diff --git a/docs/examples/classification.md b/docs/examples/classification.md
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+++ b/docs/examples/classification.md
@@ -0,0 +1,117 @@
+# Example: Text Classification using OpenAI and Pydantic
+
+This tutorial showcases how to implement text classification tasks—specifically, single-label and multi-label classifications—using the OpenAI API, Python's **`enum`** module, and Pydantic models.
+
+!!! tips "Motivation"
+
+    Text classification is a common problem in many NLP applications, such as spam detection or support ticket categorization. The goal is to provide a systematic way to handle these cases using OpenAI's GPT models in combination with Python data structures.
+
+## Single-Label Classification
+
+### Defining the Structures
+
+For single-label classification, we first define an **`enum`** for possible labels and a Pydantic model for the output.
+
+```python
+import enum
+from pydantic import BaseModel
+
+class Labels(str, enum.Enum):
+    """Enumeration for single-label text classification."""
+    SPAM = "spam"
+    NOT_SPAM = "not_spam"
+
+class SinglePrediction(BaseModel):
+    """
+    Class for a single class label prediction.
+    """
+    class_label: Labels
+
+```
+
+### Classifying Text
+
+The function **`classify`** will perform the single-label classification.
+
+```python
+import openai
+
+def classify(data: str) -> SinglePrediction:
+    """Perform single-label classification on the input text."""
+    return openai.ChatCompletion.create(
+        model="gpt-3.5-turbo-0613",
+        response_model=SinglePrediction,
+        messages=[
+            {
+                "role": "user",
+                "content": f"Classify the following text: {data}",
+            },
+        ],
+    )  # type: ignore
+
+```
+
+### Testing and Evaluation
+
+Let's run an example to see if it correctly identifies a spam message.
+
+```python
+# Test single-label classification
+prediction = classify("Hello there I'm a Nigerian prince and I want to give you money")
+assert prediction.class_label == Labels.SPAM
+
+```
+
+## Multi-Label Classification
+
+### Defining the Structures
+
+For multi-label classification, we introduce a new enum class and a different Pydantic model to handle multiple labels.
+
+```python
+# Define Enum class for multiple labels
+class MultiLabels(str, enum.Enum):
+    TECH_ISSUE = "tech_issue"
+    BILLING = "billing"
+    GENERAL_QUERY = "general_query"
+
+# Define the multi-class prediction model
+class MultiClassPrediction(BaseModel):
+    """
+    Class for a multi-class label prediction.
+    """
+    class_labels: List[MultiLabels]
+
+```
+
+### Classifying Text
+
+The function **`multi_classify`** is responsible for multi-label classification.
+
+```python
+def multi_classify(data: str) -> MultiClassPrediction:
+    """Perform multi-label classification on the input text."""
+    return openai.ChatCompletion.create(
+        model="gpt-3.5-turbo-0613",
+        response_model=MultiClassPrediction,
+        messages=[
+            {
+                "role": "user",
+                "content": f"Classify the following support ticket: {data}",
+            },
+        ],
+    )  # type: ignore
+
+```
+
+### Testing and Evaluation
+
+Finally, we test the multi-label classification function using a sample support ticket.
+
+```python
+# Test multi-label classification
+ticket = "My account is locked and I can't access my billing info."
+prediction = multi_classify(ticket)
+assert MultiLabels.TECH_ISSUE in prediction.class_labels
+assert MultiLabels.BILLING in prediction.class_labels
+```
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diff --git a/docs/examples/index.md b/docs/examples/index.md
index 107ebe3..6c23462 100644
--- a/docs/examples/index.md
+++ b/docs/examples/index.md
@@ -4,6 +4,7 @@ Welcome to the examples page. Here you will find emails that highlight a range o
 
 ## Quick Links 
 
+- [Classifying Text](classification.md)
 - [Segmenting search requests into multiple search queries](search.md)
 - [One shot query planning](planning-tasks.md)
 - [Using recursive schema](recursive.md)
@@ -15,7 +16,9 @@ Welcome to the examples page. Here you will find emails that highlight a range o
 
 In this section, you will find examples demonstrating different aspects of our project's functionality.
 
