> ## Documentation Index > Fetch the complete documentation index at: https://docs.wisdom.ai/llms.txt > Use this file to discover all available pages before exploring further. # Context Modeling Workflow This guide outlines the complete, multi-step process for modeling your business Context in WisdomAI. The steps are: 1. [Build the foundation](#step-1-build-the-foundation-with-a-semantic-layer) 2. [Define complex Metrics](#step-2-define-complex-metrics-with-reviewed-queries) 3. [Refine and harden your logic](#step-3-refine-and-harden-your-logic) 4. [Test and measure your impact](#step-4-test-and-measure-your-impact) The sections below describe each step of this workflow. ## Step 1: Build the foundation with a semantic layer Before you can tackle complex analysis, you must establish a solid foundation. This involves creating a business-friendly semantic layer over your raw database structures and enriching it with basic, domain-specific language. * **What you'll do**: Document tables and columns, define business entities, establish relationships, and add simple rules using natural language context. * **How to do it**: For a detailed walkthrough, refer to our guide on [Advanced Data Modeling: Creating Context](/setting-up-wisdom-ai/advanced-data-modeling-creating-context). ## Step 2: Define complex Metrics with Reviewed Queries For specific business calculations where an exact SQL structure is required, you should provide the AI with pre-vetted examples. This is ideal for complex metrics that might need window functions or nested queries. * **What you'll do**: Create a library of **Reviewed Queries** by pairing a natural language question with its ideal SQL response. * **How to do it**: The creation process is covered in our guide on [Advanced Data Modeling: Creating Context](/setting-up-wisdom-ai/advanced-data-modeling-creating-context). ## Step 3: Refine and harden your logic Once your foundational context is in place, you can apply advanced strategies to make your logic more robust, maintainable, and accurate. ### Optimize your SQL for Reviewed Queries The structure of your SQL has a significant impact on clarity and reusability. Follow these best practices to write more effective queries. * **Use Common Table Expressions (CTEs) for clarity.** For complex logic, breaking the query into understandable CTEs makes it more readable and allows for reuse in future metrics. * **Maintain consistent structures for related metrics.** When creating multiple variations of a metric, use a shared CTE to define the base calculation. This prevents logic from drifting between different reports. ### Combine Knowledge types for greater accuracy In some complex cases, the AI might not follow an SQL example perfectly, leading to logical errors like double-counting. You can prevent this by supplementing your Validated Queries with explicit instructions in the Language Knowledge. For example, if there is a pre-aggregated `country_population` column in a `statewise_population` table, adding `country_population` across states in the same country would lead to double-counting. An instruction might help to make sure this never happens; add a rule like: `"Never sum up country_population column across different rows for the same country"` to prevent incorrect aggregations. ## Step 4: Test and measure your impact Knowledge modeling is an iterative process. To validate your improvements and track accuracy over time, you must continuously test your setup using a dedicated evaluation framework. * **What you'll do**: Create **Evaluation Sets** containing test prompts and their expected SQL outputs, run evaluations, and analyze the reports to identify areas for improvement. * **How to do it**: Learn the complete testing workflow in our guide, [Use Evaluations Sets and Runs](/improve-wisdom-ai-responses/use-evaluations-sets-and-runs). ## Next steps Review query history and system activity to ensure compliance and accuracy. Learn how to rate answers and provide corrections to improve the system's accuracy. Understand the methods for verifying the correctness and reliability of generated answers.