diff --git a/llm_core/llm_core/models/__init__.py b/llm_core/llm_core/models/__init__.py
index 6f9db4fdd..f5e3edd60 100644
--- a/llm_core/llm_core/models/__init__.py
+++ b/llm_core/llm_core/models/__init__.py
@@ -6,6 +6,7 @@
 
 
 DefaultModelConfig: Type[ModelConfig]
+MiniModelConfig: ModelConfig
 default_model_name = os.environ.get("LLM_DEFAULT_MODEL")
 evaluation_model_name = os.environ.get("LLM_EVALUATION_MODEL")
 
@@ -18,6 +19,8 @@
     types.append(openai_config.OpenAIModelConfig)
     if default_model_name in openai_config.available_models:
         DefaultModelConfig = openai_config.OpenAIModelConfig
+    if "openai_gpt-4o-mini" in openai_config.available_models:        
+        MiniModelConfig = openai_config.OpenAIModelConfig(model_name="openai_gpt-4o-mini",max_tokens=3000, temperature=0,top_p=0.9,presence_penalty=0,frequency_penalty=0)
     if evaluation_model_name in openai_config.available_models:
         evaluation_model = openai_config.available_models[evaluation_model_name]
 except AttributeError:
diff --git a/llm_core/llm_core/utils/llm_utils.py b/llm_core/llm_core/utils/llm_utils.py
index 4637b8558..0bfeb047d 100644
--- a/llm_core/llm_core/utils/llm_utils.py
+++ b/llm_core/llm_core/utils/llm_utils.py
@@ -1,7 +1,7 @@
 from typing import Type, TypeVar, List
 from pydantic import BaseModel
 import tiktoken
-from langchain.chat_models import ChatOpenAI
+from langchain_openai import AzureChatOpenAI, ChatOpenAI
 from langchain.base_language import BaseLanguageModel
 from langchain.prompts import (
     ChatPromptTemplate,
@@ -75,7 +75,7 @@ def supports_function_calling(model: BaseLanguageModel):
     Returns:
         boolean: True if the model supports function calling, False otherwise
     """
-    return isinstance(model, ChatOpenAI)
+    return isinstance(model, ChatOpenAI)  or isinstance(model, AzureChatOpenAI)
 
 
 def get_chat_prompt_with_formatting_instructions(
diff --git a/llm_core/llm_core/utils/predict_and_parse.py b/llm_core/llm_core/utils/predict_and_parse.py
index d73748bdf..e72bb3e91 100644
--- a/llm_core/llm_core/utils/predict_and_parse.py
+++ b/llm_core/llm_core/utils/predict_and_parse.py
@@ -12,7 +12,8 @@ async def predict_and_parse(
         chat_prompt: ChatPromptTemplate, 
         prompt_input: dict, 
         pydantic_object: Type[T], 
-        tags: Optional[List[str]]
+        tags: Optional[List[str]],
+        use_function_calling: bool = False
     ) -> Optional[T]:
     """Predicts an LLM completion using the model and parses the output using the provided Pydantic model
 
@@ -36,13 +37,30 @@ async def predict_and_parse(
     if experiment.run_id is not None:
         tags.append(f"run-{experiment.run_id}")
 
-    structured_output_llm = model.with_structured_output(pydantic_object, method="json_mode")
-    chain = RunnableSequence(
-        chat_prompt,
-        structured_output_llm
-    )
-
-    try:
-        return await chain.ainvoke(prompt_input, config={"tags": tags})
-    except ValidationError as e:
-        raise ValueError(f"Could not parse output: {e}") from e
\ No newline at end of file
+    
+    if (use_function_calling):
+        structured_output_llm = model.with_structured_output(pydantic_object)
+        chain = chat_prompt | structured_output_llm
+        
+        try:
+            result = await chain.ainvoke(prompt_input, config={"tags": tags})
+            
+            if isinstance(result, pydantic_object):
+                return result
+            else:
+                raise ValueError("Parsed output does not match the expected Pydantic model.")
+            
+        except ValidationError as e:
+            raise ValueError(f"Could not parse output: {e}") from e
+        
+    else:
+        structured_output_llm = model.with_structured_output(pydantic_object, method = "json_mode")
+        chain = RunnableSequence(
+            chat_prompt,
+            structured_output_llm
+        )
+        try:
+            return await chain.ainvoke(prompt_input, config={"tags": tags})
+        except ValidationError as e:
+            raise ValueError(f"Could not parse output: {e}") from e
+        
diff --git a/modules/text/module_text_llm/module_text_llm/__main__.py b/modules/text/module_text_llm/module_text_llm/__main__.py
index 6f0312729..e23998674 100644
--- a/modules/text/module_text_llm/module_text_llm/__main__.py
+++ b/modules/text/module_text_llm/module_text_llm/__main__.py
@@ -1,19 +1,16 @@
-import json
 import os
 from typing import List, Any
 
