AI Coding Plans Are Getting Expensive, and a Meituan RAG Interview Shows What You Need to Know to Land the Job
The interview questions and architecture decisions map directly to what production RAG systems require: hybrid search to fix pure vector retrieval's blind spots, streaming with user interrupt, and chunking strategies that balance context size against retrieval precision. The pricing shift means individual developers either pay up or target employers who subsidize access.
The era of cheap AI coding tokens is over. Multiple Coding Plans have raised prices, and one developer reports spending over $350 a month on Claude and Codex alone. The advice for developers who want top-tier models without the bill: get hired at a major Chinese internet company where internal access is unlimited.
The bulk of the piece is a Meituan interview deep-dive for a large-model application developer role. It walks through eight technical questions and their answers, centered on a project called PaiCongMing, a private-knowledge-base RAG system. Topics include a two-stage hybrid retrieval pipeline combining KNN vector recall with BM25 re-ranking, a three-layer semantic text chunking strategy that falls back to HanLP word segmentation, and Redis-based conversation memory with a 20-message FIFO cap and 7-day TTL.
Streaming responses use WebFlux and WebSocket for a typewriter effect, with a stop-generation command protected by a server-issued internal token to prevent abuse. The document parsing pipeline handles PDFs by stripping repeated headers and footers via frequency analysis. The interview also covers why WebSocket beats SSE for chat, when a heap makes sense over a queue for memory eviction, and how to structure a resume entry for an AI application project.
The hybrid retrieval weighting (0.2 KNN, 1.0 BM25) reveals a practical truth: semantic similarity alone is unreliable for factual Q&A, and keyword matching still carries the heavier vote in production.
Batching Embedding calls at 100 items with retry logic and a text-search fallback is the kind of unglamorous resilience work that separates a demo from a system users won't curse at.
The three-layer chunking fallback (paragraphs → sentences → HanLP word segmentation) is a quiet admission that no single heuristic works for Chinese text; you need a cascade that degrades gracefully.
Using frequency analysis to strip PDF headers and footers is a simple statistical trick that avoids the complexity of layout-aware parsing, and it works well enough for most corporate documents.
Choosing FIFO over a priority heap for conversation memory eviction is the right call for chat, but the interviewer's push on heaps suggests they want candidates who know when not to use a data structure, not just how to use it.
The internal command token pattern for stopping generation is a small security detail that most tutorial RAG projects skip, but in a multi-tenant system it prevents a trivial denial-of-service vector.
Advising developers to join big tech for free model access is a candid acknowledgment that the economics of personal AI tooling are breaking down as vendors end loss-leader pricing.
What the hell, a hundred question marks?????? How does Function Calling parse user intent? What does Function Calling have to do with parsing user intent? Isn't parsing user intent the AI's job? Isn't Function Calling just a way to invoke tools?
His thing isn't intent recognition at all. From what he's saying, it's just matching based on frontend parameters and then calling a method.