Retrieval-Augmented Generation (RAG)

Introduction

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

Note: Sample statistics and top value summaries improve interpretation but remain secondary to governed metadata and definitions.

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.

Important: Discovery Mode does not use RAG. Only Analysis Mode uses RAG.

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.

Important: 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

Note: Contextual data statistics and 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.