architecture clarification

Architecture_Overview.py

@@ -78,7 +78,9 @@ def _(mo):
 
     **Goal:** Convert unstructured text into a structured dataset.
 
-    1.  **Input:** All 26 Transcripts + `master_codebook.json`.
+    This will be a dedicated notebook, and be run per transcript.
+
+    1.  **Input:** Transcript + `master_codebook.json`.
     2.  **Process:**
         *   The LLM analyzes each transcript segment-by-segment.
         *   It extracts specific quotes that match a Theme Definition.
@@ -86,8 +88,9 @@ def _(mo):
         *   **Granular Sentiment Analysis:** For each quote, the model identifies:
             *   **Subject:** The specific topic/object being discussed (e.g., "Login Flow", "Brand Tone").
             *   **Sentiment:** Positive / Neutral / Negative.
-    3.  **Output:** `coded_segments.csv`
+    3.  **Output:** `<transcript_name>_coded_segments.csv`
         *   Columns: `Source_File`, `Speaker`, `Theme`, `Quote`, `Subject`, `Sentiment`, `Context`.
+        *   Each transcript produces its own CSV-file, which can be reviewed and adjusted before moving to the next stage
     """)
     return