diff --git a/.gitignore b/.gitignore
index e7058c9e..4cef1977 100644
--- a/.gitignore
+++ b/.gitignore
@@ -59,6 +59,7 @@ docs/_build/
 
 # Jupyter Notebook
 .ipynb_checkpoints
+*.ipynb
 notebooks/
 
 # IPython
diff --git a/bertopic/representation/__init__.py b/bertopic/representation/__init__.py
index f1502982..3df028fe 100644
--- a/bertopic/representation/__init__.py
+++ b/bertopic/representation/__init__.py
@@ -33,6 +33,13 @@
     msg = "`pip install openai` \n\n"
     OpenAI = NotInstalled("OpenAI", "openai", custom_msg=msg)
 
+# Ollama Generator
+try:
+    from bertopic.representation._ollama import Ollama
+except ModuleNotFoundError:
+    msg = "`pip install ollama` \n\n"
+    Ollama = NotInstalled("Ollama", "ollama", custom_msg=msg)
+
 # LiteLLM Generator
 try:
     from bertopic.representation._litellm import LiteLLM
@@ -68,6 +75,7 @@
     "LiteLLM",
     "LlamaCPP",
     "MaximalMarginalRelevance",
+    "Ollama",
     "OpenAI",
     "PartOfSpeech",
     "TextGeneration",
diff --git a/bertopic/representation/_base.py b/bertopic/representation/_base.py
index 63feeda9..54be3d2e 100644
--- a/bertopic/representation/_base.py
+++ b/bertopic/representation/_base.py
@@ -1,7 +1,10 @@
 import pandas as pd
 from scipy.sparse import csr_matrix
 from sklearn.base import BaseEstimator
-from typing import Mapping, List, Tuple
+from typing import Mapping, List, Tuple, Union, Callable
+
+from bertopic.representation._prompts import DEFAULT_CHAT_PROMPT
+from bertopic.representation._utils import truncate_document, validate_truncate_document_parameters
 
