Apply suggestions from code review

Co-authored-by: Maxime Vdb <m.verger@rasa.com>

docs/docs/llms/llm-dialogue.mdx

@@ -13,16 +13,16 @@ import RasaDiscoveryBanner from "@theme/RasaDiscoveryBanner";
 ## Motivation
 
 Our approach aims to harness the strengths of both Large Language Models (LLMs)
-and the control offered by business logic. This innovative blend enables you to
+and the control needed for business logic. This innovative blend enables you to
 manage both the flexible and predictable aspects of dialogues within your
 chatbot.
 
-By utilizing LLMs, we can effectively handle 'unhappy paths'—instances where
+By utilizing LLMs, we can effectively handle ["unhappy paths"—instances](../glossary.mdx#happy--unhappy-paths) where
 user interactions diverge from the expected or desired course. Meanwhile,
-business logic or 'Flows' are used to manage 'happy paths'—scenarios where
+business logic or 'Flows' are used to manage ["happy paths"—scenarios](../glossary.mdx#happy--unhappy-paths) where
 interactions proceed as anticipated.
 
-Combining LLMs and Flows helps prevent "black box" behavior, whereby the
+Combining LLMs and Flows helps prevent "black box" behaviors, whereby the
 chatbot's responses can seem inexplicable or out of control. With this method,
 you only need to specify a minimal amount of business logic. At the same time,
 it opens up a wide range of possibilities for customizing your chatbot's
@@ -34,17 +34,17 @@ Here's a breakdown of our approach:
 
 - We specify 'happy paths' using business logic. This means setting out clear,
   pre-defined paths that the conversation should follow under ideal
-  circumstances using [flows](../flows.mdx).
+  circumstances using [Flows](../flows.mdx).
 - We identify patterns of 'unhappy paths' and specify these using separate
-  Flows. These ["conversational patterns"](./unhappy-paths.mdx) define how the
+  Flows. These [conversational patterns](./unhappy-paths.mdx) define how the
   chatbot should respond when the user's input doesn't match the expected course
   of a flow.
 - We use Flows to keep track of the conversation's state, ensuring we always
   know where we are in the dialogue and what's been covered.
 - We employ LLMs to update the state of these Flows. These language models
   process user input, adjusting the conversation's course as needed.
 - We use LLMs to [improve the chatbot's responses](./llm-nlg.mdx). These
-  language models generate the chatbot's responses, ensuring they're natural and
+  language models generate the chatbot's responses, ensuring they feel natural and
   fluent.
 
 ## Configuration and Setup