The AI Config Tab - Fine-Tuning Autopilot Recommendations

Overview

The AI Config tab activates when you enable Autopilot on the Products tab. It lets you configure AI-driven recommendations instead of manually selecting products for each offer.

Intent Settings

The Intent setting defines what type of recommendations to make:

• Related Products: Similar items in the same category (colors, sizes, variations)
• Complementary Products: Items that pair well together
• Upgrade: Premium or higher-tier versions of products
• Cross-Sell: Products from different categories the customer might need
• Bundled Products: Items that work together as complete solutions

Max Products

Controls how many recommendations to show:

• 2-3 products: High-conversion, focused upsells; ideal for Checkout offers
• 4-5 products: Balanced approach; works for most use cases
• 6-8 products: Maximum variety; better for Thank You Page offers

Test different numbers to find what works for your store.

Inclusion Filters

Guardrails that tell AI which products can be recommended.

Filter Types:

• Collections: Limit to specific product collections
• Tags: Use product tags as boundaries (e.g., in-stock, seasonal-current)
• Product Types: Restrict to specific product types
• Metafields: Use custom fields for advanced control (e.g., margin_category = high-margin)

Filter Logic:

• Same category: OR logic (Collection A OR B)
• Different categories: AND logic (Collection A AND Tag "current")

Start with one filter and add more if needed. Too many filters can leave no products to recommend.

Real-World Examples

Fashion Store: Complementary Products intent, 4 Max Products, filtered by Apparel collection + seasonal-current tag + in-stock tag

Electronics: Upgrade intent, 3 Max Products, filtered by Premium Accessories product type + high-margin metafield

Grocery: Bundled Products intent, 5 Max Products, filtered by (Organic OR Pantry Staples) AND (meal-kits OR recipe-ready)

Monitoring Performance

Track these metrics:

• Conversion Rate: Target 2-5% depending on offer type
• Average Order Value Lift: Compare Autopilot vs manual selections
• Relevance: Review recommendations regularly for quality

Iteration frequency: Weekly reviews, monthly comparisons, quarterly adjustments for seasonal changes.

Common Mistakes

• Over-filtering: Limits available products; expand your filters if conversion drops
• Wrong Intent: Test different intents against your goals; let data guide decisions
• Max Products too high: Causes decision paralysis; start with 4 and test lower
• Ignoring seasonality: Update filters quarterly as inventory changes

Pro Tips

• A/B test different intents to find what resonates
• Use metafields for sophisticated control over recommendation quality
• Combine Autopilot with Smart Rules to segment recommendations by customer type
• Start with one offer, test, and expand once you see results

The AI Config tab automates product recommendations. Start simple, monitor performance, and iterate based on data.

Updated on: 11/02/2026