# Retrieval-Augmented Generation (RAG) Retrieval Augmented Generation (RAG) in askEdgi refers to answering questions by first retrieving trusted enterprise context and then generating responses based on that context. askEdgi is not a generic AI chatbot. It does not rely on assumptions or general knowledge. Instead, askEdgi: * Understands business terms defined by the organization * Knows what data exists and how it is governed * Considers structural relationships between datasets * Uses metadata, lineage, and contextual statistics * Suggests relevant and trusted assets before execution * Explains answers using enterprise context This approach ensures that responses are grounded in how the organization understands and manages its data. ## Business Value of RAG Business users frequently need answers to questions such as: * Which dataset should be used for a metric? * What does a business term mean? * Where does a number originate? * Why do different reports show different values? * Which datasets should be combined? These questions require understanding business meaning, structural relationships, and governance alignment. Retrieval Augmented Generation ensures: * Accurate answers * Consistent interpretation * Connected datasets * Trustworthy analysis ## Enterprise Context Used by RAG The enterprise context retrieved by askEdgi includes the following elements. | Context Type | Description | | -------------------------- | --------------------------------------------------- | | Business glossary | Approved business definitions and terminology | | Curated datasets | Trusted and governed data assets | | Governance information | Ownership, classification, and access controls | | Metadata and documentation | Business and technical descriptions | | Dataset relationships | Structural connections between assets | | Lineage information | Data movement and dependency paths | | Contextual statistics | Sample characteristics and value distributions | | Top values | Frequently occurring values used for interpretation | {% hint style="info" %} Sample statistics and top value summaries improve interpretation but remain secondary to governed metadata and definitions. {% endhint %} ## askEdgi Modes and the Role of RAG askEdgi operates in two clearly defined modes to support different user needs. Each mode has a clear purpose and boundary that ensures trust and predictability. ### Analysis Mode Analysis Mode is the default and primary mode. It supports the complete journey from understanding a question to generating insights. #### Purpose of Analysis Mode Analysis Mode supports the following activities: * Understanding a question * Identifying the correct data * Validating how datasets relate * Performing analysis * Receiving business-aligned explanations Analysis Mode supports the complete journey from discovery to insight without switching between guidance and execution modes. #### Understand RAG Usage in Analysis Mode In Analysis Mode, Retrieval Augmented Generation performs the following functions: * Interpret the business meaning behind a question * Retrieve relevant glossary definitions * Surface business and technical descriptions of assets * Evaluate asset metadata and documentation * Analyze relationships between datasets * Eliminate unrelated or disconnected tables * Confirm that selected datasets combine correctly Retrieval Augmented Generation ensures reasoning in a business context before execution begins. #### Understand Relationship Aware Intelligence When a question spans multiple datasets, askEdgi performs structural validation. askEdgi performs the following actions: * Confirm that selected assets are structurally connected * Avoid a combination of unrelated datasets * Suggest additional related assets only when required * Limit context expansion to what is necessary This prevents: * Incorrect joins * Over-selection of irrelevant tables * Misleading analysis * Loss of trust Datasets are validated as part of a connected data ecosystem rather than isolated objects. #### Understand Workspace First Execution Analysis Mode respects the Workspace as the execution boundary. Execution rules are as follows: * If tables remain pinned, analysis is restricted to pinned tables * If tables are not pinned, eligible workspace tables are considered * Additional catalog assets are surfaced only when necessary * Data outside the intended scope is not analyzed This ensures controlled execution. ### Discovery Mode Discovery Mode supports structured exploration of the Data Catalog. This mode does not use RAG and does not perform analysis execution. #### Purpose of Discovery Mode Discovery Mode supports: * Asset browsing * Data availability validation * Metadata understanding * Documentation review #### Understand Discovery Mode Behavior In Discovery Mode, askEdgi performs the following actions: * Retrieve assets from the catalog * Surface