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@@ -89,7 +89,7 @@ You can find such examples [here](https://pathway.com/developers/user-guide/llm-
 - **A wide range of connectors**: Pathway comes with connectors that connect to external data sources such as Kafka, GDrive, PostgreSQL, or SharePoint. Its Airbyte connector allows you to connect to more than 300 different data sources. If the connector you want is not available, you can build your own custom connector using Pathway Python connector.
 - **Stateless and stateful transformations**: Pathway supports stateful transformations such as joins, windowing, and sorting. It provides many transformations directly implemented in Rust. In addition to the provided transformation, you can use any Python function. You can implement your own or you can use any Python library to process your data.
 - **Persistence**: Pathway provides persistence to save the state of the computation. This allows you to restart your pipeline after an update or a crash. Your pipelines are in good hands with Pathway!
-- **Consistency**: Pathway handles the time for you, making your all your computations are consistent. In particular, Pathway manages late and out-of-order points by updating its results whenever new (or late, in this case) data points come into the system. The free version of Pathway gives the "at least once" consistency while the enterprise version provides the "exactly once" consistency.
+- **Consistency**: Pathway handles the time for you, making sure all your computations are consistent. In particular, Pathway manages late and out-of-order points by updating its results whenever new (or late, in this case) data points come into the system. The free version of Pathway gives the "at least once" consistency while the enterprise version provides the "exactly once" consistency.
 - **Scalable Rust engine**: with Pathway Rust engine, you are free from the usual limits imposed by Python. You can easily do multithreading, multiprocessing, and distributed computations.
 - **LLM helpers**: Pathway provides an LLM extension with all the utilities to integrate LLMs with your data pipelines (LLM wrappers, parsers, embedders, splitters), including an in-memory real-time Vector Index, and integrations with LLamaIndex and LangChain. You can quickly build and deploy RAG applications with your live documents.