small edit

Author: codingbandit

11_Prompt_Engineering/README.md

@@ -32,7 +32,7 @@ GPT language models can be fine-tuned to achieve several common tasks such as se
 The RAG pattern facilitates bringing private proprietary knowledge to the model so that it can perform Question Answering over this content. Remember that Large Language Models are indexed only on public information.
 Because the RAG technique accesses external knowledge sources to complete tasks, it enables more factual consistency, improves the reliability of the generated responses, and helps to mitigate the problem of "hallucination".
 
-In some cases, the RAG process involves a technique called vectorization on the proprietary data. The user prompt is compared to the vector store and only the most relevant/matching pieces of information are returned and stuffed into prompt for the LLM to reason over and provide an answer. The next set of demos will go into this further.
+In some cases, the RAG process involves a technique called vectorization on the proprietary data. The user prompt is compared to the vector store and only the most relevant/matching pieces of information are returned and stuffed into prompt for the LLM to reason over and provide an answer.
 
 ## Chain of thought
 ## ReAct