---
title: "Building Biz Ask Anything: From Prototype to Product"
url: https://daily.dev/posts/building-biz-ask-anything-from-prototype-to-product-9efe0mz1d
source_url: https://engineeringblog.yelp.com/2026/03/building-baa-from-prototype-to-product.html
type: article
source: "Yelp Engineering"
published: 2026-04-10T18:17:48.900Z
updated: 2026-04-10T18:18:16.499Z
tags: ["llm", "deep-learning", "rag"]
reading_time: 27
upvotes: 0
comments: 0
language: en
---

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# Building Biz Ask Anything: From Prototype to Product

**[Yelp Engineering](https://daily.dev/sources/yelp)** · 27 min read · 0 upvotes · 0 comments

## Summary

Yelp's engineering team details how they evolved 'Biz Ask Anything' (Yelp Assistant on business pages) from a two-week prototype to a production system over nine months. Key challenges covered include: building near-real-time data pipelines (NRT indices, Cassandra EAV store) with sub-100ms reads; a multi-stage question analysis pipeline with fine-tuned GPT-4.1-nano models for Trust & Safety classification, inquiry type routing, content source selection, and keyword generation; multi-dimensional answer quality evaluation using LLM-as-judge graders (correctness, completeness, evidence relevance); latency reduction from 10-20s to under 3s p75 via streaming (SSE), async pipelines, and model prioritization; cost reduction to 25% of original via smaller fine-tuned models, intelligent context window trimming, and dynamic prompt composition; and content-driven suggested questions that lifted engagement ~50% and reduced inability-to-answer rate by ~26%.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://engineeringblog.yelp.com/2026/03/building-baa-from-prototype-to-product.html>

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Tags: [#llm](https://daily.dev/tags/llm), [#deep-learning](https://daily.dev/tags/deep-learning), [#rag](https://daily.dev/tags/rag)

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