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.
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
Sample statistics and top value summaries improve interpretation but remain secondary to governed metadata and definitions.
askEdgi Modes and the Role of Business Context Layer (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 (tables and files)
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 (from both object and column level)
Surface business and technical descriptions of objects
Evaluate asset metadata and documentation
Analyze relationships between objects
Eliminate unrelated or disconnected objects
Confirm that selected objects 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 uses askEdgi’s Business Context Layer (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. This mode retrieves the attributes from the following object types:
Table
Name, Title, Synonyms, Source Description, Table location (e.g., catalog.schema.table), Profile status, Data quality index, Curation score, Rating, Certification, Critical Data Element, Navigation URL, Governance Roles, Business Glossary Association, Tags, Data Products, Custom Fields, Entity Relationships, Column Details (Column name, Business description), and Sample Data.
File
Name, Title, Synonyms, File location, File type, Profile status, Data quality index, Curation score, Rating, Certification (certified by, type, date), Critical Data Element, Navigation URL, Governance Roles, Business Glossary Association, Tags Classification, Data Products, Custom Fields, Column Details, and Sample Data
Report
Name, Title, Synonyms, Report location,
Report type, Parent report name, Curation score, Rating, Certification, Critical Data Element, Navigation URL, Governance Roles, Business Glossary Association, Tags, Data Products, Custom Fields, and Column Details.
API
Name, Title, Synonyms, API location / path, URL, Method, Endpoint, Curation score, Rating, Certification, Critical Data Element, Navigation URL, Governance Roles, Business Glossary Association, Tags Classification, Data Products, Custom Fields, and Attribute Details
Code
Name, Title, Code location, Job type, Curation score, Rating, Certification (certified by, type, date), Critical Data Element, Navigation URL, Business Glossary Association, Tags Classification, and Custom Fields
Schema
Name, Title, Synonyms, Database name, Curation score, Rating, Certification, Critical Data Element, Total table count, Navigation URL, Governance Roles, Business Glossary Association, Tags Classification, Custom Fields
Term
Name, Domain, Category, Subcategory, Governance status, Curation score, Rating, Navigation URL, Tags, and Custom Fields
Tag
Name, Navigation URL, and Custom Fields
Data Products
Name, Data Domain, Parent Subdomain, Status (Draft / Published), Criticality, Sensitivity (Public / Internal / Confidential / Restricted), Curation score, Rating, Navigation URL, Tags, and Custom Fields
Data Story
Name, Story Zone, Parent Story, Child Stories, Author, Rating, Navigation URL, and Tags Classification
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.
askEdgi stops and requests clarification instead of executing with incomplete data.
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
Contextual data statistics and the top 50 values improve relevance and interpretation. Governed metadata and business definitions remain the primary reference.
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.
Metadata-Aware Retrieval and Ranking
This section describes the enhanced Retrieval-Augmented Generation (RAG) capabilities in askEdgi, which introduce metadata-aware retrieval, governance-driven ranking, and intelligent embedding strategies to improve the relevance, trustworthiness, and explainability of results.
Retrieval Architecture
The askEdgi retrieval process follows a structured, multi-stage pipeline that balances semantic relevance with governance trust signals.
Stage 1 – Relevance Retrieval
User query is transformed into a semantic embedding.
The system performs:
Vector similarity search to capture semantic intent
Keyword-based search to capture exact matches across metadata attributes
Results from both approaches are combined to generate a candidate set of data assets (typically 20–30 objects).
Stage 2 – Governance Re-Ranking
Candidate assets are re-evaluated using governance and trust signals.
Each asset is assigned a final ranking score based on relevance and governance strength.
The top-ranked assets (typically 5–10) are selected and passed to downstream processing for response generation.
Key Characteristics
Retrieval prioritizes both intent relevance and metadata quality.
Governance signals influence ranking, ensuring that curated and trusted assets are preferred.
Classification levels (e.g., Public, Internal, Restricted) are applied as access controls and do not influence ranking.
Embedding Construction
Each data asset is converted into a structured semantic representation prior to embedding generation.
Context Construction
Instead of treating metadata as independent attributes, the system constructs a business-context narrative that captures:
Object title and description
Business context and usage
Glossary associations and hierarchy
Tag hierarchy and classification
Custom fields
Governance indicators
Data quality context
This contextual representation is then converted into a vector embedding.