 import nltk
 import tiktoken
-
 from athena import app, submission_selector, submissions_consumer, feedback_consumer, feedback_provider, evaluation_provider
 from athena.text import Exercise, Submission, Feedback
 from athena.logger import logger
 
 from module_text_llm.config import Configuration
 from module_text_llm.evaluation import get_feedback_statistics, get_llm_statistics
-from module_text_llm.generate_suggestions import generate_suggestions
 from module_text_llm.generate_evaluation import generate_evaluation
-
+from module_text_llm.approach_controller import generate_suggestions
 
 @submissions_consumer
 def receive_submissions(exercise: Exercise, submissions: List[Submission]):
@@ -30,7 +27,6 @@ def select_submission(exercise: Exercise, submissions: List[Submission]) -> Subm
 def process_incoming_feedback(exercise: Exercise, submission: Submission, feedbacks: List[Feedback]):
     logger.info("process_feedback: Received %d feedbacks for submission %d of exercise %d.", len(feedbacks), submission.id, exercise.id)
 
-
 @feedback_provider
 async def suggest_feedback(exercise: Exercise, submission: Submission, is_graded: bool, module_config: Configuration) -> List[Feedback]:
     logger.info("suggest_feedback: %s suggestions for submission %d of exercise %d were requested",
diff --git a/modules/text/module_text_llm/module_text_llm/approach_config.py b/modules/text/module_text_llm/module_text_llm/approach_config.py
new file mode 100644
index 000000000..6626fdd85
--- /dev/null
+++ b/modules/text/module_text_llm/module_text_llm/approach_config.py
@@ -0,0 +1,16 @@
+from abc import ABC
+from pydantic import BaseModel, Field
+from llm_core.models import ModelConfigType, DefaultModelConfig
+from enum import Enum
+
+class ApproachType(str, Enum):
+    basic = "BasicApproach"
+    chain_of_thought = "ChainOfThought"
+    
+class ApproachConfig(BaseModel, ABC):
+    max_input_tokens: int = Field(default=3000, description="Maximum number of tokens in the input prompt.")
+    model: ModelConfigType = Field(default=DefaultModelConfig())
+    type: str = Field(..., description="The type of approach config")
+
+    class Config:
+        use_enum_values = True
\ No newline at end of file
diff --git a/modules/text/module_text_llm/module_text_llm/approach_controller.py b/modules/text/module_text_llm/module_text_llm/approach_controller.py
new file mode 100644
index 000000000..25329b8ec
--- /dev/null
+++ b/modules/text/module_text_llm/module_text_llm/approach_controller.py
@@ -0,0 +1,16 @@
+
+from typing import List
+from athena.text import Exercise, Submission, Feedback
+from module_text_llm.basic_approach import BasicApproachConfig
+from module_text_llm.chain_of_thought_approach  import ChainOfThoughtConfig
+from module_text_llm.approach_config import ApproachConfig
+
+from module_text_llm.basic_approach.generate_suggestions import generate_suggestions as generate_suggestions_basic
+from module_text_llm.chain_of_thought_approach.generate_suggestions import generate_suggestions as generate_cot_suggestions
+
+async def generate_suggestions(exercise: Exercise, submission: Submission, config: ApproachConfig, debug: bool) -> List[Feedback]:
+    if(isinstance(config, BasicApproachConfig)):
+        return await generate_suggestions_basic(exercise, submission, config, debug)
+    elif(isinstance(config, ChainOfThoughtConfig)):
+        return await generate_cot_suggestions(exercise, submission, config, debug)
+
diff --git a/modules/text/module_text_llm/module_text_llm/basic_approach/__init__.py b/modules/text/module_text_llm/module_text_llm/basic_approach/__init__.py
new file mode 100644
index 000000000..37a674e7c
--- /dev/null
+++ b/modules/text/module_text_llm/module_text_llm/basic_approach/__init__.py
@@ -0,0 +1,11 @@
+from module_text_llm.approach_config import ApproachConfig
+from pydantic import Field
+from typing import Literal
+
+
+from module_text_llm.basic_approach.prompt_generate_suggestions import GenerateSuggestionsPrompt
+
+class BasicApproachConfig(ApproachConfig):
+    type: Literal['basic'] = 'basic'
+    generate_suggestions_prompt: GenerateSuggestionsPrompt = Field(default=GenerateSuggestionsPrompt())
+
diff --git a/modules/text/module_text_llm/module_text_llm/generate_suggestions.py b/modules/text/module_text_llm/module_text_llm/basic_approach/generate_suggestions.py
similarity index 77%
rename from modules/text/module_text_llm/module_text_llm/generate_suggestions.py
rename to modules/text/module_text_llm/module_text_llm/basic_approach/generate_suggestions.py
index be928dc2e..b9e8694cb 100644
--- a/modules/text/module_text_llm/module_text_llm/generate_suggestions.py
+++ b/modules/text/module_text_llm/module_text_llm/basic_approach/generate_suggestions.py
@@ -1,46 +1,22 @@
-from typing import List, Optional, Sequence
-from pydantic import BaseModel, Field
+from typing import List
 