 
 class BaseRepresentation(BaseEstimator):
@@ -38,3 +41,113 @@ def extract_topics(
                     model is used.
         """
         return topic_model.topic_representations_
+
+
+class LLMRepresentation(BaseRepresentation):
+    """Base class for LLM-based representation models."""
+
+    def __init__(
+        self,
+        prompt: str | None = None,
+        nr_docs: int = 4,
+        diversity: float | None = None,
+        doc_length: int | None = None,
+        tokenizer: Union[str, Callable] | None = None,
+    ):
+        """Generate a representation model that leverages LLMs.
+
+        Arguments:
+            prompt: The prompt to be used in the model. If no prompt is given,
+                    `bertopic.representation._prompts.DEFAULT_CHAT_PROMPT` is used instead.
+                    NOTE: Use `"[KEYWORDS]"` and `"[DOCUMENTS]"` in the prompt
+                    to decide where the keywords and documents need to be
+                    inserted.
+            nr_docs: The number of documents to pass to Ollama if a prompt
+                    with the `["DOCUMENTS"]` tag is used.
+            diversity: The diversity of documents to pass to Ollama.
+                    Accepts values between 0 and 1. A higher
+                    values results in passing more diverse documents
+                    whereas lower values passes more similar documents.
+            doc_length: The maximum length of each document. If a document is longer,
+                        it will be truncated. If None, the entire document is passed.
+            tokenizer: The tokenizer used to calculate to split the document into segments
+                    used to count the length of a document.
+                        * If tokenizer is 'char', then the document is split up
+                            into characters which are counted to adhere to `doc_length`
+                        * If tokenizer is 'whitespace', the document is split up
+                            into words separated by whitespaces. These words are counted
+                            and truncated depending on `doc_length`
+                        * If tokenizer is 'vectorizer', then the internal CountVectorizer
+                            is used to tokenize the document. These tokens are counted
+                            and truncated depending on `doc_length`
+                        * If tokenizer is a callable, then that callable is used to tokenize
+                            the document. These tokens are counted and truncated depending
+                            on `doc_length`
+        """
+        self.prompt = prompt
+
+        # Representative document extraction parameters
+        self.nr_docs = nr_docs
+        self.diversity = diversity
+
+        # Document truncation
+        self.doc_length = doc_length
+        self.tokenizer = tokenizer
+        validate_truncate_document_parameters(self.tokenizer, self.doc_length)
+
+        # Store prompts for inspection
+        self.prompts_ = []
+
+    def _create_prompt(
+        self, docs: list[str], topic: int, topics: Mapping[str, List[Tuple[str, float]]], topic_model
+    ) -> str:
+        """Create prompt for LLM by either using the default prompt or replacing custom tags.
+        Specifically, [KEYWORDS] and [DOCUMENTS] can be used in custom prompts to insert the topic's keywords and most representative documents.
+
+        Arguments:
+            docs: The most representative documents for a given topic.
+            topic: The topic for which to create the prompt.
+            topics: A dictionary with topic (key) and tuple of word and
+                    weight (value) as calculated by c-TF-IDF.
+            topic_model: The BERTopic model that is fitted until topic
+                         representations are calculated.
+
+        Returns:
+            prompt: The created prompt.
+        """
+        keywords = next(zip(*topics[topic]))
+
+        # Use the Default Chat Prompt
+        if self.prompt == DEFAULT_CHAT_PROMPT:
+            prompt = self.prompt.replace("[KEYWORDS]", ", ".join(keywords))
+            prompt = self._replace_documents(prompt, docs, topic_model)
+
+        # Use a custom prompt that leverages keywords, documents or both using
+        # custom tags, namely [KEYWORDS] and [DOCUMENTS] respectively
+        else:
+            prompt = self.prompt
+            if "[KEYWORDS]" in prompt:
+                prompt = prompt.replace("[KEYWORDS]", ", ".join(keywords))
+            if "[DOCUMENTS]" in prompt:
+                prompt = self._replace_documents(prompt, docs, topic_model)
+
+        return prompt
+
+    def _replace_documents(self, prompt: str, docs: list[str], topic_model) -> str:
+        """Replace [DOCUMENTS] tag in prompt with actual documents.
+
+        Arguments:
+            prompt: The prompt containing the [DOCUMENTS] tag.
+            docs: The most representative documents for a given topic.
+            topic_model: The BERTopic model that is fitted until topic
+                         representations are calculated.
+
+        Returns:
+            The prompt with the [DOCUMENTS] tag replaced by actual documents.
+        """
+        # Truncate documents if needed
+        truncated_docs = [truncate_document(topic_model, self.doc_length, self.tokenizer, doc) for doc in docs]
+
+        # Replace tag with documents
+        formatted_docs = "\n".join([f"- {doc}" for doc in truncated_docs])
+        return prompt.replace("[DOCUMENTS]", formatted_docs)
diff --git a/bertopic/representation/_cohere.py b/bertopic/representation/_cohere.py
index 9ccd5ae5..9d6c056b 100644
--- a/bertopic/representation/_cohere.py
+++ b/bertopic/representation/_cohere.py
@@ -3,42 +3,11 @@
 from tqdm import tqdm
 from scipy.sparse import csr_matrix
 from typing import Mapping, List, Tuple, Union, Callable
-from bertopic.representation._base import BaseRepresentation
-from bertopic.representation._utils import truncate_document, validate_truncate_document_parameters
-
-
-DEFAULT_PROMPT = """
-This is a list of texts where each collection of texts describe a topic. After each collection of texts, the name of the topic they represent is mentioned as a short-highly-descriptive title
----
-Topic:
-Sample texts from this topic:
-- Traditional diets in most cultures were primarily plant-based with a little meat on top, but with the rise of industrial style meat production and factory farming, meat has become a staple food.
-- Meat, but especially beef, is the word food in terms of emissions.
-- Eating meat doesn't make you a bad person, not eating meat doesn't make you a good one.
-
-Keywords: meat beef eat eating emissions steak food health processed chicken
-Topic name: Environmental impacts of eating meat
----
-Topic:
-Sample texts from this topic:
-- I have ordered the product weeks ago but it still has not arrived!
-- The website mentions that it only takes a couple of days to deliver but I still have not received mine.
-- I got a message stating that I received the monitor but that is not true!
-- It took a month longer to deliver than was advised...
-
-Keywords: deliver weeks product shipping long delivery received arrived arrive week
-Topic name: Shipping and delivery issues
----
-Topic:
-Sample texts from this topic:
-[DOCUMENTS]
-Keywords: [KEYWORDS]
-Topic name:"""
-
-DEFAULT_SYSTEM_PROMPT = "You are an assistant that extracts high-level topics from texts."
-
-
-class Cohere(BaseRepresentation):
+from bertopic.representation._base import LLMRepresentation
+from bertopic.representation._prompts import DEFAULT_SYSTEM_PROMPT, DEFAULT_CHAT_PROMPT
+
+
+class Cohere(LLMRepresentation):
     """Use the Cohere API to generate topic labels based on their
     generative model.
 
