diff --git a/backend/src/prompts/graph_extraction_prompt.py b/backend/src/prompts/graph_extraction_prompt.py
deleted file mode 100644
index c49a700..0000000
--- a/backend/src/prompts/graph_extraction_prompt.py
+++ /dev/null
@@ -1,81 +0,0 @@
-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT License.
-
-from typing import List
-
-from src.models import EntityTypeExample
-
-NEWLINE = "\n"
-
-
-def _get_example(entity_examples: EntityTypeExample, last: bool = False) -> str:
-    return f"""Entity_types: {entity_examples.entity_types}
-    Text:
-    {entity_examples.text}
-    ######################
-    Output:
-    {entity_examples.output + (f"{NEWLINE}######################{NEWLINE}" if not last else "")}"""
-
-
-def get_prompt(
-    entity_types: List[str], entity_examples: List[EntityTypeExample]
-) -> str:
-    if len(entity_types) < 1:
-        raise ValueError("entity_types cannot be empty")
-
-    if len(entity_examples) < 1:
-        raise ValueError("entity_examples cannot be empty")
-
-    # define the extraction goal
-    GRAPH_EXTRACTION_PROMPT = (
-        """
-    -Goal-
-    Given a text document that is potentially relevant to this activity and a list of entity types, identify all entities of those types from the text and all relationships among the identified entities.
-
-    -Steps-
-    1. Identify all entities. For each identified entity, extract the following information:
-    - entity_name: Name of the entity, capitalized
-    - entity_type: One of the following types: [{entity_types}]
-    - entity_description: Comprehensive description of the entity's attributes and activities
-    Format each entity as ("entity"{tuple_delimiter}<entity_name>{tuple_delimiter}<entity_type>{tuple_delimiter}<entity_description>
-
-    2. From the entities identified in step 1, identify all pairs of (source_entity, target_entity) that are *clearly related* to each other.
-    For each pair of related entities, extract the following information:
-    - source_entity: name of the source entity, as identified in step 1
-    - target_entity: name of the target entity, as identified in step 1
-    - relationship_description: explanation as to why you think the source entity and the target entity are related to each other
-    - relationship_strength: a numeric score indicating strength of the relationship between the source entity and target entity
-    Format each relationship as ("relationship"{tuple_delimiter}<source_entity>{tuple_delimiter}<target_entity>{tuple_delimiter}<relationship_description>{tuple_delimiter}<relationship_strength>)
-
-    3. Return output in English as a single list of all the entities and relationships identified in steps 1 and 2. Use **{record_delimiter}** as the list delimiter.
-
-    4. When finished, output {completion_delimiter}
-
-    ######################
-    -Examples-
-    ######################
-    {examples}
-    ######################
-    -Real Data-
-    ######################
-    Entity_types: {entity_types}
-    Text: {input_text}
-    ######################
-    Output:
-    """
-        # define the entity types to be extracted
-        .replace("{entity_types}", ",".join(entity_types))
-        # define the extraction examples
-        .replace(
-            "{examples}",
-            "".join(
-                [
-                    _get_example(entity_example, i == len(entity_examples))
-                    for i, entity_example in enumerate(entity_examples, start=1)
-                ]
-            ),
-        )
-        # eliminate leading white spaces (lstrip is not working)
-        .replace("    ", "")  # replace white 4 spaces with 1 space
-    )
-    return GRAPH_EXTRACTION_PROMPT