Update title formatting and improve consistency in audio-to-LLM documentation

chapters/audio-intelligence/audio-to-llm.mdx

@@ -1,32 +1,32 @@
 ---
-title: Audio to LLM
+title: Audio-to-LLM
 description: "Run your own prompts on a pre-recorded transcript with an LLM - summaries, Q&A, extraction, and more."
 ---
 
 import PrerecordedBadge from "/snippets/badges/prerecorded.mdx"
 
 <PrerecordedBadge />
 
-**Audio to LLM** runs once the transcription is generated. You provide **one or more prompts**; each prompt is executed against the **transcript text** from the same job using the configured model, yielding **one LLM response per prompt**. Use it to extract action items, answer questions about the recording, or run any text analysis you express in natural language.
+**Audio-to-LLM** runs once the transcription is generated. You provide **one or more prompts**; each prompt is executed against the **transcript text** from the same job using the configured model, yielding **one LLM response per prompt**. Use it to extract action items, answer questions about the recording, or run any text analysis you express in natural language.
 
-Unlike the built-in [Summarization](/chapters/audio-intelligence/summarization) feature — which produces a fixed-format summary — Audio to LLM lets you write **your own instructions**: ask for a summary in the exact format, tone, and level of detail your product needs, or combine a summary with other analyses (action items, compliance checks) in a single request.
+Unlike the built-in [Summarization](/chapters/audio-intelligence/summarization) feature — which produces a fixed-format summary — Audio-to-LLM lets you write **your own instructions**: ask for a summary in the exact format, tone, and level of detail your product needs, or combine a summary with other analyses (action items, compliance checks) in a single request.
 
 ## Usage
 
 1. Include `audio_to_llm: true` and an `audio_to_llm_config` object (at minimum, a `prompts` array) in your [pre-recorded transcription request](/chapters/pre-recorded-stt/quickstart).
 2. Gladia transcribes the audio, along with any other audio-intelligence options you enabled on that request.
 3. Each prompt is run on the resulting transcript via the LLM.
 4. The API returns **one result object per prompt** (same order as `prompts`), each containing the original `prompt` and the model `response`.
 
 <Note>
-  Audio to LLM sends **plain transcript text** to the model. Raw audio and other fields from the transcription response are **not** added to the LLM prompt context.
+  Audio-to-LLM sends **plain transcript text** to the model. Raw audio and other fields from the transcription response are **not** added to the LLM prompt context.
 </Note>
 
 ## Model selection
 
 By default the model used to execute your prompts is **[GPT 5.4 Nano](https://openrouter.ai/openai/gpt-5.4-nano)** (`openai/gpt-5.4-nano`), a fast option suited to high-volume summaries and extraction. The `model` can be customized when you need stronger reasoning, richer analysis, longer outputs, or behavior that fits a specific model.
 
 You can use **any model listed on [OpenRouter](https://openrouter.ai/models)** by setting the `model` key. Prices reflect the public OpenRouter rate plus a platform fee added by Gladia.
 
 ## Example
 

chapters/audio-intelligence/index.mdx

@@ -57,7 +57,7 @@ Use these capabilities alongside Live or Pre-recorded STT to automate workflows
   </Card>
 
   <Card
-    title="Audio to LLM"
+    title="Audio-to-LLM"
     icon="robot"
     href="/chapters/audio-intelligence/audio-to-llm"
   >

chapters/pre-recorded-stt/audio-intelligence.mdx

@@ -74,7 +74,7 @@ Audio intelligence turns raw speech into structured, useful data on top of trans
   </Card>
 
   <Card
-    title="Audio to LLM"
+    title="Audio-to-LLM"
     icon="robot"
     href="/chapters/audio-intelligence/audio-to-llm"
   >