Editorial illustration for LLMs need fine-grained catalog context: large model routes data to SLMs
LLM Routing: How AI Connects Catalog Details Precisely
LLMs need fine-grained catalog context: large model routes data to SLMs
Why does a “brownie recipe problem” matter for today’s language models? While the hype around massive LLMs promises generic understanding, the real test is whether they can stitch together the tiny details that make a recipe—or a product catalog—coherent in real time. The challenge isn’t just recognizing a word like “flour”; it’s grasping how that ingredient interacts with butter, sugar, and eggs, and then delivering a usable answer instantly.
Here’s the thing: without a granular view of how items pair, a model can churn out vague suggestions that miss the point entirely. The partnership between a broad‑scope foundation and narrowly tuned specialists offers a possible fix. By first interpreting user intent and sorting items, then handing off that structured signal to purpose‑built units that know the nuances of food pairings and semantic links, the system aims to close the gap between generic comprehension and actionable detail.
The next step explains exactly how that handoff works.
First, data is fed into a large foundational model that can understand intent and categorize products. That processed data is then routed to small language models (SLMs) designed for catalog context (the types of food or other items that work together) and semantic understanding. In the case of catalog context, the SLM must be able to process multiple levels of details around the order itself as well as the different products. For instance, what products go together and what are their relevant replacements if the first choice isn't in stock?
The 'brownie recipe problem' highlights a gap that many large language models still face: translating a simple intent into a detailed, market‑specific plan. Instacart’s solution layers a foundational model with purpose‑built small language models that specialize in catalog context and semantic linking. In practice, a user’s request—say, 'I want to make brownies'—first passes through the big model to capture intent and classify relevant items, then routes to the SLMs which can distinguish organic eggs from regular ones and align ingredients with local inventory.
This architecture promises more precise, real‑time assistance in grocery ordering. Yet, the approach hinges on the seamless handoff between models, and it's unclear whether the small models can consistently maintain the nuanced context across diverse product ranges. Moreover, the brief description leaves open how the system handles edge cases, such as out‑of‑stock items or unusual dietary restrictions.
As the prototype matures, its ability to deliver reliable, fine‑grained recommendations will be the true test of its practicality.
Further Reading
- Exploring the Potential of Large Language Models in Fine-Grained Code Review Classification - ArXiv
- Large Language Models Achieve Fine-Grained Opinion Annotation Through Declarative Pipelines - Quantum Zeitgeist
- LLM-Based Fine-Grained ABAC Policy Generation for IT Networks - SCITEPRESS
- LLMs Meet VLMs: Boosting Open Vocabulary Object Detection with Fine-Grained Descriptors - ICLR
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Common Questions Answered
How do Instacart's Large Language Models (LLMs) handle complex product search queries?
Instacart's approach involves using LLMs to understand user intent for challenging 'long-tail searches' where traditional machine learning models fell short. The strategy moves from context-engineering with guardrails to ultimately fine-tuning models that can transform generalist models into domain-specific experts for product catalog understanding.
What challenges do traditional Query Understanding (QU) systems face when processing user searches?
Traditional Query Understanding systems struggle with broad queries like 'healthy food' that span multiple categories and lack specificity. Additionally, these systems often suffer from a lack of labeled data, making it difficult to precisely interpret user intent and deliver accurate search results.
Why are Small Language Models (SLMs) becoming important for catalog attribute extraction?
Small Language Models offer a more efficient and targeted approach to processing product catalog data, allowing for specialized attribute extraction across millions of SKUs. They can handle diverse attribute requirements, such as numeric reasoning for sheet count or extracting evolving flavor information, while being more cost-effective and scalable than large language models.