@@ -49,12 +18,12 @@ class Cohere(BaseRepresentation):
         client: A `cohere.Client`
         model: Model to use within Cohere, defaults to `"xlarge"`.
         prompt: The prompt to be used in the model. If no prompt is given,
-                `self.default_prompt_` is used instead.
+                `bertopic.representation._prompts.DEFAULT_CHAT_PROMPT` is used instead.
                 NOTE: Use `"[KEYWORDS]"` and `"[DOCUMENTS]"` in the prompt
                 to decide where the keywords and documents need to be
                 inserted.
         system_prompt: The system prompt to be used in the model. If no system prompt is given,
-                       `self.default_system_prompt_` is used instead.
+                       `bertopic.representation._prompts.DEFAULT_SYSTEM_PROMPT` is used instead.
         delay_in_seconds: The delay in seconds between consecutive prompts
                                 in order to prevent RateLimitErrors.
         nr_docs: The number of documents to pass to OpenAI if a prompt
@@ -120,20 +89,19 @@ def __init__(
         doc_length: int | None = None,
         tokenizer: Union[str, Callable] | None = None,
     ):
+        super().__init__(
+            prompt=prompt if prompt is not None else DEFAULT_CHAT_PROMPT,
+            nr_docs=nr_docs,
+            diversity=diversity,
+            doc_length=doc_length,
+            tokenizer=tokenizer,
+        )
+
+        # Cohere specific parameters
         self.client = client
         self.model = model
-        self.prompt = prompt if prompt is not None else DEFAULT_PROMPT
         self.system_prompt = system_prompt if system_prompt is not None else DEFAULT_SYSTEM_PROMPT
-        self.default_prompt_ = DEFAULT_PROMPT
-        self.default_system_prompt_ = DEFAULT_SYSTEM_PROMPT
         self.delay_in_seconds = delay_in_seconds
-        self.nr_docs = nr_docs
-        self.diversity = diversity
-        self.doc_length = doc_length
-        self.tokenizer = tokenizer
-        validate_truncate_document_parameters(self.tokenizer, self.doc_length)
-
-        self.prompts_ = []
 
     def extract_topics(
         self,
@@ -161,8 +129,7 @@ def extract_topics(
         # Generate using Cohere's Language Model
         updated_topics = {}
         for topic, docs in tqdm(repr_docs_mappings.items(), disable=not topic_model.verbose):
-            truncated_docs = [truncate_document(topic_model, self.doc_length, self.tokenizer, doc) for doc in docs]
-            prompt = self._create_prompt(truncated_docs, topic, topics)
+            prompt = self._create_prompt(docs=docs, topic=topic, topics=topics, topic_model=topic_model)
             self.prompts_.append(prompt)
 