 from athena import emit_meta
 from athena.text import Exercise, Submission, Feedback
 from athena.logger import logger
-
-from module_text_llm.config import BasicApproachConfig
 from llm_core.utils.llm_utils import (
     get_chat_prompt_with_formatting_instructions, 
     check_prompt_length_and_omit_features_if_necessary, 
     num_tokens_from_prompt,
 )
+from athena.text import Exercise, Submission, Feedback
 from llm_core.utils.predict_and_parse import predict_and_parse
 
+from module_text_llm.config import BasicApproachConfig
 from module_text_llm.helpers.utils import add_sentence_numbers, get_index_range_from_line_range, format_grading_instructions
-
-class FeedbackModel(BaseModel):
-    title: str = Field(description="Very short title, i.e. feedback category or similar", example="Logic Error")
-    description: str = Field(description="Feedback description")
-    line_start: Optional[int] = Field(description="Referenced line number start, or empty if unreferenced")
-    line_end: Optional[int] = Field(description="Referenced line number end, or empty if unreferenced")
-    credits: float = Field(0.0, description="Number of points received/deducted")
-    grading_instruction_id: Optional[int] = Field(
-        description="ID of the grading instruction that was used to generate this feedback, or empty if no grading instruction was used"
-    )
-
-    class Config:
-        title = "Feedback"
-
-
-class AssessmentModel(BaseModel):
-    """Collection of feedbacks making up an assessment"""
-    
-    feedbacks: Sequence[FeedbackModel] = Field(description="Assessment feedbacks")
-
-    class Config:
-        title = "Assessment"
-
+from module_text_llm.basic_approach.prompt_generate_suggestions import AssessmentModel
 
 async def generate_suggestions(exercise: Exercise, submission: Submission, config: BasicApproachConfig, debug: bool) -> List[Feedback]:
     model = config.model.get_model()  # type: ignore[attr-defined]
-
     prompt_input = {
         "max_points": exercise.max_points,
         "bonus_points": exercise.bonus_points,
@@ -83,7 +59,8 @@ async def generate_suggestions(exercise: Exercise, submission: Submission, confi
         tags=[
             f"exercise-{exercise.id}",
             f"submission-{submission.id}",
-        ]
+        ],
+        use_function_calling=True
     )
 
