diff --git a/docs/source/index.rst b/docs/source/index.rst index 80d364b1..c587124f 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -90,8 +90,11 @@ You have a similar coding experience as PyTorch. Here is a side to side comparis 3. **Suitted for Both Researchers and Production Engineers** - We start with understanding the frontier of AI research and LLM productionalization, and our design is grounded on our understanding of all dataflow in LLM applications, and - the process of + On top of the easiness to use, we in particular optimize the configurability of components for researchers to build their solutions and to benchmark existing solutions. + Like how PyTorch has united both researchers and production teams, it enables smooth transition from research to production. + With researchers building on LightRAG, production engineers can easily take over the method and test and iterate on their production data. + Researchers will want their code to be adapted into more products too. + **LightRAG vs other LLM libraries:**