             # Delay
@@ -180,30 +147,3 @@ def extract_topics(
             updated_topics[topic] = [(label, 1)] + [("", 0) for _ in range(9)]
 
         return updated_topics
-
-    def _create_prompt(self, docs, topic, topics):
-        keywords = next(zip(*topics[topic]))
-
-        # Use the Default Chat Prompt
-        if self.prompt == DEFAULT_PROMPT:
-            prompt = self.prompt.replace("[KEYWORDS]", ", ".join(keywords))
-            prompt = self._replace_documents(prompt, docs)
-
-        # Use a custom prompt that leverages keywords, documents or both using
-        # custom tags, namely [KEYWORDS] and [DOCUMENTS] respectively
-        else:
-            prompt = self.prompt
-            if "[KEYWORDS]" in prompt:
-                prompt = prompt.replace("[KEYWORDS]", ", ".join(keywords))
-            if "[DOCUMENTS]" in prompt:
-                prompt = self._replace_documents(prompt, docs)
-
-        return prompt
-
-    @staticmethod
-    def _replace_documents(prompt, docs):
-        to_replace = ""
-        for doc in docs:
-            to_replace += f"- {doc}\n"
-        prompt = prompt.replace("[DOCUMENTS]", to_replace)
-        return prompt
diff --git a/bertopic/representation/_langchain.py b/bertopic/representation/_langchain.py
index b80d9b77..b5560e31 100644
--- a/bertopic/representation/_langchain.py
+++ b/bertopic/representation/_langchain.py
@@ -3,13 +3,12 @@
 from scipy.sparse import csr_matrix
 from typing import Callable, Mapping, List, Tuple, Union
 
-from bertopic.representation._base import BaseRepresentation
-from bertopic.representation._utils import truncate_document, validate_truncate_document_parameters
+from bertopic.representation._base import LLMRepresentation
+from bertopic.representation._utils import truncate_document
+from bertopic.representation._prompts import DEFAULT_COMPLETION_PROMPT
 
-DEFAULT_PROMPT = "What are these documents about? Please give a single label."
 
-
-class LangChain(BaseRepresentation):
+class LangChain(LLMRepresentation):
     """Using chains in langchain to generate topic labels.
 
     The classic example uses `langchain.chains.question_answering.load_qa_chain`.
@@ -22,7 +21,7 @@ class LangChain(BaseRepresentation):
                Input keys must be `input_documents` and `question`.
                Output key must be `output_text`.
         prompt: The prompt to be used in the model. If no prompt is given,
-                `self.default_prompt_` is used instead.
+                `bertopic.representation._prompts.DEFAULT_COMPLETION_PROMPT` is used instead.
                  NOTE: Use `"[KEYWORDS]"` in the prompt
                  to decide where the keywords need to be
                  inserted. Keywords won't be included unless
@@ -140,15 +139,17 @@ def __init__(
         tokenizer: Union[str, Callable] | None = None,
         chain_config=None,
     ):
+        super().__init__(
+            prompt=prompt if prompt is not None else DEFAULT_COMPLETION_PROMPT,
+            nr_docs=nr_docs,
+            diversity=diversity,
+            doc_length=doc_length,
+            tokenizer=tokenizer,
+        )
+
+        # LangChain specific parameters
         self.chain = chain
-        self.prompt = prompt if prompt is not None else DEFAULT_PROMPT
-        self.default_prompt_ = DEFAULT_PROMPT
         self.chain_config = chain_config
-        self.nr_docs = nr_docs
-        self.diversity = diversity
-        self.doc_length = doc_length
-        self.tokenizer = tokenizer
-        validate_truncate_document_parameters(self.tokenizer, self.doc_length)
 