     if debug:
diff --git a/modules/text/module_text_llm/module_text_llm/basic_approach/prompt_generate_suggestions.py b/modules/text/module_text_llm/module_text_llm/basic_approach/prompt_generate_suggestions.py
new file mode 100644
index 000000000..4b23137d5
--- /dev/null
+++ b/modules/text/module_text_llm/module_text_llm/basic_approach/prompt_generate_suggestions.py
@@ -0,0 +1,65 @@
+from pydantic import Field, BaseModel
+from typing import List, Optional
+from pydantic import BaseModel, Field
+
+system_message = """\
+You are an AI tutor for text assessment at a prestigious university.
+
+# Task
+Create graded feedback suggestions for a student\'s text submission that a human tutor would accept. \
+Meaning, the feedback you provide should be applicable to the submission with little to no modification.
+
+# Style
+1. Constructive, 2. Specific, 3. Balanced, 4. Clear and Concise, 5. Actionable, 6. Educational, 7. Contextual
+
+# Problem statement
+{problem_statement}
+
+# Example solution
+{example_solution}
+
+# Grading instructions
+{grading_instructions}
+Max points: {max_points}, bonus points: {bonus_points}\
+    
+Respond in json.
+"""
+
+human_message = """\
+Student\'s submission to grade (with sentence numbers <number>: <sentence>):
+
+Respond in json.
+
+\"\"\"
+{submission}
+\"\"\"\
+"""
+
+# Input Prompt
+class GenerateSuggestionsPrompt(BaseModel):
+    """\
+Features available: **{problem_statement}**, **{example_solution}**, **{grading_instructions}**, **{max_points}**, **{bonus_points}**, **{submission}**
+
+_Note: **{problem_statement}**, **{example_solution}**, or **{grading_instructions}** might be omitted if the input is too long._\
+"""
+    system_message: str = Field(default=system_message,
+                                description="Message for priming AI behavior and instructing it what to do.")
+    human_message: str = Field(default=human_message,
+                               description="Message from a human. The input on which the AI is supposed to act.")
+# Output Object
+class FeedbackModel(BaseModel):
+    title: str = Field(description="Very short title, i.e. feedback category or similar", example="Logic Error")
+    description: str = Field(description="Feedback description")
+    line_start: Optional[int] = Field(description="Referenced line number start, or empty if unreferenced")
+    line_end: Optional[int] = Field(description="Referenced line number end, or empty if unreferenced")
+    credits: float = Field(0.0, description="Number of points received/deducted")
+    grading_instruction_id: Optional[int] = Field(
+        description="ID of the grading instruction that was used to generate this feedback, or empty if no grading instruction was used"
+    )
+
+
+class AssessmentModel(BaseModel):
+    """Collection of feedbacks making up an assessment"""
+    
+    feedbacks: List[FeedbackModel] = Field(description="Assessment feedbacks")
+
diff --git a/modules/text/module_text_llm/module_text_llm/chain_of_thought_approach/__init__.py b/modules/text/module_text_llm/module_text_llm/chain_of_thought_approach/__init__.py
new file mode 100644
index 000000000..b63b770e1
--- /dev/null
+++ b/modules/text/module_text_llm/module_text_llm/chain_of_thought_approach/__init__.py
@@ -0,0 +1,15 @@
+from pydantic import BaseModel, Field
+from typing import Literal
+from llm_core.models import ModelConfigType, MiniModelConfig
+
+from module_text_llm.approach_config import ApproachConfig
+from module_text_llm.chain_of_thought_approach.prompt_generate_feedback import CoTGenerateSuggestionsPrompt
+from module_text_llm.chain_of_thought_approach.prompt_thinking import ThinkingPrompt
+
+class ChainOfThoughtConfig(ApproachConfig):
+    # Defaults to the cheaper mini 4o model
+    type: Literal['chain_of_thought'] = 'chain_of_thought'
+    model: ModelConfigType = Field(default=MiniModelConfig)  # type: ignore
+    thikning_prompt: ThinkingPrompt = Field(default=ThinkingPrompt())
+    generate_suggestions_prompt: CoTGenerateSuggestionsPrompt = Field(default=CoTGenerateSuggestionsPrompt())
+    
\ No newline at end of file
diff --git a/modules/text/module_text_llm/module_text_llm/chain_of_thought_approach/generate_suggestions.py b/modules/text/module_text_llm/module_text_llm/chain_of_thought_approach/generate_suggestions.py
new file mode 100644
index 000000000..6b86dc978
--- /dev/null
+++ b/modules/text/module_text_llm/module_text_llm/chain_of_thought_approach/generate_suggestions.py
@@ -0,0 +1,129 @@
+from typing import List, Optional
+from pydantic import BaseModel, Field
+
+from athena import emit_meta
+from athena.text import Exercise, Submission, Feedback
+from athena.logger import logger
+
+from module_text_llm.chain_of_thought_approach import ChainOfThoughtConfig
+
+from llm_core.utils.llm_utils import (
+    get_chat_prompt_with_formatting_instructions, 
+    check_prompt_length_and_omit_features_if_necessary, 
+    num_tokens_from_prompt,
+)
+from llm_core.utils.predict_and_parse import predict_and_parse
+
+from module_text_llm.helpers.utils import add_sentence_numbers, get_index_range_from_line_range, format_grading_instructions
+from module_text_llm.chain_of_thought_approach.prompt_thinking import InitialAssessmentModel
+from module_text_llm.chain_of_thought_approach.prompt_generate_feedback import AssessmentModel
+
+
+async def generate_suggestions(exercise: Exercise, submission: Submission, config: ChainOfThoughtConfig, debug: bool) -> List[Feedback]:
+    model = config.model.get_model()  # type: ignore[attr-defined]
+
+    prompt_input = {
+        "max_points": exercise.max_points,
+        "bonus_points": exercise.bonus_points,
+        "grading_instructions": format_grading_instructions(exercise.grading_instructions, exercise.grading_criteria),
+        "problem_statement": exercise.problem_statement or "No problem statement.",
+        "example_solution": exercise.example_solution,
