diff --git a/align_system/algorithms/outlines_adm.py b/align_system/algorithms/outlines_adm.py
index 9dda92b9..9694d7b8 100644
--- a/align_system/algorithms/outlines_adm.py
+++ b/align_system/algorithms/outlines_adm.py
@@ -25,7 +25,6 @@
     calculate_votes,
     filter_votes_to_responses,
 )
-from align_system.utils.hydrate_state import hydrate_scenario_state
 from align_system.algorithms.abstracts import ActionBasedADM
 from align_system.prompt_engineering.outlines_prompts import (
     baseline_system_prompt,
@@ -41,13 +40,13 @@
     followup_clarify_treatment_from_list,
     followup_clarify_tag,
     action_choice_json_schema,
+    action_choice_json_schema_untrimmed,
     aid_choice_json_schema,
     character_choice_json_schema,
     tag_choice_json_schema,
     treatment_choice_json_schema,
     treatment_choice_from_list_json_schema,
-    detailed_unstructured_treatment_action_text,
-    detailed_unstructured_tagging_action_text
+
 )
 
 log = logging.getLogger(__name__)
@@ -55,15 +54,19 @@
 
 
 class OutlinesTransformersADM(ActionBasedADM):
-    def __init__(self,
-                 model_name,
-                 device='auto',
-                 baseline=False,
-                 sampler=MultinomialSampler(),
-                 **kwargs):
+    def __init__(
+        self,
+        model_name,
+        device='auto',
+        baseline=False,
+        mode='eval',
+        sampler=MultinomialSampler(),
+        **kwargs
+    ):
         self.baseline = baseline
 
         model_kwargs = kwargs.get('model_kwargs', {})
+        self.mode = mode
         if 'precision' in kwargs:
             if kwargs['precision'] == 'half':
                 torch_dtype = torch.float16
@@ -87,6 +90,8 @@ def __init__(self,
         # the sampler itself (which defaults to 1); setting the number
         # of samples in the sampler may result in unexpected behavior
         self.sampler = sampler
+        # Edited prompt from the Demo interface
+        self._system_ui_prompt = None
 
     def dialog_to_prompt(self, dialog):
         tokenizer = self.model.tokenizer.tokenizer
@@ -152,25 +157,35 @@ def batched(cls, iterable, n):
             yield batch
 
     @classmethod
-    def run_in_batches(cls, inference_function, inputs, batch_size):
+    def run_in_batches(cls, inference_function, inputs, batch_size, max_tokens, seed):
         ''' Batch inference to avoid out of memory error'''
         outputs = []
         for batch in cls.batched(inputs, batch_size):
-            output = inference_function(list(batch))
+            output = inference_function(
+                list(batch),
+                max_tokens=max_tokens,
+                rng=torch.cuda.manual_seed(seed)
+            )
             if not isinstance(output, list):
                 output = [output]
             outputs.extend(output)
         return outputs
 
-    def top_level_choose_action(self,
-                                scenario_state,
-                                available_actions,
-                                alignment_target,
-                                num_positive_samples=1,
-                                num_negative_samples=0,
-                                generator_batch_size=5,
-                                kdma_descriptions_map='align_system/prompt_engineering/kdma_descriptions.yml',
-                                **kwargs):
+    @property
+    def system_ui_prompt(self) -> str:
+        return self._system_ui_prompt
+
+    @system_ui_prompt.setter
+    def system_ui_prompt(self, edited_system_prompt: str):
+        self._system_ui_prompt = edited_system_prompt
+
+    def get_dialog_texts(self, scenario_state,
+            available_actions,
+            alignment_target,
+            num_positive_samples=1,
+            num_negative_samples=0,
+            kdma_descriptions_map="align_system/prompt_engineering/kdma_descriptions.yml",
+            **kwargs):
         if self.baseline and num_negative_samples > 0:
             raise RuntimeError("No notion of negative samples for baseline run")
         if self.baseline and "incontext" in kwargs and kwargs["incontext"]["number"] > 0:
@@ -185,6 +200,8 @@ def top_level_choose_action(self,
             available_actions,
             scenario_state
             )
+        # Sort the choices
+        choices = sorted(choices)
 
         positive_icl_examples = []
         negative_icl_examples = []
@@ -224,8 +241,11 @@ def top_level_choose_action(self,
 
                 # Create positive ICL example generators
                 positive_target = {'kdma': kdma, 'name': name, 'value': value}
-                positive_icl_example_generator = incontext_utils.BaselineIncontextExampleGenerator(incontext_settings,
-                                                                                                    [positive_target])
+                positive_icl_example_generator = (
+                    incontext_utils.BaselineIncontextExampleGenerator(
+                        incontext_settings, [positive_target]
+                    )
+                )
                 # Get subset of relevant of examples
                 positive_selected_icl_examples = positive_icl_example_generator.select_icl_examples(
                     sys_kdma_name=kdma,
@@ -244,8 +264,11 @@ def top_level_choose_action(self,
                 if num_negative_samples > 0:
                     # Create negative ICL example generators
                     negative_target = {'kdma': kdma, 'name': name, 'value': negative_value}
-                    negative_icl_example_generator = incontext_utils.BaselineIncontextExampleGenerator(incontext_settings,
-                                                                                                    [negative_target])
+                    negative_icl_example_generator = (
+                        incontext_utils.BaselineIncontextExampleGenerator(
+                            incontext_settings, [negative_target]
+                        )
+                    )
                     # Get subset of relevant of examples
                     negative_selected_icl_examples = negative_icl_example_generator.select_icl_examples(
                         sys_kdma_name=kdma,
@@ -265,10 +288,13 @@ def top_level_choose_action(self,
             if "incontext" in kwargs and kwargs["incontext"]["number"] > 0:
                 raise RuntimeError("No notion of incontext examples for baseline run")
 
