Using Web Search to Mitigate Catalogue Gaps in Custom GPTs

Introduction

When building a shopping-focused Custom GPT, a structured product catalogue is usually the primary source of truth. It allows the model to surface accurate, branded, and trusted products in a carousel or recommendation format. However, catalogues are rarely perfect: they may be incomplete, out-of-date, or too narrow to satisfy user queries. This is where integrating web search becomes a powerful fallback.

This article explores why catalogue gaps occur, how web search can mitigate them, and provides a practical JSON-based strategy to implement a hybrid approach.

Why Catalogues Fail to Surface Products

A Custom GPT may fail to show catalogue results due to:

  • Data issues: Missing fields (price, image, stock), stale data, broken purchase links, or invalid schema.

  • Intent issues: User queries may be too broad ("show me all laptops"), too narrow ("under $50" but catalogue starts at $80), or ambiguous ("good shoes for travel").

  • System issues: Disabled knowledge files, API errors, large file size limits, or rendering constraints that prevent carousel output.

Role of Web Search in Filling Catalogue Gaps

Web search acts as a safety net for product discovery. It can:

  1. Enrich missing data: Fetch up-to-date prices, stock information, and images.

  2. Expand coverage: Bring in products beyond the catalogue, including from marketplaces or niche retailers.

  3. Validate recency: Confirm whether a product is the latest model year or currently in stock.

  4. Interpret vague queries: Provide context from buying guides and reviews to narrow down ambiguous requests.

  5. Ensure resilience: If catalogue access fails (API down, misconfigured), web search ensures users still see relevant products.

Best Practices for Hybrid Catalogue + Search

  1. Catalogue first: Always prioritise in-house or trusted catalogue results.

  2. Trigger web search selectively: Use search when catalogue hits are below a threshold, when freshness is critical, or when explicit recency cues are present in the query.

  3. Merge results transparently: Label web-sourced items clearly (e.g., "From the web").

  4. Deduplicate: Avoid showing duplicates by comparing product IDs, model numbers, and names.

  5. Fallback gracefully: If no results exist, widen filters slightly (±15% price range, near-synonym features) or ask clarifying questions.

JSON Strategy for Implementation

Here’s an outline of a JSON instruction file that encodes this hybrid approach:

{

"triggers": {

"shopping_intent_patterns": ["(?i)best|buy|recommend|deal|under \\d+"],

"force_search_if": {

"catalogue_hits_lt": 3,

"user_mentions_recency": ["latest", "new", "2025"]

}

},

"data_sources": {

"catalogue": { "type": "internal" },

"web": { "type": "search", "provider": "web.run" }

},

"decision_logic": {

"steps": [

"Search catalogue first",

"If insufficient results, trigger web search",

"Normalize results into common product schema",

"Merge and label sources",

"Fallback to nearest alternatives if nothing found"

]

},

"presentation": {

"style": "carousel_if_possible_else_list",

"labels": {

"catalogue_item": "From our catalogue",

"web_item": "From the web"

}

}

}

This template can be extended with ranking weights, deduplication rules, and validation logic for stock and price.

Benefits of Hybrid Integration

  • Improved user trust: Gaps are explained transparently instead of returning nothing.

  • Higher satisfaction: Users are less likely to abandon the conversation if catalogue-only results are sparse.

  • Dynamic coverage: The assistant stays relevant even when catalogue data lags behind reality.

  • Future-ready: The framework allows easy integration of merchant APIs, affiliate feeds, or direct checkout flows.

Conclusion

For Custom GPTs, a well-structured catalogue is foundational, but it should not be the only source. By integrating web search as a fallback mechanism, developers can deliver a more robust, resilient, and satisfying shopping experience. The key is to set clear rules for when to trust catalogue data, when to enrich with search, and how to present the results transparently.

This hybrid model ensures that even when catalogues fall short, the user journey never breaks.

Custom GPT, Product CatalogueFrancesca Tabor