Building an AI Scoring Pipeline for 10,000+ Listings a Day

Short summary

Building a cost-effective AI scoring pipeline for 10,000+ job listings daily requires strategic choices: use traditional rules and keyword matching as a pre-filter to avoid unnecessary LLM calls, batch smaller requests via OpenAI's Batch API for 50% cost savings, and prefer gpt-4o-mini over gpt-4o for classification tasks. Implement token bucket rate limiting and caching. The core insight: combine LLMs with simpler filters and batch processing for production scale.

• •Two-stage pipeline: cheap pre-filter (rules) then LLM scoring reduces API costs
• •Batch processing via OpenAI Batch API saves 50% on token costs
• •Use gpt-4o-mini for structured scoring tasks instead of full gpt-4o

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