Optimize for Speed and Relevance in Technical Search

The architecture of the search system embraces a straightforward yet effective retrieval step that leverages intelligent query rewriting to enhance relevance, particularly for technical searches. It employs a compact, high-speed language model that reformulates user queries and combines this with on-the-fly pre-computation to determine whether a single search is sufficient or if multiple searches are necessary. With the help of a classifier, the system can execute up to eight parallel searches, optimizing speed and throughput to ensure that the embedding process completes within 100 milliseconds, even when processing substantial amounts of data. The context formed from multiple sources is then sent to both GPT models and custom-developed models, showcasing a continuous evolution in their technical capabilities due to initial limitations in existing model APIs. The integration of GPT-4 significantly boosted user engagement and popularity, culminating in a notable Hacker News milestone.