omni-ai-optimizer

name

omni-ai-optimizer

description

Optimize your Omni Analytics model for Blobby, the Omni Agent — 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 (the Omni Agent) returns accurate, contextual answers.

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

Prerequisites

# Verify the Omni CLI is installed — if not, ask the user to install it
# See: https://github.com/exploreomni/cli#readme
command -v omni >/dev/null || echo "ERROR: Omni CLI is not installed."

# Show available profiles and select the appropriate one
omni config show
# If multiple profiles exist, ask the user which to use, then switch:
omni config use <profile-name>

# Confirm the active profile is authenticated and inspect your permissions:
omni whoami whoami

Auth: a profile authenticates with an API key or OAuth. If whoami (or any call) returns 401, hand off — ask the user to run ! omni config login <profile> (OAuth 2.1 browser flow; it blocks ~2 min on the browser). Don't run config login yourself in a headless/CI session (no browser → timeout); on a local interactive machine you may. See the omni-api-conventions rule for profile setup (omni config init --auth oauth) and discovering request-body shapes with --schema.

Requires Modeler or Connection Admin permissions.

Discovering Commands

omni models --help                    # List all model operations
omni models yaml-create --help        # Show flags for writing YAML

Tip: Use -o json to force structured output for programmatic parsing, or -o human for readable tables. The default is auto (human in a TTY, JSON when piped).

Safe Model Write Defaults

• Branch first — never write AI optimization YAML directly to the shared model unless the user explicitly asks for a production change. Create or use a model branch, then pass the branch id to omni models yaml-create.
• Read before writing — inspect the current topic/view YAML before adding ai_context, ai_fields, sample_queries, descriptions, or synonyms. If the requested optimization already exists, report that it is already configured instead of duplicating it.
• Topic requests stay on topics — when the user asks to improve a topic, prefer topic-level ai_context, ai_fields, or sample_queries. Use field-level synonyms only when the request is clearly about alternate names for one specific field.
• Do not add "supporting" synonyms after a complete topic mapping — for requests like "Blobby confuses revenue with order count" or "map these terms correctly", if topic-level ai_context already maps the business terms to the correct fields and includes the needed negative guardrails, stop and report that no duplicate write is needed. Adding field-level synonyms in that case is redundant and increases prompt/token surface.
• Create branches with --name — omni models create-branch <model-id> --name <branch-name> does not accept a JSON --body.

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
8. ai_chat_topics — which topics are included/excluded from AI chat (model-level)

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

Writing ai_context

Add via the YAML API:

omni models yaml-create <modelId> --body '{
  "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",
  "branchId": "{branchId}",
  "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.

For requests like "map these terms correctly" or "Blobby confuses X with Y", add explicit positive mappings in topic-level ai_context. Field-level synonyms are useful supporting signal, but they are not a substitute for a topic-level mapping when the requested behavior depends on choosing between two measures. If the topic context already has both positive mappings and negative guardrails, do not add synonyms just to reinforce it; report that the requested optimization already exists. If synonyms already exist but the topic context only says what not to use, add the direct topic mapping instead of adding more synonyms.

Good:

ai_context: |
  "revenue" or "sales" -> order_items.total_revenue
  "order count" or "number of orders" -> order_items.count
  Never use order_items.count when the user asks for revenue.

Avoid stopping at:

measures:
  total_revenue:
    synonyms: [revenue, sales]
  count:
    synonyms: [order count, orders]

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.

Keeping Context Concise

Every token in ai_context, description, and label is sent to the AI on every query. Verbose values waste context window and push out other fields.

• Target 1-2 sentences per ai_context entry. Focus on disambiguation and gotchas, not general explanation.
• Keep labels short and human-readable — avoid redundant qualification (e.g., "Order Total Revenue Amount" → "Total Revenue").
• Rewrite long description values to be direct. If a description restates the field name, remove it.

Curating Fields with ai_fields

The AI context window holds ~550 fields before truncation. If a topic approaches this limit, use ai_fields to curate which fields are included.

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:).

Controlling Topic Visibility with ai_chat_topics

ai_chat_topics is a model-level property that controls which topics Blobby can see:

• No ai_chat_topics property (default) — Blobby can query across all topics.
• ai_chat_topics: [] (empty list) — Blobby cannot query any topics. This effectively disables AI chat for the model.
• Explicit list — only the listed topics (or tag matches) are available. Supports all_topics, tag selectors (tag:customer_facing), and negation (-tag:internal, -staging_events).

Check this first — if a topic isn't in ai_chat_topics, no amount of ai_context or ai_fields on it will matter. Use omni-model-builder to modify this property.

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.

Enumerating Values for Categorical Fields

For closed-set enums, use all_values so Blobby knows every valid filter value:

dimensions:
  status:
    all_values: [complete, pending, cancelled, returned]
  payment_method:
    all_values: [credit_card, debit_card, bank_transfer, paypal, gift_card]

For open-ended categoricals where a full list isn't practical, use sample_values to give representative examples:

dimensions:
  product_category:
    sample_values: [Electronics, Clothing, Home & Garden, Sports, Books]
  city:
    sample_values: [New York, Los Angeles, Chicago, Houston, Phoenix]

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.

Pruning caveat: When the model is large and context is tight, synonyms are pruned before descriptions. Reserve synonyms for high-value fields where users commonly use alternative names.

Avoid redundancy: Don't add synonyms that duplicate the field's label or field name — they add no signal and waste tokens.

Avoiding Duplication

ai_context and description serve different audiences. description is human-facing (shown in the field picker and docs). ai_context is an AI-only hint. Don't put the same text in both — ai_context should add guidance the description doesn't cover (disambiguation, gotchas, when to use one field over another).

Consolidate shared context at the view level. If multiple fields in a view share the same ai_context (e.g., "all monetary values are in USD"), move it to the view-level ai_context instead of repeating it on each field. Field-level ai_context should be specific to that field.

Example — before:

dimensions:
  gross_revenue:
    ai_context: "Monetary value in USD. This is revenue before refunds."
    description: "Monetary value in USD. This is revenue before refunds."
  net_revenue:
    ai_context: "Monetary value in USD. This is revenue after refunds."
    description: "Monetary value in USD. This is revenue after refunds."

After:

ai_context: "All monetary values in this view are in USD."

dimensions:
  gross_revenue:
    ai_context: "Revenue before refunds."
    description: "Total revenue before refunds and cancellations are applied."
  net_revenue:
    ai_context: "Revenue after refunds. Use this for profitability analysis."
    description: "Total revenue after refunds and cancellations."

Optimization Checklist

Prioritize high-impact changes. Improve wording without changing semantics.

1. Inspect current state with omni-model-explorer
2. Check model-level ai_chat_topics — ensure the right topics are visible to AI
3. Check AI usage dashboard for real user questions
4. Count fields — curate with ai_fields if approaching 550
5. Write ai_context mapping business terms to fields (keep to 1-2 sentences)
6. Add synonyms to key dimensions and measures (skip if they duplicate the label)
7. Improve field description and label values
8. Add all_values/sample_values for categorical fields
9. Add sample_queries for top 3-5 questions
10. Remove duplication between ai_context and description; consolidate shared context at view level
11. Consider extends for AI-specific topic variants
12. 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