Perplexity's "Search as Code" lets AI models write their own search pipelines instead of calling fixed APIs
Quick Answer
This paper shows that Perplexity's 'Search as Code' allows AI models to autonomously create search pipelines in Python, outperforming OpenAI and Anthropic on benchmarks while reducing token costs by up to 85%.
Quick Take
Perplexity's 'Search as Code' allows AI models to autonomously create search pipelines in Python, outperforming OpenAI and Anthropic on benchmarks while reducing token costs by up to 85%. This innovative approach enables enhanced filtering and deduplication within a sandbox environment.
Key Points
- AI models can now write their own search routines in Python.
- The new system outperforms OpenAI and Anthropic on key benchmarks.
- Token costs are reduced by up to 85% with this architecture.
- Filtering and deduplication are handled inside a sandbox environment.
- This approach marks a shift from rigid search APIs.
Article Excerpt
From source RSS / original summaryPerplexity's new "Search as Code" architecture dumps rigid search APIs and lets AI models write their own search routines in Python. By letting the agent handle its own filtering and deduplication inside a sandbox, the system beats OpenAI and Anthropic on key benchmarks, while cutting token costs by up to 85 percent. The article Perplexity's "Search as Code" lets AI models write their own search pipelines instead of calling fixed APIs appeared first on The Decoder.
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