     def extract_topics(
         self,
diff --git a/bertopic/representation/_litellm.py b/bertopic/representation/_litellm.py
index c9f69ae8..9a3ed66c 100644
--- a/bertopic/representation/_litellm.py
+++ b/bertopic/representation/_litellm.py
@@ -1,23 +1,18 @@
 import time
 from litellm import completion
 import pandas as pd
+from typing import Union, Callable
 from tqdm import tqdm
 from scipy.sparse import csr_matrix
 from typing import Mapping, List, Tuple, Any
-from bertopic.representation._base import BaseRepresentation
-from bertopic.representation._utils import retry_with_exponential_backoff
+from bertopic.representation._base import LLMRepresentation
+from bertopic.representation._utils import (
+    retry_with_exponential_backoff,
+)
+from bertopic.representation._prompts import DEFAULT_CHAT_PROMPT
 
 
-DEFAULT_PROMPT = """
-I have a topic that contains the following documents:
-[DOCUMENTS]
-The topic is described by the following keywords: [KEYWORDS]
-Based on the information above, extract a short topic label in the following format:
-topic: <topic label>
-"""
-
-
-class LiteLLM(BaseRepresentation):
+class LiteLLM(LLMRepresentation):
     """Using the LiteLLM API to generate topic labels.
 
     For an overview of models see:
@@ -27,7 +22,7 @@ class LiteLLM(BaseRepresentation):
         model: Model to use. Defaults to OpenAI's "gpt-3.5-turbo".
         generator_kwargs: Kwargs passed to `litellm.completion`.
         prompt: The prompt to be used in the model. If no prompt is given,
-                `self.default_prompt_` is used instead.
+                `bertopic.representation._prompts.DEFAULT_CHAT_PROMPT` is used instead.
                 NOTE: Use `"[KEYWORDS]"` and `"[DOCUMENTS]"` in the prompt
                 to decide where the keywords and documents need to be
                 inserted.
@@ -44,6 +39,21 @@ class LiteLLM(BaseRepresentation):
                    Accepts values between 0 and 1. A higher
                    values results in passing more diverse documents
                    whereas lower values passes more similar documents.
+        doc_length: The maximum length of each document. If a document is longer,
+                    it will be truncated. If None, the entire document is passed.
+        tokenizer: The tokenizer used to calculate to split the document into segments
+                   used to count the length of a document.
+                       * If tokenizer is 'char', then the document is split up
+                         into characters which are counted to adhere to `doc_length`
+                       * If tokenizer is 'whitespace', the document is split up
+                         into words separated by whitespaces. These words are counted
+                         and truncated depending on `doc_length`
+                       * If tokenizer is 'vectorizer', then the internal CountVectorizer
+                         is used to tokenize the document. These tokens are counted
+                         and truncated depending on `doc_length`
+                       * If tokenizer is a callable, then that callable is used to tokenize
+                         the document. These tokens are counted and truncated depending
+                         on `doc_length`
 
     Usage:
 
@@ -85,20 +95,22 @@ def __init__(
         exponential_backoff: bool = False,
         nr_docs: int = 4,
         diversity: float | None = None,
+        doc_length: int | None = None,
+        tokenizer: Union[str, Callable] | None = None,
     ):
+        super().__init__(
+            prompt=prompt if prompt is not None else DEFAULT_CHAT_PROMPT,
+            nr_docs=nr_docs,
+            diversity=diversity,
+            doc_length=doc_length,
+            tokenizer=tokenizer,
+        )
+
+        # LiteLLM specific parameters
         self.model = model
-        self.prompt = prompt if prompt else DEFAULT_PROMPT
-        self.default_prompt_ = DEFAULT_PROMPT
         self.delay_in_seconds = delay_in_seconds
         self.exponential_backoff = exponential_backoff
-        self.nr_docs = nr_docs
-        self.diversity = diversity
-
         self.generator_kwargs = generator_kwargs
-        if self.generator_kwargs.get("model"):
-            self.model = generator_kwargs.get("model")
-        if self.generator_kwargs.get("prompt"):
-            del self.generator_kwargs["prompt"]
 
