The ‘brownie recipe problem’: why LLMs must have fine-grained context to deliver real-time results
Summary
Instacart's CTO Anirban Kundu highlights the challenges of integrating LLMs in real-time ordering systems, emphasizing the need for context and personalization. The company employs a modular approach with microagents to enhance efficiency and manage complex integrations effectively.
Key Insights
What is the 'brownie recipe problem' in the context of LLMs?
The 'brownie recipe problem' refers to the challenge where large language models (LLMs) like ChatGPT fail to provide accurate real-time evaluations without fine-grained context, as demonstrated in a University of Illinois study where ChatGPT gave overly positive scores (8.5-9.5/10) to all brownie recipes, even those with unappealing ingredients like mealworm powder and fish oil, due to hedonic asymmetry.
Why do LLMs require fine-grained context for real-time applications like Instacart's ordering systems?
LLMs need fine-grained context and personalization to deliver accurate real-time results in complex systems, as generic responses lack the specificity for tasks like recipe evaluation or order personalization; Instacart addresses this with a modular microagents approach to manage integrations efficiently.