business descriptions and technical documentation * Apply governance-aware filters * Return metadata and definitions RAG-based reasoning does not occur in this mode. Discovery Mode provides clarity without execution. {% hint style="warning" %} Discovery Mode does not use RAG. Only Analysis Mode uses RAG. {% endhint %} ## How askEdgi Finds the Right Context in Analysis Mode The following sequence describes how askEdgi retrieves context and prepares for execution. ### Step 1: Determine Workspace Dependency askEdgi evaluates whether the request requires existing workspace data. Workspace data is required when the request: * Reads existing tables * Computes metrics from data * References workspace objects * Validates schemas Workspace data is not required when the request: * Requests an example * Requests a sample SQL or Python * Requires logical reasoning without data * Creates new structures without referencing existing data This separation improves clarity and efficiency. ### Step 2: Evaluate Existing Workspace Context askEdgi checks whether sufficient context already exists within the Workspace. If sufficient context exists, search expansion does not occur. ### Step 3: Enrich Business Understanding When additional clarity is required, askEdgi retrieves: * Glossary definitions * Asset descriptions * Metadata context This ensures the correct interpretation of business intent. ### Step 4: Suggest Relevant Assets When necessary, askEdgi identifies additional datasets aligned with business intent. Only governed and relevant assets are considered. ### Step 5: Validate Dataset Compatibility Before execution, askEdgi confirms: * Required attributes exist * Datasets are structurally connected * Necessary relationships are available * Required elements are complete If validation fails, execution does not proceed. {% hint style="warning" %} askEdgi stops and requests clarification instead of executing with incomplete data. {% endhint %} ### Step 6: Execute Analysis Execution occurs only after: * Context is sufficient * Relationships are confirmed * Required data elements exist Execution is intentional and validated. ## How askEdgi Handles Missing or Incomplete Information askEdgi stops intentionally to ensure accurate and trustworthy results. askEdgi stops when: * Required data is missing * Structural compatibility cannot be confirmed * Business context is unclear * Confidence is insufficient askEdgi does not: * Guess * Partially execute * Assume schema This results in predictable and trustworthy outcomes. ## RAG Trust in Analysis Mode The RAG framework in askEdgi relies on controlled enterprise grounding. RAG uses the following information sources: * Curated business descriptions * Technical documentation * Structured metadata * Relationship context between assets * Lineage information * Contextual data statistics, such as sample data characteristics * Top 50 values {% hint style="info" %} Contextual data statistics and top 50 values improve relevance and interpretation. Governed metadata and business definitions remain the primary reference. {% endhint %} askEdgi enforces the following controls: * Respect governance and access rules * Validate structural compatibility before dataset combination * Confirm required attributes before execution * Stop when information remains incomplete * Maintain clear separation between discovery and execution This layered grounding ensures business-aligned, structurally valid, and explainable responses. ## RAG Limitations Certain behaviors remain intentionally restricted to maintain trust. askEdgi avoids: * Guessing or fabricating answers * Ignoring governance controls * Combining unrelated datasets * Excessive expansion across the data ecosystem * Execution with incomplete schema validation Restraint remains a core system principle. ## Business Impact Organizations gain the following benefits: * Faster and safer data discovery * Reduced dependency on technical teams * Fewer incorrect dataset combinations * Strong structural validation before analysis * Higher trust in analytics and reporting * Streamlined workflows without mode confusion * Better alignment between business and data teams askEdgi serves as a reliable entry point to enterprise data knowledge and trusted analysis. ## Summary RAG forms the foundation that makes askEdgi: * Context-aware instead of generic * Relationship-aware instead of isolated * Schema-aware instead of assumptive * Dependency-aware instead of speculative * Trusted instead of uncertain * Business aligned instead of technically driven Clear separation between Analysis Mode and Discovery Mode with structured validation before execution ensures intentional, explainable, and trustworthy interactions. *** Copyright © 2026, OvalEdge LLC, Peachtree Corners, GA USA --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. 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