-- [Segmented Search](search.md): Learn how to perform segmented search using a multi task definition using function calling 
+- [Classfying Text](classification.md): Doing single and multi class prediction using enums.
+
+- [Segmented Search](search.md): Learn how to perform segmented search using a multi task definition using function calling
 
 - [One shot Query Planning](planning-tasks.md): Explore how to plan and decompose a complex query into multiple subqueries in a single request.
 
diff --git a/examples/classification/multi_prediction.py b/examples/classification/multi_prediction.py
new file mode 100644
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+++ b/examples/classification/multi_prediction.py
@@ -0,0 +1,44 @@
+from typing import List
+import enum
+import openai
+from pydantic import BaseModel
+from instructor import patch
+
+patch()
+
+
+# Define new Enum class for multiple labels
+class MultiLabels(str, enum.Enum):
+    TECH_ISSUE = "tech_issue"
+    BILLING = "billing"
+    GENERAL_QUERY = "general_query"
+
+
+# Adjust the prediction model to accommodate a list of labels
+class MultiClassPrediction(BaseModel):
+    """
+    List of correct class labels for the given text (Multi Class)
+    """
+
+    class_labels: List[MultiLabels]
+
+
+# Modify the classify function
+def multi_classify(data: str) -> MultiClassPrediction:
+    return openai.ChatCompletion.create(
+        model="gpt-3.5-turbo-0613",
+        response_model=MultiClassPrediction,
+        messages=[
+            {
+                "role": "user",
+                "content": f"Classify the following support ticket: {data}",
+            },
+        ],
+    )  # type: ignore
+
+
+# Example using a support ticket
+ticket = "My account is locked and I can't access my billing info."
+prediction = multi_classify(ticket)
+assert MultiLabels.TECH_ISSUE in prediction.class_labels
+assert MultiLabels.BILLING in prediction.class_labels
diff --git a/examples/classification/simple_prediction.py b/examples/classification/simple_prediction.py
new file mode 100644
index 0000000..c31a6c7
--- /dev/null
+++ b/examples/classification/simple_prediction.py
@@ -0,0 +1,36 @@
+import enum
+import openai
+from pydantic import BaseModel
+from instructor import patch
+
+patch()
+
+
+class Labels(str, enum.Enum):
+    SPAM = "spam"
+    NOT_SPAM = "not_spam"
+
+
+class SinglePrediction(BaseModel):
+    """
+    Correct class label for the given text
+    """
+
+    class_label: Labels
+
+
+def classify(data: str) -> SinglePrediction:
+    return openai.ChatCompletion.create(
+        model="gpt-3.5-turbo-0613",
+        response_model=SinglePrediction,
+        messages=[
+            {
+                "role": "user",
+                "content": f"Classify the following text: {data}",
+            },
+        ],
+    )  # type: ignore
+
+
+prediction = classify("Hello there I'm a nigerian prince and I want to give you money")
+assert prediction.class_label == Labels.SPAM
diff --git a/examples/reference-citation/readme.md b/examples/reference-citation/readme.md
new file mode 100644
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+++ b/examples/reference-citation/readme.md
@@ -0,0 +1,20 @@
+# Legal Document Entity Resolution
+
+This example demonstrates how to use an entity resolution system to extract and resolve entities from a legal document. The system leverages OpenAI's GPT-4 language model to achieve this task. The primary purpose of this example is to showcase the capabilities of the entity resolution system in a simple and illustrative manner.
+
+## Overview
+The entity resolution system processes a given legal document and identifies key entities such as parties, dates, terms, and clauses. It then resolves relevant information to provide a structured output. This example uses a Python script to interact with the system and demonstrates the process with a sample legal contract.
+
+## How to Use
+
+* **Input Document:** Provide the legal document you want to analyze. The document should include relevant legal terms, dates, parties' names, and other pertinent information.
+
+* **Entity Extraction:** The system employs the GPT-4 model to extract entities from the input document.
+
+* **Entity Resolution:** Extracted entities are resolved to their absolute values when applicable. For instance, relative date phrases are converted to specific dates.
+
+* **Dependency Handling:** The system identifies dependencies between entities. If one entity's resolution depends on another's, it ensures proper order of resolution.
+
+## Limitations
+
+The context window is the biggest limitation of the size of document, but I imagine a system where you stream chunks of the document into a model, that acculimates the entities in some state and formats a simple version back into the prompt (id, name, absolute_resolved_value) and the output emits only 'new' entities, thinking of it as a acculilating the object.
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diff --git a/mkdocs.yml b/mkdocs.yml
index b94b72a..55fbf5e 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -49,6 +49,7 @@ nav:
       - Philosophy: 'philosophy.md'
   - Use Cases:
       - 'Overview': 'examples/index.md'
+      - 'Classification': 'examples/classification.md'
       - 'Segmented Search': 'examples/search.md'
       - 'One shot Query Planning': 'examples/planning-tasks.md'
       - 'Recursive Schemas': 'examples/recursive.md'