     def extract_topics(
         self, topic_model, documents: pd.DataFrame, c_tf_idf: csr_matrix, topics: Mapping[str, List[Tuple[str, float]]]
@@ -122,7 +134,8 @@ def extract_topics(
         # Generate using a (Large) Language Model
         updated_topics = {}
         for topic, docs in tqdm(repr_docs_mappings.items(), disable=not topic_model.verbose):
-            prompt = self._create_prompt(docs, topic, topics)
+            prompt = self._create_prompt(docs=docs, topic=topic, topics=topics, topic_model=topic_model)
+            self.prompts_.append(prompt)
 
             # Delay
             if self.delay_in_seconds:
@@ -133,43 +146,14 @@ def extract_topics(
                 {"role": "user", "content": prompt},
             ]
             kwargs = {"model": self.model, "messages": messages, **self.generator_kwargs}
-            if self.exponential_backoff:
-                response = chat_completions_with_backoff(**kwargs)
-            else:
-                response = completion(**kwargs)
-            label = response["choices"][0]["message"]["content"].strip().replace("topic: ", "")
 
+            # Generate response
+            response = chat_completions_with_backoff(**kwargs) if self.exponential_backoff else completion(**kwargs)
+            label = response["choices"][0]["message"]["content"].strip().replace("topic: ", "")
             updated_topics[topic] = [(label, 1)]
 
         return updated_topics
 
-    def _create_prompt(self, docs, topic, topics):
-        keywords = next(zip(*topics[topic]))
-
-        # Use the Default Chat Prompt
-        if self.prompt == DEFAULT_PROMPT:
-            prompt = self.prompt.replace("[KEYWORDS]", " ".join(keywords))
-            prompt = self._replace_documents(prompt, docs)
-
-        # Use a custom prompt that leverages keywords, documents or both using
-        # custom tags, namely [KEYWORDS] and [DOCUMENTS] respectively
-        else:
-            prompt = self.prompt
-            if "[KEYWORDS]" in prompt:
-                prompt = prompt.replace("[KEYWORDS]", " ".join(keywords))
-            if "[DOCUMENTS]" in prompt:
-                prompt = self._replace_documents(prompt, docs)
-
-        return prompt
-
-    @staticmethod
-    def _replace_documents(prompt, docs):
-        to_replace = ""
-        for doc in docs:
-            to_replace += f"- {doc[:255]}\n"
-        prompt = prompt.replace("[DOCUMENTS]", to_replace)
-        return prompt
-
 
 def chat_completions_with_backoff(**kwargs):
     return retry_with_exponential_backoff(
diff --git a/bertopic/representation/_llamacpp.py b/bertopic/representation/_llamacpp.py
index 730a5f55..8b0b6747 100644
--- a/bertopic/representation/_llamacpp.py
+++ b/bertopic/representation/_llamacpp.py
@@ -3,54 +3,23 @@
 from scipy.sparse import csr_matrix
 from llama_cpp import Llama
 from typing import Mapping, List, Tuple, Any, Union, Callable
-from bertopic.representation._base import BaseRepresentation
-from bertopic.representation._utils import truncate_document, validate_truncate_document_parameters
-
-
-DEFAULT_PROMPT = """
-This is a list of texts where each collection of texts describe a topic. After each collection of texts, the name of the topic they represent is mentioned as a short-highly-descriptive title
----
-Topic:
-Sample texts from this topic:
-- Traditional diets in most cultures were primarily plant-based with a little meat on top, but with the rise of industrial style meat production and factory farming, meat has become a staple food.
-- Meat, but especially beef, is the word food in terms of emissions.
-- Eating meat doesn't make you a bad person, not eating meat doesn't make you a good one.
-
-Keywords: meat beef eat eating emissions steak food health processed chicken
-Topic name: Environmental impacts of eating meat
----
-Topic:
-Sample texts from this topic:
-- I have ordered the product weeks ago but it still has not arrived!
-- The website mentions that it only takes a couple of days to deliver but I still have not received mine.
-- I got a message stating that I received the monitor but that is not true!
-- It took a month longer to deliver than was advised...
-
-Keywords: deliver weeks product shipping long delivery received arrived arrive week
-Topic name: Shipping and delivery issues
----
-Topic:
-Sample texts from this topic:
-[DOCUMENTS]
-Keywords: [KEYWORDS]
-Topic name:"""
-
-DEFAULT_SYSTEM_PROMPT = "You are an assistant that extracts high-level topics from texts."
-
-
-class LlamaCPP(BaseRepresentation):
+from bertopic.representation._base import LLMRepresentation
+from bertopic.representation._prompts import DEFAULT_SYSTEM_PROMPT, DEFAULT_CHAT_PROMPT
+
+
+class LlamaCPP(LLMRepresentation):
     """A llama.cpp implementation to use as a representation model.
 
