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‘Questions and answers are the bread and butter of stackoverflow.com‘. When the platform started in 2008, it was using Microsoft SQL’s full-text search capabilities. With the constant rise of traffic on the play the company had to change its approach. Today, to make its search equally intuitive and approachable, the company has decided to switch to semantic search.
In the announcement blog, the company stated, “Semantic search and LLMs go together like cookies and milk.” As organizations are rapidly deploying Retrieval Augmented Generation (RAG) to create intuitive search experiences, Stack Overflow has opted for it, too. With the integration of RAG, the platform claims its tens of millions of questions and answers—curated and moderated by the amazing community—are about as qualified as it gets.
The community based platform has further mentioned that closed-source models are unnecessary or even overkill. For the time being, a pre-tuned open source model that produces 768 dimensions is being used by the company.
Another challenge is to figure out a way(s) to break the text up into tokens for embedding. As embedding models have a fixed context length, the company has added the right text in the embedding: not too little nor too much. With the latest semantic mapping of data, rigidity and strictness of search can be avoided. Users can write their questions in natural language and get relevant results.
The company stated, its ‘ethos is simple: accuracy and attribution’. While large language models (LLMs) out there are generating results from sources unknown, The company has taken charge to clearly attribute questions and answers used in their RAG LLM summaries.
With its latest offering the company is convinced that technologists looking for answers will use their semantic search instead of a search engine or conversational AI. The news comes in the light of a recent event where they announced the integration of generative AI on their platform with Overflow AI — something that the company has been hinting about since April.