Key Characteristics
Improves semantic understanding of data assets
Enhances alignment between user intent and retrieved results
Enables more accurate and context-aware retrieval
Ranking and Scoring Model
The final ranking of data assets is determined using a combination of relevance and governance signals.
Final Ranking Formula
FinalScore = (0.75 × Relevance Score) + (0.25 × Governance Score)
Where:
SearchRelevanceScore = (VectorSimilarity + KeywordScore) / 2
Governance Score Calculation
GovernanceScore represents the trustworthiness and curation level of a data asset.
Contributing Signals
Critical Data Element (CDE) indicator
Data Quality score
Certification status
Authoritative dataset flag
Weight Distribution
CDE Indicator – 40%
Data Quality Score – 30%
Certification Status – 20%
Authoritative Flag – 10%
Key Characteristics
Prioritizes curated and enterprise-approved datasets
Ensures that highly governed assets rank higher than unmanaged data
Maintains balance between relevance and trust
Metadata Curation Score Gate (Embedding Eligibility)
A metadata curation score is used to determine whether a data asset is eligible for inclusion in the embedding and retrieval process.
Funtionality
Assets are evaluated based on a composite metadata curation score.
Only assets meeting the configured threshold are:
Embedded into the vector index
Considered during analysis and retrieval
Assets below the threshold:
Remain available in the catalog
Are excluded from analysis-driven retrieval
Configuration
Threshold is configurable (range: 0–100)
The default value allows the inclusion of all assets
Designed to support gradual adoption based on governance maturity
Re-Evaluation
Eligibility is periodically re-evaluated
Assets can automatically enter or exit the embedding pool based on updated metadata quality
Steward Feedback
When assets fall below the threshold, stewards are provided with:
Current score
Required threshold
Key metadata gaps
Recommended improvements
Custom Field Trust Signal Integration
Custom fields are interpreted as governance signals to enhance trust scoring without requiring explicit configuration.
Signal Identification
The system analyzes custom field names using predefined keyword patterns.
Matching fields are mapped to governance signal categories such as:
CDE indicators
Data quality rules
Authoritative source indicators
Policy and compliance references
Signal Processing
If a native governance signal is available, it is used directly.
If not, matched custom fields act as proxy signals.
Confidence Adjustment
Custom field–derived signals are applied with a confidence factor to ensure balanced scoring.
Validation
Signals are applied only when associated with valid object types
Prevents incorrect or irrelevant mappings
Key Characteristics
Enables the utilization of governance information stored in custom fields
Improves the accuracy of trust scoring across diverse implementations
Reduces dependency on the strict standardization of metadata models
Prompt Guidance for Next-Best Questions
askEdgi provides prompt guidance to assist users in continuing their analysis by suggesting relevant follow-up questions. This capability helps users refine or expand their queries without requiring manual prompt formulation.
After each user prompt is processed and a response is generated, the system evaluates:
The user’s original question
The intent identified by askEdgi
The results returned (tables, insights, summaries)
Based on this, the system generates a set of relevant follow-up prompts that naturally extend the current analysis.
How Prompt Guidance Works
Prompt suggestions are generated only after a response is produced
Suggestions are derived from:
Current query intent
Result patterns (e.g., trends, anomalies, groupings)
Available metadata and context
Each suggestion is:
Directly related to the current analysis
Focused on helping users go deeper or refine results
Written in simple, natural language
Example flow:
User asks: “Show delayed orders.”
askEdgi responds with results.
Prompt Guidance suggests:
“Which vendors have the highest delays?”
“Are delays increasing over time?”
“Which regions are most affected?”
Execution Boundaries
Prompt Guidance does not participate in execution or data processing. Its role is limited to suggestion generation.
It does not trigger:
Data retrieval
Query execution
Recipe execution
It does not:
Modify the current results
Re-run analysis
Interfere with the main response
Suggestions are only recommendations, and execution happens only when the user selects a suggestion or enters a new prompt.
User Interaction Model
Suggestions are displayed immediately after each response
Users can:
Click on a suggested prompt to continue analysis
Ignore suggestions and enter their own query
When a suggestion is selected:
It is treated as a new prompt
askEdgi processes it through the standard flow (intent → execution → response)
Copyright © 2026, OvalEdge LLC, Peachtree Corners, GA USA
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