By Deepika Geetla, HEXstream Data Engineer

When users interact withthe Oracle Analytics AI Assistant, a multi-step process takes place behind the scenario. In just a few seconds, the assistant translates a natural language question into a meaningful visualization—giving professionals fast, accurate and contextual insights.

How AI turns questions into visual insights

Let us say, “What was the peak electricity demand in Texas last August?”

1.    User Question Received: Oracle Analytics captures your natural language query.

2.    Dataset Identification: Oracle Analytics identifies the most appropriate dataset based on your indexed metadata and recognizes relevant attribute matches.

3.    Prompt Construction: Oracle Analytics prepares a comprehensive prompt for the Large Language Model (LLM) that includes dataset metadata, column names, synonyms you have defined, and the user’s original question.

4.    LLM Processing: The language model evaluates the prompt and translates the question into a structured query with appropriate refinements.

5.     Query Verification & Execution: Oracle Analytics validates the query against your data
model and executes it against your database.

6.    Visualization Generation: Based on the query results and data composition, Oracle Analytics produces an appropriate visualization.

Let us decode the AI into action

Imagine a customer service manager asks, “How many new electric connections were added in California last month?”

Oracle Analytics AI Assistant would:

·      Map “new electric connections” to the “New Service Activations” metric and apply a COUNT or SUM aggregation as needed.

·      Recognize “California” as a value in the State field.

·      Interpret “last month” as a dynamic filter based on the current date, applied to the Connection Date or Activation Month dimension.

·      Generate a bar or KPI visualization showing the total number of new connections for California for the selected period.

Why tuning makes or breaks your AI experience

While the Oracle Analytics AI Assistant is powerful, its effectiveness depends on how well you prepare your data and metadata. As Oracle notes, “Gen AI is Magical, but It Is Not Magic.” The performance, accuracy and user satisfaction all hinge on proper tuning.

Three areas where tuning drives success

1. Accuracy amplifier: Getting the right answers, not just any answers

• Without proper tuning: The AI Assistant may misinterpret questions or select incorrect fields. For example, if your dataset has similar columns like “Product ID” and “Product Name,” a question about “product sales” might confuse the AI about which field to use.
• With proper tuning: When metadata is prepared with clear field names and synonyms, the AI Assistant correctly maps user questions to the right data elements. A question like “Show income by product” will accurately identify “income” as referring to your “Net Profit” measure.

2. Speed booster

• Without proper tuning: Indexing everything creates overhead and slower processing. The LLM must navigate irrelevant field names and values, which slows down interpretation.
• With proper tuning: A focused index enables quicker processing. The first answer is more likely correct, so the overall time to insight is significantly reduced.

3. Trust builder

• Without proper tuning: When the AI Assistant frequently misunderstands questions, users become frustrated and lose trust.
• With proper tuning: Users gain confidence when the AI Assistant consistently returns correct answers. This positive experience encourages continued use and wider adoption.

The Fantastic Four: Principles for AI analytics excellence

For ongoing optimization of your AI Assistant implementation, follow these key principles:

1. Iterate: Continuously tune your metadata based on user feedback and observed usage patterns. Monitor which questions succeed or fail and refine accordingly.

2. Describe: Ensure your metadata clearly describes your data in business terms that users
naturally employ, not technical database terminology.

3. Clarify: Use synonyms to resolve ambiguity, especially for similarly named fields:

• Month → Birth Month
• Month → Hire Month
• Month → Role Change Month

4. Less is more: Be selective in what you expose to the AI Assistant. A focused, well-curated dataset will yield better results than including everything.

The Oracle Analytics AI Assistant works out of the box—but tuning your metadata makes a significant difference. It improves query accuracy, speeds up analysis, and builds trust across your analytics users.

By investing just a little time in indexing the right fields, renaming technical labels, and adding thoughtful synonyms, you can transform how your teams engage with data. What once required dashboards, filters and manual SQL can now be accomplished with a single question.

When done right, Oracle’s AI Assistant bridges the gap between complex utility datasets and the way people naturally think and ask questions—turning data into conversations, and conversations into actionable insights.

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