feat: overhaul session documentation, PSA notes, and client communications

- Reformat PSA resolution/escalation notes: clean single-line header,
  steps with engineer responses inline, remove duplicate timing blocks,
  remove AI confidence section, add follow-up recommendations
- Standardize time display to decimal hours (e.g. 0.25 hrs) across all
  note formatters and status update context
- Add follow_up_recommendations to SessionDocumentation schema and
  surface in SessionDocView; extracted from resolution suggestion steps
- Add _build_what_we_know() helper: uses session.evidence_items when
  cockpit branch merges, falls back to deriving findings from steps
- Fix option label lookup in generate_status_update (was passing raw
  machine values to AI instead of human-readable labels)
- Add 'What We Know' section to status update ticket notes prompt
- Improve _build_session_context in resolution_output_generator to
  include intake text and full step details instead of truncated chat
- Add request_info audience type: client-facing information request
  that skips the length step and generates a numbered question list
- Improve client_update and email_draft prompts with per-context
  guidance (status/resolution/escalation) and fix escalation subject
  line from 'Specialist Review' to 'Specialist Assistance'

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

backend/app/services/resolution_output_generator.py

@@ -83,19 +83,55 @@ class ResolutionOutputGenerator:
         return output
 
     def _build_session_context(self, session: AISession) -> str:
+        intake = session.intake_content or {}
+        intake_text = intake.get("text", "") or str(intake)
         parts = [
             f"Problem: {session.problem_summary or 'Unknown'}",
             f"Domain: {session.problem_domain or 'Unknown'}",
+            f"Original intake: {intake_text[:300]}",
             f"Resolution: {session.resolution_summary or 'Not specified'}",
-            f"Steps taken: {session.step_count}",
         ]
-        msgs = session.conversation_messages or []
-        if msgs:
-            parts.append("\nConversation highlights:")
-            for msg in msgs[-10:]:
-                role = msg.get("role", "unknown")
-                content = msg.get("content", "")[:200]
-                parts.append(f"  [{role}]: {content}")
+
+        steps = sorted(session.steps or [], key=lambda s: s.step_order)
+        diagnostic = []
+        follow_ups: list[str] = []
+        for step in steps:
+            content = step.content or {}
+            step_type = content.get("type", "")
+            if step_type == "resolution_suggestion":
+                recs = content.get("follow_up_recommendations", [])
+                if isinstance(recs, list):
+                    follow_ups.extend(recs)
+                continue
+            description = content.get("text", "").strip()
+            if not description:
+                continue
+            response = None
+            if step.was_skipped:
+                response = "skipped"
+            elif step.selected_option and step.options_presented:
+                for opt in step.options_presented:
+                    if opt.get("value") == step.selected_option:
+                        response = opt.get("label", step.selected_option)
+                        break
+                else:
+                    response = step.selected_option
+            elif step.selected_option:
+                response = step.selected_option
+            elif step.free_text_input:
+                response = step.free_text_input
+            entry = f"  {step.step_order + 1}. {description}"
+            if response and response != "skipped":
+                entry += f" — {response}"
+            diagnostic.append(entry)
+
+        if diagnostic:
+            parts.append("\nDiagnostic steps:")
+            parts.extend(diagnostic)
+        if follow_ups:
+            parts.append("\nRecommended follow-up:")
+            parts.extend(f"  - {r}" for r in follow_ups)
+
         return "\n".join(parts)
 
     def _psa_notes_prompt(self, context: str) -> str: