Simplified the json schema passed to the LLM, and implemented more forgiving error handling for LLM proposed corrections

tests/test_prompts.py

@@ -1,3 +1,5 @@
+import json
+
 from audita.chunking import chunk_transcript
 from audita.prompts import build_glossary_correction_messages
 from audita.schemas import parse_glossary_yaml, parse_transcript_json
@@ -32,3 +34,31 @@ def test_prompt_requires_acoustically_plausible_transcription_errors():
     assert '"gestures" to "Jesters"' in prompt_text
     assert '"Lyra" to "Jesters"' in prompt_text
     assert "should be omitted" in prompt_text
+
+
+def test_prompt_uses_simplified_segment_payload():
+    transcript = parse_transcript_json(
+        """
+        [
+          {"speaker": "Eric", "start": 0.0, "end": 1.0, "text": "The gestures are nearby."}
+        ]
+        """
+    )
+    glossary = parse_glossary_yaml(
+        """
+        glossary:
+          - name: "Jesters"
+            category: faction
+            summary: "The Jesters are a local faction."
+        """
+    )
+    section = chunk_transcript(transcript, max_section_tokens=16000)[0]
+
+    messages = build_glossary_correction_messages(section, glossary)
+    transcript_json = messages[1]["content"].split("Transcript section:\n", maxsplit=1)[1]
+    prompt_segments = json.loads(transcript_json)
+
+    assert prompt_segments == [{"segment_id": 0, "original_text": "The gestures are nearby."}]
+    assert "speaker" not in prompt_segments[0]
+    assert "start" not in prompt_segments[0]
+    assert "end" not in prompt_segments[0]