+        shuffled_choices = choices
         positive_dialogs = []
         for _ in range(num_positive_samples):
-            shuffled_choices = random.sample(choices, len(choices))
-
+            if kwargs["demo_kwargs"]["shuffle_choices"]:
+                shuffled_choices = random.sample(choices, len(choices))
+            if self.system_ui_prompt is not None and self.mode == "demo":
+                positive_system_prompt = self.system_ui_prompt
             prompt = action_selection_prompt(scenario_description, shuffled_choices)
             dialog = [{'role': 'system', 'content': positive_system_prompt}]
             dialog.extend(positive_icl_examples)
@@ -278,8 +304,8 @@ def top_level_choose_action(self,
 
         negative_dialogs = []
         for _ in range(num_negative_samples):
-            shuffled_choices = random.sample(choices, len(choices))
-
+            if kwargs["demo_kwargs"]["shuffle_choices"]:
+                shuffled_choices = random.sample(choices, len(choices))
             prompt = action_selection_prompt(scenario_description, shuffled_choices)
             dialog = [{'role': 'system', 'content': negative_system_prompt}]
             dialog.extend(negative_icl_examples)
@@ -290,20 +316,66 @@ def top_level_choose_action(self,
         # Need to set the whitespace_pattern to prevent the state
         # machine from looping indefinitely in some cases, see:
         # https://github.com/outlines-dev/outlines/issues/690#issuecomment-2102291934
-        generator = outlines.generate.json(
-            self.model,
-            action_choice_json_schema(json.dumps(choices)),
-            sampler=self.sampler,
-            whitespace_pattern=r"[ ]?")
 
-        dialog_texts = [self.dialog_to_prompt(d) for d in
-                        itertools.chain(positive_dialogs, negative_dialogs)]
+        dialog_texts = [
+            self.dialog_to_prompt(d)
+            for d in itertools.chain(positive_dialogs, negative_dialogs)
+        ]
 
-        log.info("[bold]*DIALOG PROMPT*[/bold]",
-                 extra={"markup": True})
+        log.info("[bold]*DIALOG PROMPT*[/bold]", extra={"markup": True})
         log.info(dialog_texts[0])
 
-        responses = self.run_in_batches(generator, dialog_texts, generator_batch_size)
+        return dialog_texts, positive_dialogs
+
+    def top_level_choose_action(self,
+                                scenario_state,
+                                available_actions,
+                                alignment_target,
+                                num_positive_samples=1,
+                                num_negative_samples=0,
+                                generator_batch_size=5,
+                                kdma_descriptions_map='align_system/prompt_engineering/kdma_descriptions.yml',
+                                **kwargs):
+        if 'demo_kwargs' not in kwargs and self.mode == "demo":
+            raise ValueError('Demo configuration missing')
+
+        demo_kwargs = kwargs["demo_kwargs"]
+        choices = adm_utils.format_choices(
+            [a.unstructured for a in available_actions],
+            available_actions,
+            scenario_state,
+        )
+
+        dialog_texts, positive_dialogs = self.get_dialog_texts(
+            scenario_state,
+            available_actions,
+            alignment_target,
+            num_positive_samples=num_positive_samples,
+            num_negative_samples=num_negative_samples,
+            generator_batch_size=generator_batch_size,
+            kdma_descriptions_map=kdma_descriptions_map,
+            **kwargs
+        )
+        if self.mode == "eval":
+            generator = outlines.generate.json(
+                self.model,
+                action_choice_json_schema(json.dumps(choices)),
+                sampler=self.sampler,
+                whitespace_pattern=r"[ ]?")
+        else:
+            generator = outlines.generate.json(
+                self.model,
+                action_choice_json_schema_untrimmed(json.dumps(choices)),
+                sampler=self.sampler,
+                whitespace_pattern=r"[ ]?",
+            )
+        responses = self.run_in_batches(
+            inference_function=generator,
+            inputs=dialog_texts,
+            batch_size=generator_batch_size,
+            max_tokens=int(demo_kwargs["max_generator_tokens"]),
+            seed=int(demo_kwargs["generator_seed"]),
+        )
         positive_responses_choices =\
             [r['action_choice'] for r in
              responses[0:num_positive_samples]]
diff --git a/align_system/prompt_engineering/outlines_prompts.py b/align_system/prompt_engineering/outlines_prompts.py
index df112f45..69b48e61 100644
--- a/align_system/prompt_engineering/outlines_prompts.py
+++ b/align_system/prompt_engineering/outlines_prompts.py
@@ -347,6 +347,21 @@ def action_choice_json_schema(choices_json_str):
     '''
 
 
+@outlines.prompt
+def action_choice_json_schema_untrimmed(choices_json_str):
+    '''
+    {"$defs": {"ActionChoice": {"enum": {{ choices_json_str }},
+    "title": "ActionChoice",
+    "type": "string"}},
+    "properties": {"detailed_reasoning": {"title": "Detailed Reasoning",
+    "type": "string", "minLength": 1},
+    "action_choice": {"$ref": "#/$defs/ActionChoice"}},
+    "required": ["detailed_reasoning", "action_choice"],
+    "title": "ActionSelection",
+    "type": "object"}
+    '''
+
+
 @outlines.prompt
 def character_choice_json_schema(choices_json_str):
     '''