verifies that:
- Referenced tables exist.
- Referenced columns are available.
- Required metadata is present.
- Generated configurations are valid.
If validation fails, Bold BI automatically refines the request and resubmits it to the model.This iterative validation process helps improve the accuracy and reliability of generated results.
Note: Validation mechanisms significantly reduce hallucinations but cannot completely eliminate them. Critical business decisions should always be reviewed by users.
Data Protection in Bold BI AI
Bold BI is designed with enterprise-grade security and privacy controls. Customer data is processed securely and is not used by Bold BI to train foundation AI models.
No Customer Data Used for Model Training:
Customer data processed through Bold BI AI is not used to train or improve foundation AI models.This helps ensure that customer information remains confidential and isolated from other workloads.
Azure OpenAI Privacy Controls:
When Azure OpenAI is configured, data handling follows Microsoft’s Azure OpenAI privacy commitments.
User prompts, responses, embeddings, and associated processing data are:
- Not shared with other customers.
- Not accessible to OpenAI.
- Not used to train OpenAI models.
- Not used to train Azure OpenAI foundation models.
- Not used to improve Microsoft or third-party products without explicit permission.
Any fine-tuned models created within Azure OpenAI remain available exclusively within the customer’s Azure environment.
Infrastructure Isolation:
Azure OpenAI Service operates entirely within Microsoft’s Azure infrastructure and does not route customer workloads through consumer AI services such as ChatGPT.
This helps organizations maintain security, privacy, and compliance requirements.
Conclusion
Bold BI AI reduces hallucinations through schema optimization, structured prompting, layered validation, controlled output generation, and response verification.
In addition, Bold BI provides strong privacy protections by ensuring that customer data is not used for model training and by leveraging Azure OpenAI’s enterprise-grade security and privacy controls.
These capabilities help organizations adopt AI-powered analytics while maintaining accuracy, security, and data privacy.
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