+        "submission": add_sentence_numbers(submission.text)
+    }
+
+    chat_prompt = get_chat_prompt_with_formatting_instructions(
+        model=model, 
+        system_message=config.thikning_prompt.system_message, 
+        human_message=config.thikning_prompt.human_message, 
+        pydantic_object=InitialAssessmentModel
+    )
+    
+
+
+    # Check if the prompt is too long and omit features if necessary (in order of importance)
+    omittable_features = ["example_solution", "problem_statement", "grading_instructions"]
+    prompt_input, should_run = check_prompt_length_and_omit_features_if_necessary(
+        prompt=chat_prompt,
+        prompt_input= prompt_input,
+        max_input_tokens=config.max_input_tokens,
+        omittable_features=omittable_features,
+        debug=debug
+    )
+
+    # Skip if the prompt is too long
+    if not should_run:
+        logger.warning("Input too long. Skipping.")
+        if debug:
+            emit_meta("prompt", chat_prompt.format(**prompt_input))
+            emit_meta("error", f"Input too long {num_tokens_from_prompt(chat_prompt, prompt_input)} > {config.max_input_tokens}")
+        return []
+
+    initial_result = await predict_and_parse(
+        model=model, 
+        chat_prompt=chat_prompt, 
+        prompt_input=prompt_input, 
+        pydantic_object=InitialAssessmentModel,
+        tags=[
+            f"exercise-{exercise.id}",
+            f"submission-{submission.id}",
+        ],
+        use_function_calling=True
+    )
+
+    second_prompt_input = {
+        "answer" : initial_result.dict(),
+        "submission": add_sentence_numbers(submission.text)
+
+    }
+    
+    second_chat_prompt = get_chat_prompt_with_formatting_instructions(     
+        model=model, 
+        system_message=config.generate_suggestions_prompt.second_system_message, 
+        human_message=config.generate_suggestions_prompt.answer_message, 
+        pydantic_object=AssessmentModel)
+    
+    result = await predict_and_parse(
+    model=model, 
+    chat_prompt=second_chat_prompt, 
+    prompt_input=second_prompt_input, 
+    pydantic_object=AssessmentModel,
+    tags=[
+        f"exercise-{exercise.id}",
+        f"submission-{submission.id}",
+    ],
+        use_function_calling=True
+    )
+        
+    if debug:
+        emit_meta("generate_suggestions", {
+            "prompt": chat_prompt.format(**prompt_input),
+            "result": result.dict() if result is not None else None
+        })
+
+
+    if result is None:
+        return []
+
+    grading_instruction_ids = set(
+        grading_instruction.id 
+        for criterion in exercise.grading_criteria or [] 
+        for grading_instruction in criterion.structured_grading_instructions
+    )
+
+    feedbacks = []
+    for feedback in result.feedbacks:
+        index_start, index_end = get_index_range_from_line_range(feedback.line_start, feedback.line_end, submission.text)
+        grading_instruction_id = feedback.grading_instruction_id if feedback.grading_instruction_id in grading_instruction_ids else None
+        feedbacks.append(Feedback(
+            exercise_id=exercise.id,
+            submission_id=submission.id,
+            title=feedback.title,
+            description=feedback.description,
+            index_start=index_start,
+            index_end=index_end,
+            credits=feedback.credits,
+            structured_grading_instruction_id=grading_instruction_id,
+            meta={}
+        ))
+
+    return feedbacks
\ No newline at end of file
diff --git a/modules/text/module_text_llm/module_text_llm/chain_of_thought_approach/prompt_generate_feedback.py b/modules/text/module_text_llm/module_text_llm/chain_of_thought_approach/prompt_generate_feedback.py
new file mode 100644
index 000000000..9065ad67f
--- /dev/null
+++ b/modules/text/module_text_llm/module_text_llm/chain_of_thought_approach/prompt_generate_feedback.py
@@ -0,0 +1,55 @@
+from pydantic import BaseModel, Field
+from typing import List, Optional
+
+system_message = """
+         You gave the following feedback on the first iteration: {answer}
+         On this step you need to refine your feedback.
+         Make sure to follow the following steps to assess and improve your feedback:
+         It shuold follow the grading instructions and the sample solution, if it doesn't, consider improvements.
+         If you have your own additional improvements that are not present in the grading instructions, add them in a new feedback with 0 credits and no reference.
+         Remember that your response is directly seen by students and it should adress them directly.
+         For each feedback where the student has room for improvement, think about how the student could improve his solution.
+         Once you have thought how the student can improve the solution, formulate it in a way that guides the student towards the correct solution without revealing it directly.
+         Consider improvements to the feedback if any of this points is not satisfied.
+         
+         Respond in json
+
+         """
+
+human_message = """\
+Student\'s submission to grade (with sentence numbers <number>: <sentence>):
+\"\"\"
+{submission}
+\"\"\"\
+"""
+
+# Input Prompt
+
+class CoTGenerateSuggestionsPrompt(BaseModel):
+    """\
+Features cit available: **{problem_statement}**, **{example_solution}**, **{grading_instructions}**, **{max_points}**, **{bonus_points}**, **{submission}**
+
+_Note: **{problem_statement}**, **{example_solution}**, or **{grading_instructions}** might be omitted if the input is too long._\
+"""
+    second_system_message: str = Field(default=system_message,
+                                description="Message for priming AI behavior and instructing it what to do.")
+    answer_message: str = Field(default=human_message,
