Update README.md

README.md

@@ -199,7 +199,7 @@ GPTCache offers the following primary benefits:
 
 - **Decreased expenses**: Most LLM services charge fees based on a combination of number of requests and [token count](https://openai.com/pricing). GPTCache effectively minimizes your expenses by caching query results, which in turn reduces the number of requests and tokens sent to the LLM service. As a result, you can enjoy a more cost-efficient experience when using the service.
 - **Enhanced performance**: LLMs employ generative AI algorithms to generate responses in real-time, a process that can sometimes be time-consuming. However, when a similar query is cached, the response time significantly improves, as the result is fetched directly from the cache, eliminating the need to interact with the LLM service. In most situations, GPTCache can also provide superior query throughput compared to standard LLM services.
-- **Flexible development environment**: While developing LLM applications, a connection to LLM APIs is usually required, and it's essential to thoroughly test your application before deploying it in a production environment. GPTCache offers an interface identical to LLM APIs and supports storing LLM-generated or mocked data. This functionality allows for seamless development and testing of your application without the need to connect to the LLM service.
+- **Adaptable development and testing environment**: As a developer working on LLM applications, you're aware that connecting to LLM APIs is generally necessary, and comprehensive testing of your application is crucial before moving it to a production environment. GPTCache provides an interface that mirrors LLM APIs and accommodates storage of both LLM-generated and mocked data. This feature enables you to effortlessly develop and test your application, eliminating the need to connect to the LLM service.
 - **Improved scalability and availability**: LLM services frequently enforce [rate limits](https://platform.openai.com/docs/guides/rate-limits), which are constraints that APIs place on the number of times a user or client can access the server within a given timeframe. Hitting a rate limit means that additional requests will be blocked until a certain period has elapsed, leading to a service outage. With GPTCache, you can easily scale to accommodate an increasing volume of of queries, ensuring consistent performance as your application's user base expands.
 
 ## 🤔 How does it work?