omni-ai-optimizer

name

omni-ai-optimizer

description

Optimize your Omni Analytics model for Blobby, Omni's AI assistant — configure ai_context, ai_fields, sample_queries, and create AI-specific topic extensions. Use this skill whenever someone wants to improve AI accuracy in Omni, make Blobby smarter, configure AI context, add example questions, tune AI responses, set up sample queries, curate fields for AI, create AI-optimized topics, troubleshoot why Blobby gives wrong answers, or any variant of "make the AI better", "Blobby isn't answering correctly", "add context for AI", "optimize for AI", or "teach the AI about our data".

Omni AI Optimizer

Optimize your Omni semantic model so Blobby (Omni's AI assistant) returns accurate, contextual answers.

Tip: Use omni-model-explorer to inspect current AI context before making changes.

Prerequisites

export OMNI_BASE_URL="https://yourorg.omniapp.co"
export OMNI_API_KEY="your-api-key"

Requires Modeler or Connection Admin permissions.

API Discovery

When unsure whether an endpoint or parameter exists, fetch the OpenAPI spec:

curl -L "$OMNI_BASE_URL/openapi.json" \
  -H "Authorization: Bearer $OMNI_API_KEY"

Use this to verify endpoints, available parameters, and request/response schemas before making calls.

How Blobby Works

Blobby generates queries by examining:

1. Topic structure — which views and fields are joined
2. Field labels and descriptions — how fields are named
3. synonyms — alternative names for fields
4. ai_context — explicit instructions you write
5. ai_fields — which fields are visible to AI
6. sample_queries — example questions with correct queries
7. Hidden fields — hidden: true fields are excluded

Impact order: ai_context > ai_fields > sample_queries > synonyms > field descriptions.

Writing ai_context

Add via the YAML API:

curl -L -X POST "$OMNI_BASE_URL/api/v1/models/{modelId}/yaml" \
  -H "Authorization: Bearer $OMNI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "fileName": "order_transactions.topic",
    "yaml": "base_view: order_items\nlabel: Order Transactions\nai_context: |\n  Map \"revenue\" → total_revenue. Map \"orders\" → count.\n  Map \"customers\" → unique_users.\n  Status values: complete, pending, cancelled, returned.\n  Only complete orders for revenue unless specified otherwise.",
    "mode": "extension",
    "commitMessage": "Add AI context to order transactions topic"
  }'

What Makes Good ai_context

Terminology mapping — map business language to field names:

ai_context: |
  "revenue" or "sales" → order_items.total_revenue
  "orders" → order_items.count
  "customers" → users.count or order_items.unique_users
  "AOV" → order_items.average_order_value

Data nuances — explain what isn't obvious from field names:

ai_context: |
  Each row is a line item, not an order. One order has multiple line items.
  total_revenue already excludes returns and cancellations.
  Dates are in UTC.

Behavioral guidance — direct common patterns:

ai_context: |
  For trends, default to weekly granularity, sort ascending.
  For "top N", sort descending and limit to 10.

Persona prompting — set the analytical perspective:

ai_context: |
  You are the head of finance analyzing customer payment data.
  Default to monetary values in USD with 2 decimal places.

Curating Fields with ai_fields

Reduce noise for large models:

ai_fields:
  - all_views.*
  - -tag:internal
  - -distribution_centers.*

# Or explicit list
ai_fields:
  - order_items.created_at
  - order_items.total_revenue
  - order_items.count
  - users.name
  - users.state
  - products.category

Same operators as topic fields: wildcard (*), negation (-), tags (tag:).

Adding sample_queries

Teach Blobby by example. Build the correct query in a workbook, retrieve its structure, then add to the topic YAML:

sample_queries:
  revenue_by_month:
    prompt: "What month has the highest revenue?"
    ai_context: "Use total_revenue grouped by month, sorted descending, limit 1"
    query:
      base_view: order_items
      fields:
        - order_items.created_at[month]
        - order_items.total_revenue
      topic: order_transactions
      limit: 1
      sorts:
        - field: order_items.total_revenue
          desc: true

Note: When exporting queries from Omni's workbook, you'll get JSON with table, join_paths_from_topic_name, and sorts using column_name/sort_descending. Map these to YAML as follows:

• table → base_view
• join_paths_from_topic_name → topic
• column_name → field, sort_descending → desc
• Workbook JSON includes filters, pivots, limit, column_limit which you can include in YAML (though filter syntax requires consulting the Model YAML API docs directly)

Focus on questions users actually ask — check Analytics > AI usage in Omni.

AI-Specific Topic Extensions

Create a curated topic variant for Blobby using extends:

# ai_order_transactions.topic
extends: [order_items]
label: AI - Order Transactions

fields:
  - order_items.created_at
  - order_items.status
  - order_items.total_revenue
  - order_items.count
  - users.name
  - users.state
  - products.category

ai_context: |
  Curated view of order data for AI analysis.
  [detailed context here]

sample_queries:
  top_categories_last_month:
    prompt: "Top selling categories last month?"
    query:
      base_view: order_items
      fields:
        - products.category
        - order_items.total_revenue
      topic: ai_order_transactions
      limit: 10
      sorts:
        - field: order_items.total_revenue
          desc: true

Improving Field Descriptions

dimensions:
  status:
    label: Order Status
    description: >
      Current fulfillment status. Values: complete, pending, cancelled, returned.
      Use 'complete' for revenue calculations.

Good descriptions help both Blobby and human analysts.

Adding synonyms

Map alternative names, abbreviations, and domain-specific terminology so Blobby matches user queries to the correct field. Works on both dimensions and measures.

dimensions:
  customer_name:
    synonyms: [client, account, buyer, purchaser]
  order_date:
    synonyms: [purchase date, transaction date, order timestamp]

measures:
  total_revenue:
    synonyms: [sales, income, earnings, gross revenue, top line]
  average_order_value:
    synonyms: [AOV, avg order, basket size]

Synonyms vs ai_context: Use synonyms for field-level name mapping. Use ai_context for topic-level behavioral guidance, data nuances, and multi-field relationships.

Optimization Checklist

1. Inspect current state with omni-model-explorer
2. Check AI usage dashboard for real user questions
3. Write ai_context mapping business terms to fields
4. Add synonyms to key dimensions and measures
5. Curate ai_fields to remove noise
6. Add sample_queries for top 3-5 questions
7. Improve field description values
8. Consider extends for AI-specific topic variants
9. Test iteratively — ask Blobby and refine

Docs Reference

• Optimizing Models for AI · Synonyms · Topic Parameters · Model YAML API · Omni AI Overview

Related Skills

• omni-model-explorer — inspect existing AI context
• omni-model-builder — modify views and topics
• omni-query — test queries to verify Blobby's output