+                               description="Message from a human. The input on which the AI is supposed to act.")
+
+
+# Output Object
+
+class FeedbackModel(BaseModel):
+    title: str = Field(description="Very short title, i.e. feedback category or similar", example="Logic Error")
+    description: str = Field(description="Feedback description")
+    line_start: Optional[int] = Field(description="Referenced line number start, or empty if unreferenced")
+    line_end: Optional[int] = Field(description="Referenced line number end, or empty if unreferenced")
+    credits: float = Field(0.0, description="Number of points received/deducted")
+    grading_instruction_id: Optional[int] = Field(
+        description="ID of the grading instruction that was used to generate this feedback, or empty if no grading instruction was used"
+    )
+
+class AssessmentModel(BaseModel):
+    """Collection of feedbacks making up an assessment"""
+    
+    feedbacks: List[FeedbackModel] = Field(description="Assessment feedbacks")
\ No newline at end of file
diff --git a/modules/text/module_text_llm/module_text_llm/chain_of_thought_approach/prompt_thinking.py b/modules/text/module_text_llm/module_text_llm/chain_of_thought_approach/prompt_thinking.py
new file mode 100644
index 000000000..6f72b2b10
--- /dev/null
+++ b/modules/text/module_text_llm/module_text_llm/chain_of_thought_approach/prompt_thinking.py
@@ -0,0 +1,59 @@
+from pydantic import BaseModel, Field
+from typing import Optional, List
+
+system_message = """
+You are a grading assistant at a prestrigious university tasked with grading student submissions for text exercises.
+You goal is to be as helpful as possible to the student while providing constructive feedback without revealing the solution.
+In order to successfully complete this task, you must:
+1. Analyze the problem statement and the provided grading instructions to understand the requirements of the task.
+2. The problem solution is an example of a solution that meets the requirements of the task. Analyze the solution to understand the logic and the approach used to solve the problem, keeping in mind that the student solutions might diverge and still be correct.
+3. Analyze the student's submission in regards to the problem statement, so that you can create chunks of the solution that relate to a part of the problem statement.
+4. Use the information gathered from the previous steps to provide constructive feedback to the student, guiding them towards the correct solution without revealing it.
+5. If you have additional comments, create an unreferenced feedback.
+6. For each feedback make sure that the credits are given only on the basis of the grading instructions and soltuion, the minimal answer from a student that satisfies this should be given the credits. If you have notes or additional comments, make sure to include them in a new feedback with 0 credits and no reference.
+
+You are tasked with grading the following exercise, your response should take into account that you are directly responding to the student so you should adress the student:
+The maximal amount of points for this exercise is {max_points}.
+# Problem Statement
+{problem_statement}
+# Sample Solution
+{example_solution}
+# Grading Instructions
+{grading_instructions}
+
+Respond in json
+"""
+
+human_message = """\
+Student\'s submission to grade (with sentence numbers <number>: <sentence>):
+\"\"\"
+{submission}
+\"\"\"\
+"""
+
+# Input Prompt
+class ThinkingPrompt(BaseModel):
+    """\
+Features available: **{problem_statement}**, **{example_solution}**, **{grading_instructions}**, **{max_points}**, **{bonus_points}**, **{submission}**
+
+_Note: **{problem_statement}**, **{example_solution}**, or **{grading_instructions}** might be omitted if the input is too long._\
+"""
+    system_message: str = Field(default=system_message,
+                                description="Message for priming AI behavior and instructing it what to do.")
+    human_message: str = Field(default=human_message,
+                               description="Message from a human. The input on which the AI is supposed to act.")
+  
+# Output Object
+class InitialAssessment(BaseModel):
+    title: str = Field(description="Very short title, i.e. feedback category or similar", example="Logic Error")
+    description: str = Field(description="Feedback description")
+    line_start: Optional[int] = Field(description="Referenced line number start, or empty if unreferenced")
+    line_end: Optional[int] = Field(description="Referenced line number end, or empty if unreferenced")
+    credits: float = Field(0.0, description="Number of points received/deducted")
+    reasoning: str = Field(description="Reasoning why the feedback was given")
+    impprovment_suggestion: str = Field(description="Suggestion for improvement for the student")
+
+class InitialAssessmentModel(BaseModel):
+    """Collection of feedbacks making up an assessment"""
+    
+    feedbacks: List[InitialAssessment] = Field(description="Assessment feedbacks")
\ No newline at end of file
diff --git a/modules/text/module_text_llm/module_text_llm/config.py b/modules/text/module_text_llm/module_text_llm/config.py
index 0a61eeba6..c620e62e3 100644
--- a/modules/text/module_text_llm/module_text_llm/config.py
+++ b/modules/text/module_text_llm/module_text_llm/config.py
@@ -1,33 +1,16 @@
 from pydantic import BaseModel, Field
-
+from typing import Union
 from athena import config_schema_provider
-from llm_core.models import ModelConfigType, DefaultModelConfig
-from module_text_llm.prompts.generate_suggestions import (
-  system_message as generate_suggestions_system_message, 
-  human_message as generate_suggestions_human_message
-)
-
-
-class GenerateSuggestionsPrompt(BaseModel):
-    """\
-Features available: **{problem_statement}**, **{example_solution}**, **{grading_instructions}**, **{max_points}**, **{bonus_points}**, **{submission}**
 
