Recipe User Guide

What is a Recipe?

A Recipe in askEdgi is a saved, reusable analytical workflow. It captures not just the code (SQL/Python), but the entire context of an analysis, including the datasets used ("ingredients"), the specific queries, and any AI enrichments applied. Recipes allow you to automate repetitive tasks and standardise reporting across teams.

It captures datasets, queries, AI enrichments, and visualisations in a structured, reusable format. Recipes eliminate the need to recreate multi-step analyses manually.

When you execute a recipe, askEdgi applies the same logic to the current data in your workspace. Results appear in a new chat with all outputs preserved.

When to Use Recipes

Use recipes when:

• You perform the same analysis regularly (weekly reports, monthly summaries)

• Your team needs consistent KPI calculations across departments

• You want to share proven analytical workflows with colleagues

• You need to standardise data quality checks or metadata audits

Recipes reduce errors, save time, and ensure everyone uses the same analytical logic.

Recipe Components

Every recipe contains the following elements:

• Name: A descriptive title for the recipe

• Description: An explanation of the recipe's purpose and outputs

• Ingredients: Datasets or metadata objects required for execution (optional)

• Steps: Queries, enrichments, and transformations captured from the original analysis

• Analysis Type: Indicates whether the recipe operates on data or metadata

When you save a chat as a recipe, askEdgi captures these components automatically. You can edit them before saving.

Creating a Recipe

There are two ways to create a recipe: converting an existing conversation or building one from scratch.

Method 1: Convert to a Chat

This is the easiest way to save a successful analysis.

1. Open the My Chats panel on the left side of the interface.

1. Select the conversation chat you want to save.

2. Click the Save as Recipe button.

1. The system automatically extracts the logic and queries into steps.

2. Enter a Name, Description, and Ingredients for the recipe.

1. Click Save as Recipe.

Method 2: Creating from Scratch

1. Navigate to the Recipes tab in the main menu.

2. Click Create Recipe.

1. Define Ingredients: Select the data objects (tables or files) required for this recipe. Ingredients are optional but recommended for workflows that depend on specific data structures.

2. Add Steps: For each step of the analysis:

3. Select the Type of Analysis (SQL, Python, or AI Function).

4. Enter the specific query, code, or prompt.

5. Click Save as Recipe

Understanding Ingredients

Ingredients define which datasets a recipe requires. However, ingredients are optional.

When to include ingredients:

• The recipe is designed for specific tables (example: service request analysis requires the service_requests table)

• You want to enforce that certain datasets must be present

When to omit ingredients:

• The recipe applies generic logic that works with any dataset

• You want maximum flexibility for reuse across different data environments

From My Recipes

1. Open the Recipes section

2. Navigate to My Recipes tab

3. Locate the recipe you want to run

4. Click the Execute button

1. The recipe runs in a new dedicated chat

From the Chat Interface

1. Click the Execute Recipe icon next to the Send button

2. Paste the recipe token (if using a Marketplace recipe)

3. Click Execute

4. Results appear in the chat and are saved as a new chat

From the Marketplace (Public Edition)

• Access the Recipe from Marketplace

• Navigate to the Marketplace section where all published recipes are displayed.

• Click on a recipe card to open its Recipe Details screen.

• On this screen, users can view the recipe’s description, verification status, associated tags, and pricing details.

• Once reviewed, click on the Get Recipe button to proceed. A pop-up window will appear displaying the Recipe Token.

• Copy the Recipe Token

• Copy the Recipe Token displayed in the pop-up. This token uniquely identifies the selected recipe for execution within askEdgi.

• Open the Execute Recipe Panel

• In the chat interface, click on the Execute Recipe icon located beside the Send button.

• Or click on the “+Add Recipe from Markeplace” button in My Recipe tab.

• The Add Recipe from Marketplace pop-up will appear.

• Paste the Recipe Token

• Paste the copied Recipe Token into the input field within the My Recipe pop-up.

• Execute the Recipe

• Click on the Execute button to run the recipe.

• askEdgi will process the recipe and display the results directly in the chat interface.

• View Executed Recipe in Chats

• Once executed, the recipe and its output are automatically saved and can be viewed in the Chats section.

• This allows users to revisit or reuse the executed recipe as part of their ongoing workspace history.

AI Enrichment in Recipes

Recipes can include AI functions to transform data. Supported functions include:

• Sentiment Analysis: Classifies text as Positive, Neutral, or Negative.

• Intent Analysis: Categorises the purpose behind text (e.g., "Support Request," "Complaint").

• Text Classification: Sorts data into custom categories like "Fraud/Not Fraud".

• Calculated Columns: Creates new fields based on mathematical or logical formulas.

Sharing Recipes Internally (SaaS Editions)

Enterprise users can share recipes with colleagues through the Organisation tab.

Listing a Recipe in an Organisation

1. Navigate to Recipes

2. Click the Organisation tab

3. Click "List to Organisation"

4. Select a recipe from My Recipes

5. Click Submit

The recipe becomes visible to other users in your organisation. Ownership remains with you, but authorised users can execute it according to role-based access controls.

Using Organization Recipes

1. Navigate to Recipes

2. Click the Organization tab

3. Browse recipes listed by your colleagues

4. Click Execute on any recipe you want to run

The recipe runs in your workspace using your available datasets and connectors. Results are stored in your chat history.

Publishing to the Marketplace (Public Edition Only)

Public edition users can publish recipes to the Marketplace for community use and monetisation.

Step 1: Prepare the Recipe

Ensure your recipe is complete, well-documented, and tested. Review the name, description, and steps for clarity.

Step 2: List to the Marketplace

1. Navigate to My Recipes

2. Select the recipe

3. Click "List to Marketplace"

4. Click the "Marketplace Edit" link

Step 3: Complete the Marketplace Edit Form

Basic Information:

• Recipe Name: Clear, descriptive title

• Recipe Slug: Unique identifier for the recipe URL (auto-generated, editable)

• Description: Detailed explanation of purpose, outputs, and use cases

• Recipe Guide: Step-by-step instructions for execution

Seller Information:

• Seller Support: Contact email or support link

• LinkedIn Profile: Optional professional profile link

Pricing:

• Pricing Type: Free or Paid

• Per Execution Price (USD): If Paid, specify price per execution

Categories:

• Industry: Select relevant sector (Retail, Finance, Healthcare, etc.)

• Application: Identify business application (Product Returns, Customer Sentiment, etc.)

• Others: Additional applicable categories

Tags:

• Enter keywords separated by commas to improve discoverability

Example: returns, sentiment, quality

Step 4: Request to Publish

• Click "Request to Publish". The recipe is submitted for review by the OvalEdge admin team. Admins verify quality, completeness, and compliance before approval.

Step 5: Approval and Publication

• Once approved, the recipe becomes publicly available in the Marketplace. Other users can subscribe, execute, and provide feedback. If you set a Paid pricing model, earnings accrue to your account based on execution counts.

Managing Published Recipes

Viewing Earnings

1. Click the profile icon

2. Select "Manage Subscription"

1. Navigate to the Earnings tab

The Earnings tab displays revenue generated from recipe executions. Data is structured by recipe type, quantity, price, and total earnings. A historical chart shows performance across months.

Revoking a Recipe

If you need to withdraw a published recipe:

1. Access the Marketplace dashboard (creator view)

2. Locate the recipe

3. Select the "Revoke" action

The recipe is removed from public discovery. New consumers cannot subscribe or execute it. Existing executions already purchased may continue to function.

Recipe Execution by Edition

Recipe behaviour varies by deployment edition. Understand which recipes you can create and execute based on your environment.

Public Edition

Capabilities:

• Create recipes in the workspace

• Execute recipes on uploaded or public datasets

• Full access to the Public Marketplace

• Publish and monetise recipes

Constraints:

• No enterprise connector access

• Limited to public data and uploaded files

SaaS – Data Analytics

Capabilities:

• Full internal recipe creation

• Execute all recipe types (data and metadata)

• Share recipes via the Organisation tab

• Optional access to Public Marketplace recipes (policy-controlled)

Scope:

All connectors, uploaded files, and catalog tables are supported

SaaS – Metadata Analytics

Capabilities:

• Create metadata-focused recipes

• Execute recipes operating through the OvalEdge (-1 connector)

• Share recipes internally

• Optional access to Public Marketplace recipes (policy-controlled)

Constraints:

• Only metadata recipes are supported

• No data-level enrichments

On-Prem Edition

Capabilities:

• Create internal metadata analytics recipes

• Execute recipes using the OvalEdge (-1 connector)

• Share recipes internally (optional, policy-controlled)

Constraints:

• No external AI enrichments

• No public recipe sharing

• No Marketplace access

• Metadata recipes only

Special Case: Recipes Without Ingredients

Some recipes operate without requiring specific datasets. These are typically metadata-focused workflows.

Example: Data quality checks across all tables in the catalog

In this case:

• No ingredients are defined in the recipe

• Execution runs against all tables in the workspace or catalog

• Results depend on which objects are present during execution

This pattern is common for governance checks, profiling summaries, and catalog audits.

Recipe execution: Common failure reasons

• Required datasets are missing from the workspace

• Insufficient permissions to access catalog objects

• Column names or structures differ from the recipe's expectations

• Workspace compute limits exceeded

Best Practices

Naming Conventions

Use clear, descriptive names that explain what the recipe does. Avoid generic names like "Analysis 1" or "Report."

Good examples:

- "Monthly Sales Summary by Region"

- "Customer Sentiment Analysis with Risk Classification"

- "Data Quality Check for Orders Table"

Documentation:

Write descriptions that help others understand the recipe's purpose, required inputs, and expected outputs. Include guidance on when to use the recipe and what insights it provides.

