diff --git a/app/routers/chat.py b/app/routers/chat.py
index ae44547..986c131 100644
--- a/app/routers/chat.py
+++ b/app/routers/chat.py
@@ -1,8 +1,9 @@
 """
 FILE: app/routers/chat.py
 DESCRIPTION: Haupt-Chat-Interface (RAG & Interview). Enthält Intent-Router (Keywords/LLM) und Prompt-Construction.
-VERSION: 2.7.0 (WP-22 Semantic Graph Routing)
+VERSION: 2.7.1 (WP-22 Semantic Graph Routing)
 STATUS: Active
+FIX: Umstellung auf llm.get_prompt() zur Behebung des 500 Server Errors (Dictionary replace crash).
 DEPENDENCIES: app.config, app.models.dto, app.services.llm_service, app.core.retriever, app.services.feedback_service
 EXTERNAL_CONFIG: config/decision_engine.yaml, config/types.yaml
 """
@@ -199,7 +200,8 @@ async def _classify_intent(query: str, llm: LLMService) -> tuple[str, str]:
 
     # 3. SLOW PATH: LLM Router
     if settings.get("llm_fallback_enabled", False):
-        router_prompt_template = llm.prompts.get("router_prompt", "")
+        # FIX: Nutze get_prompt statt direktem Zugriff auf dict
+        router_prompt_template = llm.get_prompt("router_prompt")
         
         if router_prompt_template:
             prompt = router_prompt_template.replace("{query}", query)
@@ -262,7 +264,8 @@ async def chat_endpoint(
             logger.info(f"[{query_id}] Interview Type: {target_type}. Fields: {len(fields_list)}")
             fields_str = "\n- " + "\n- ".join(fields_list)
             
-            template = llm.prompts.get(prompt_key, "")
+            # FIX: Nutze get_prompt() zur Auflösung der provider-spezifischen Templates
+            template = llm.get_prompt(prompt_key)
             final_prompt = template.replace("{context_str}", "Dialogverlauf...") \
                                    .replace("{query}", request.message) \
                                    .replace("{target_type}", target_type) \
@@ -276,7 +279,6 @@ async def chat_endpoint(
             prepend_instr = strategy.get("prepend_instruction", "")
             
             # --- WP-22: Semantic Graph Routing (Teil C) ---
-            # Wir laden die konfigurierten Edge-Boosts für diesen Intent
             edge_boosts = strategy.get("edge_boosts", {})
             if edge_boosts:
                 logger.info(f"[{query_id}] Applying Edge Boosts: {edge_boosts}")
@@ -286,7 +288,6 @@ async def chat_endpoint(
                 mode="hybrid",     
                 top_k=request.top_k,
                 explain=request.explain,
-                # WP-22: Boosts an den Retriever weitergeben
                 boost_edges=edge_boosts
             )
             retrieve_result = await retriever.search(query_req)
@@ -299,7 +300,6 @@ async def chat_endpoint(
                     top_k=3,
                     filters={"type": inject_types},
                     explain=False,
-                    # WP-22: Boosts auch hier anwenden (Konsistenz)
                     boost_edges=edge_boosts
                 )
                 strategy_result = await retriever.search(strategy_req)
@@ -313,8 +313,12 @@ async def chat_endpoint(
             else:
                 context_str = _build_enriched_context(hits)
 
-            template = llm.prompts.get(prompt_key, "{context_str}\n\n{query}")
+            # FIX: Nutze get_prompt() zur Auflösung der provider-spezifischen Templates
+            template = llm.get_prompt(prompt_key)
             
+            if not template:
+                 template = "{context_str}\n\n{query}"
+
             if prepend_instr:
                  context_str = f"{prepend_instr}\n\n{context_str}"
 
@@ -322,7 +326,7 @@ async def chat_endpoint(
             sources_hits = hits
         
         # --- GENERATION ---
-        system_prompt = llm.prompts.get("system_prompt", "")
+        system_prompt = llm.get_prompt("system_prompt")
         
         # Chat nutzt IMMER realtime priority
         answer_text = await llm.generate_raw_response(