diff --git a/backend/prompts.py b/backend/prompts.py
index 916fd37..2a201a5 100644
--- a/backend/prompts.py
+++ b/backend/prompts.py
@@ -281,6 +281,30 @@ If the sheet has no extractable objects, return an empty objects array.
 Optional sheet hint: {sheet_hint}"""
 
 
+# ---------------------------------------------------------------------------
+# Stage 2b - text-only structuring (extraction retry ladder, rung 2)
+# ---------------------------------------------------------------------------
+
+TEXT_STRUCTURING_SYSTEM_PROMPT = """You are a construction document structuring engine.
+You receive the deterministic text layer extracted from one drawing sheet. It is complete and authoritative.
+Your ONLY job is to segment it into structured objects. You are NOT reading an image. You must NOT invent, complete, or correct any text.
+Rules:
+- Every numbered note, schedule row, callout, tag, legend entry, and title-block field becomes its own object.
+- source_text must be copied VERBATIM from the input, character-for-character. Never paraphrase.
+- Cover the ENTIRE input. Omitting a note is a failure. When unsure of an object's type, use general_note with confidence low.
+- Numbers, model numbers, dimensions, and tags must appear in source_text exactly as in the input.
+Respond only with valid JSON."""
+
+TEXT_STRUCTURING_USER_INSTRUCTION = """Segment this sheet's text layer into structured construction objects.
+Every note, schedule row, callout, tag, and title-block field in the text layer must become an object - omit nothing.
+Respond ONLY with a valid JSON object - no markdown fences:
+{ "sheet": { "sheet_number": "string or null", "sheet_title": "string or null", "discipline": "string or null", "drawing_type": "string or null", "level": "string or null", "scale": "string or null" }, "objects": [ { "object_id": "string", "object_type": "room | door | window | wall | finish | ceiling | dimension | grid | callout | keynote | general_note | equipment | plumbing_fixture | mechanical_equipment | electrical_device | lighting_fixture | structural_element | schedule_reference | symbol | abbreviation", "category": "architectural | structural | mechanical | electrical | plumbing | code | general", "tag": "string or null", "name": "string or null", "description": "string or null", "attributes": { "attribute_name": "attribute_value" }, "location_key": { "room_number": "string or null", "grid": "string or null", "detail_reference": "string or null" }, "source_text": "VERBATIM text copied from the input", "graphical_basis": null, "review_uses": [ "schedule_comparison", "cross_discipline_coordination", "code_review", "constructability_review" ], "confidence": "high | medium | low" } ], "unresolved_items": [] }
+Optional sheet hint: {sheet_hint}
+
+TEXT LAYER (segment ALL of it):
+{text_layer}"""
+
+
 # ---------------------------------------------------------------------------
 # Stage 3a - assertion normalization  (WIRED: normalizer.py)
 # ---------------------------------------------------------------------------
diff --git a/tests/agents/test_extraction_ladder.py b/tests/agents/test_extraction_ladder.py
new file mode 100644
index 0000000..33ea10c
--- /dev/null
+++ b/tests/agents/test_extraction_ladder.py
@@ -0,0 +1,6 @@
+from backend.prompts import TEXT_STRUCTURING_SYSTEM_PROMPT, TEXT_STRUCTURING_USER_INSTRUCTION
+
+def test_text_structuring_prompt_demands_verbatim_and_completeness():
+    assert "verbatim" in TEXT_STRUCTURING_USER_INSTRUCTION.lower()
+    assert "every" in TEXT_STRUCTURING_USER_INSTRUCTION.lower()
+    assert "{text_layer}" in TEXT_STRUCTURING_USER_INSTRUCTION