building: https://searchattention.com (launching soon!)
consulting: https://betalyra.pt
The search api announcement:
www.perplexity.ai/hub/blog/int...
The insights article
research.perplexity.ai/articles/arc...
hashtag#aio hashtag#aiso hashtag#geo hashtag#ai hashtag#llm hashtag#aeo
The search api announcement:
www.perplexity.ai/hub/blog/int...
The insights article
research.perplexity.ai/articles/arc...
hashtag#aio hashtag#aiso hashtag#geo hashtag#ai hashtag#llm hashtag#aeo
- they start with semantic embeddings and lexical retrieval (probably BM25 and similar) for a first very fast retrieval
- then they apply heuristics based filtering and base filters for things like stale content
- then using cross-encoders aka rerankers to rank the final result sets
- they start with semantic embeddings and lexical retrieval (probably BM25 and similar) for a first very fast retrieval
- then they apply heuristics based filtering and base filters for things like stale content
- then using cross-encoders aka rerankers to rank the final result sets
They also published an article giving some super interesting insights into how their index is constructed.
They also published an article giving some super interesting insights into how their index is constructed.
Here is an example of how that looks in practise for the prompt "What organization is that?" where you see that the model looks at the context & type
Here is an example of how that looks in practise for the prompt "What organization is that?" where you see that the model looks at the context & type