     Arguments:
         model: Either a string pointing towards a local LLM or a
                 `llama_cpp.Llama` object.
         prompt: The prompt to be used in the model. If no prompt is given,
-                `self.default_prompt_` is used instead.
+                `bertopic.representation._prompts.DEFAULT_CHAT_PROMPT` is used instead.
                 NOTE: Use `"[KEYWORDS]"` and `"[DOCUMENTS]"` in the prompt
                 to decide where the keywords and documents need to be
                 inserted.
         system_prompt: The system prompt to be used in the model. If no system prompt is given,
-                       `self.default_system_prompt_` is used instead.
+                       `bertopic.representation._prompts.DEFAULT_SYSTEM_PROMPT` is used instead.
         pipeline_kwargs: Kwargs that you can pass to the `llama_cpp.Llama`
                          when it is called such as `max_tokens` to be generated.
         nr_docs: The number of documents to pass to OpenAI if a prompt
@@ -123,6 +92,15 @@ def __init__(
         doc_length: int | None = None,
         tokenizer: Union[str, Callable] | None = None,
     ):
+        super().__init__(
+            prompt=prompt if prompt is not None else DEFAULT_CHAT_PROMPT,
+            nr_docs=nr_docs,
+            diversity=diversity,
+            doc_length=doc_length,
+            tokenizer=tokenizer,
+        )
+
+        # Llama.cpp specific parameters
         if isinstance(model, str):
             self.model = Llama(model_path=model, n_gpu_layers=-1, stop="\n", chat_format="ChatML")
         elif isinstance(model, Llama):
@@ -133,18 +111,8 @@ def __init__(
                 "pass is either a string referring to a"
                 "local LLM or a ` llama_cpp.Llama` object."
             )
-        self.prompt = prompt if prompt is not None else DEFAULT_PROMPT
         self.system_prompt = system_prompt if system_prompt is not None else DEFAULT_SYSTEM_PROMPT
-        self.default_prompt_ = DEFAULT_PROMPT
-        self.default_system_prompt_ = DEFAULT_SYSTEM_PROMPT
         self.pipeline_kwargs = pipeline_kwargs
-        self.nr_docs = nr_docs
-        self.diversity = diversity
-        self.doc_length = doc_length
-        self.tokenizer = tokenizer
-        validate_truncate_document_parameters(self.tokenizer, self.doc_length)
-
-        self.prompts_ = []
 
     def extract_topics(
         self,
@@ -172,8 +140,7 @@ def extract_topics(
         updated_topics = {}
         for topic, docs in tqdm(repr_docs_mappings.items(), disable=not topic_model.verbose):
             # Prepare prompt
-            truncated_docs = [truncate_document(topic_model, self.doc_length, self.tokenizer, doc) for doc in docs]
-            prompt = self._create_prompt(truncated_docs, topic, topics)
+            prompt = self._create_prompt(docs=docs, topic=topic, topics=topics, topic_model=topic_model)
             self.prompts_.append(prompt)
 