-_Note: **{problem_statement}**, **{example_solution}**, or **{grading_instructions}** might be omitted if the input is too long._\
-"""
-    system_message: str = Field(default=generate_suggestions_system_message,
-                                description="Message for priming AI behavior and instructing it what to do.")
-    human_message: str = Field(default=generate_suggestions_human_message,
-                               description="Message from a human. The input on which the AI is supposed to act.")
-
-
-class BasicApproachConfig(BaseModel):
-    """This approach uses a LLM with a single prompt to generate feedback in a single step."""
-    max_input_tokens: int = Field(default=3000, description="Maximum number of tokens in the input prompt.")
-    model: ModelConfigType = Field(default=DefaultModelConfig()) # type: ignore
-    generate_suggestions_prompt: GenerateSuggestionsPrompt = Field(default=GenerateSuggestionsPrompt())
+from module_text_llm.chain_of_thought_approach import ChainOfThoughtConfig
+from module_text_llm.basic_approach import BasicApproachConfig
 
+ApproachConfigUnion = Union[BasicApproachConfig, ChainOfThoughtConfig]
 
 @config_schema_provider
 class Configuration(BaseModel):
     debug: bool = Field(default=False, description="Enable debug mode.")
-    approach: BasicApproachConfig = Field(default=BasicApproachConfig())
+    approach: ApproachConfigUnion = Field(default_factory=BasicApproachConfig)  # Default to BasicApproach
+
+    class Config:
+        smart_union = True 
diff --git a/modules/text/module_text_llm/module_text_llm/prompts/generate_suggestions.py b/modules/text/module_text_llm/module_text_llm/prompts/generate_suggestions.py
deleted file mode 100644
index 29dc465a2..000000000
--- a/modules/text/module_text_llm/module_text_llm/prompts/generate_suggestions.py
+++ /dev/null
@@ -1,27 +0,0 @@
-system_message = """\
-You are an AI tutor for text assessment at a prestigious university.
-
-# Task
-Create graded feedback suggestions for a student\'s text submission that a human tutor would accept. \
-Meaning, the feedback you provide should be applicable to the submission with little to no modification.
-
-# Style
-1. Constructive, 2. Specific, 3. Balanced, 4. Clear and Concise, 5. Actionable, 6. Educational, 7. Contextual
-
-# Problem statement
-{problem_statement}
-
-# Example solution
-{example_solution}
-
-# Grading instructions
-{grading_instructions}
-Max points: {max_points}, bonus points: {bonus_points}\
-"""
-
-human_message = """\
-Student\'s submission to grade (with sentence numbers <number>: <sentence>):
-\"\"\"
-{submission}
-\"\"\"\
-"""
\ No newline at end of file
diff --git a/modules/text/module_text_llm/poetry.lock b/modules/text/module_text_llm/poetry.lock
index 860a0f71e..236b739b3 100644
--- a/modules/text/module_text_llm/poetry.lock
+++ b/modules/text/module_text_llm/poetry.lock
@@ -138,13 +138,13 @@ frozenlist = ">=1.1.0"
 