Testing Before Sharing

Execute the recipe on test data before sharing it with your organization or publishing to the Marketplace. Verify that all steps produce correct results and that visualizations are accurate.

Version Control

If you modify a recipe significantly, save it as a new recipe with a version indicator in the name (example: "Customer Analysis v2"). This preserves the original workflow while allowing experimentation.

Metadata Recipes

When creating metadata recipes, document which catalog objects or metadata domains the recipe targets. This helps users understand whether the recipe applies to their governance needs.

Troubleshooting Recipe Execution

The recipe fails immediately

Cause: Required datasets are not in the workspace

Solution: Add the necessary catalog objects or files to your workspace before executing the recipe

The recipe produces unexpected results

Cause: Column names or structures differ from the original analysis

Solution: Review the recipe steps and verify that the datasets in your workspace match the expected schema

Recipe times out or exceeds limits

Cause: Large datasets or complex joins exceed workspace capacity

Solution: Upgrade the workspace container or simplify the recipe by breaking it into smaller steps

The recipe cannot be executed in my edition

Cause: The recipe requires connectors or features not available in your edition.

Solution: Verify that the recipe is compatible with your deployment model (Public, SaaS Data Analytics, SaaS Metadata Analytics, or On-Prem)

Example of Recipe

Creating a Recipe with a Real Use Case

Example Scenario

Business Question:

“How are customers feeling about our support tickets, and which issues need immediate attention?”

This example walks through creating a Customer Support Sentiment Analysis recipe using a real dataset and AI enrichment.

Step 1: Start with an Analysis (Chat-Based)

Assume you already have a table in your workspace:

Table: support_tickets Columns:

• ticket_id

• created_date

• customer_comment

• priority

• category

You run the following steps in a chat:

Step 2: Basic Data Selection

SELECT

ticket_id,

created_date,

customer_comment,

priority,

category

FROM support_tickets

WHERE created_date >= current_date - INTERVAL '30 days';

This filters tickets from the last 30 days.

Step 3: AI Enrichment – Sentiment Analysis

You apply Sentiment Analysis on the customer_comment column.

AI Function: Sentiment Analysis Input Column: customer_comment Output Column: sentiment Values: Positive / Neutral / Negative

Step 4: AI Enrichment – Intent Classification

You add an Intent Analysis step to understand why customers raised tickets.

AI Function: Intent Analysis Input Column: customer_comment Output Column: intent Example Values:

• Complaint

• Support Request

• Feature Request

• Billing Issue

Step 5: Derived Insight (Optional)

You create a calculated column to flag high-risk tickets.

CASE

WHEN sentiment = 'Negative' AND priority = 'High' THEN 'High Risk'

ELSE 'Normal'

END AS risk_flag

Step 6: Save the Chat as a Recipe

Click Save as Recipe and fill in:

Recipe Name: Customer Support Sentiment & Risk Analysis

Description: Analyzes customer support tickets from the last 30 days, classifies sentiment and intent using AI, and highlights high-risk tickets requiring immediate attention.

Step 7: Define Ingredients

Ingredients (Included):

• support_tickets (table)

Why include ingredients? This recipe depends on a specific table structure and should only run when that dataset is available.

Step 8: Review Recipe Components

When saved, the recipe contains:

Component	Captured Value
Ingredients	support_tickets
Steps	SQL query + Sentiment Analysis + Intent Analysis + Risk flag
Analysis Type	Data Analytics
Outputs	Table + charts (sentiment distribution, risk count)

Step 9: Execute the Recipe Later

Next month, a user can simply:

1. Add support_tickets to their workspace

2. Open Recipes → My Recipes

3. Click Execute on Customer Support Sentiment & Risk Analysis

askEdgi:

• Re-runs the same logic

• Applies AI enrichments again

• Generates a new chat with updated insights

No manual rework required.

Frequently Asked Questions

Can I delete a recipe?

Yes. Recipes can be deleted from My Recipes. Deletion does not affect chats or analyses that used the recipe.

Do recipes update automatically when I change the original chat?

No. Recipes are static snapshots. Changes to the original chat do not affect saved recipes.

Can I share recipes with external users?

Only in Public Edition via the Marketplace. SaaS and On-Prem editions support internal sharing only.

What happens if I cancel my subscription after publishing a recipe?

In Public Edition, your published recipes remain active in the Marketplace even if you cancel your subscription. Earnings continue to accrue.

Can I import a recipe from the Marketplace into my enterprise environment?

Yes, if your organization permits Public Marketplace access. Marketplace recipes run within your edition's constraints and connector availability.

Summary

Recipes transform ad-hoc analyses into reusable workflows. They standardize logic, reduce manual effort, and ensure consistency across teams. Create recipes from successful chats, share them internally, or publish them to the Marketplace to contribute to the community.

For additional guidance on recipe creation, execution, and troubleshooting, consult the Deep Dive Articles and FAQs available in the support portal.