 [[package]]
 name = "anyio"
-version = "4.6.0"
+version = "4.6.2.post1"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "anyio-4.6.0-py3-none-any.whl", hash = "sha256:c7d2e9d63e31599eeb636c8c5c03a7e108d73b345f064f1c19fdc87b79036a9a"},
-    {file = "anyio-4.6.0.tar.gz", hash = "sha256:137b4559cbb034c477165047febb6ff83f390fc3b20bf181c1fc0a728cb8beeb"},
+    {file = "anyio-4.6.2.post1-py3-none-any.whl", hash = "sha256:6d170c36fba3bdd840c73d3868c1e777e33676a69c3a72cf0a0d5d6d8009b61d"},
+    {file = "anyio-4.6.2.post1.tar.gz", hash = "sha256:4c8bc31ccdb51c7f7bd251f51c609e038d63e34219b44aa86e47576389880b4c"},
 ]
 
 [package.dependencies]
@@ -153,7 +153,7 @@ sniffio = ">=1.1"
 
 [package.extras]
 doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
-test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.21.0b1)"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21.0b1)"]
 trio = ["trio (>=0.26.1)"]
 
 [[package]]
@@ -460,88 +460,103 @@ flake8 = "*"
 
 [[package]]
 name = "frozenlist"
-version = "1.4.1"
+version = "1.5.0"
 description = "A list-like structure which implements collections.abc.MutableSequence"
 optional = false
 python-versions = ">=3.8"
 files = [
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-    {file = "frozenlist-1.4.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:29acab3f66f0f24674b7dc4736477bcd4bc3ad4b896f5f45379a67bce8b96868"},
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 [[package]]
@@ -960,13 +975,13 @@ langchain-core = ">=0.2.38,<0.3.0"
 
 [[package]]
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-version = "0.1.134"
+version = "0.1.137"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
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+tests = ["pytest", "simplejson"]
 
 [[package]]
 name = "mccabe"
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 [package.dependencies]
@@ -1174,6 +1194,7 @@ typing-extensions = ">=4.6.0"
 
 [package.extras]
 dmypy = ["psutil (>=4.0)"]
+faster-cache = ["orjson"]
 install-types = ["pip"]
 mypyc = ["setuptools (>=50)"]
 reports = ["lxml"]
@@ -1285,68 +1306,69 @@ datalib = ["numpy (>=1)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)"]
 
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 mssql = ["pyodbc"]
 mssql-pymssql = ["pymssql"]
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