             # Extract result from generator and use that as label
@@ -186,30 +153,3 @@ def extract_topics(
             updated_topics[topic] = [(label, 1)] + [("", 0) for _ in range(9)]
 
         return updated_topics
-
-    def _create_prompt(self, docs, topic, topics):
-        keywords = next(zip(*topics[topic]))
-
-        # Use the Default Chat Prompt
-        if self.prompt == DEFAULT_PROMPT:
-            prompt = self.prompt.replace("[KEYWORDS]", ", ".join(keywords))
-            prompt = self._replace_documents(prompt, docs)
-
-        # Use a custom prompt that leverages keywords, documents or both using
-        # custom tags, namely [KEYWORDS] and [DOCUMENTS] respectively
-        else:
-            prompt = self.prompt
-            if "[KEYWORDS]" in prompt:
-                prompt = prompt.replace("[KEYWORDS]", ", ".join(keywords))
-            if "[DOCUMENTS]" in prompt:
-                prompt = self._replace_documents(prompt, docs)
-
-        return prompt
-
-    @staticmethod
-    def _replace_documents(prompt, docs):
-        to_replace = ""
-        for doc in docs:
-            to_replace += f"- {doc}\n"
-        prompt = prompt.replace("[DOCUMENTS]", to_replace)
-        return prompt
diff --git a/bertopic/representation/_ollama.py b/bertopic/representation/_ollama.py
new file mode 100644
index 00000000..f7fc4803
--- /dev/null
+++ b/bertopic/representation/_ollama.py
@@ -0,0 +1,154 @@
+import json
+import pandas as pd
+from ollama import chat
+from ollama import ChatResponse
+from tqdm import tqdm
+from scipy.sparse import csr_matrix
+from typing import Mapping, List, Tuple, Any, Union, Callable
+from bertopic.representation._base import LLMRepresentation
+from json.decoder import JSONDecodeError
+from bertopic.representation._prompts import DEFAULT_SYSTEM_PROMPT, DEFAULT_CHAT_PROMPT, DEFAULT_JSON_SCHEMA
+
+
+class Ollama(LLMRepresentation):
+    r"""Using the Ollama API to generate topic labels based on a local LLM.
+
+    Arguments:
+        model: Model to use within Ollama.
+        generator_kwargs: Kwargs passed to `ollama.chat` for fine-tuning the output.
+        prompt: The prompt to be used in the model. If no prompt is given,
+                `bertopic.representation._prompts.DEFAULT_CHAT_PROMPT` is used instead.
+                NOTE: Use `"[KEYWORDS]"` and `"[DOCUMENTS]"` in the prompt
+                to decide where the keywords and documents need to be
+                inserted.
+        system_prompt: The system prompt to be used in the model. If no system prompt is given,
+                       `bertopic.representation._prompts.DEFAULT_SYSTEM_PROMPT` is used instead.
+        json_schema: A dictionary representing the JSON schema to enforce structured output.
+                     If set to True, a default schema will be used (`bertopic.representation._prompts.DEFAULT_JSON_SCHEMA`).
+        nr_docs: The number of documents to pass to Ollama if a prompt
+                 with the `["DOCUMENTS"]` tag is used.
+        diversity: The diversity of documents to pass to Ollama.
+                   Accepts values between 0 and 1. A higher
+                   values results in passing more diverse documents
+                   whereas lower values passes more similar documents.
+        doc_length: The maximum length of each document. If a document is longer,
+                    it will be truncated. If None, the entire document is passed.
+        tokenizer: The tokenizer used to calculate to split the document into segments
+                   used to count the length of a document.
+                       * If tokenizer is 'char', then the document is split up
+                         into characters which are counted to adhere to `doc_length`
+                       * If tokenizer is 'whitespace', the document is split up
+                         into words separated by whitespaces. These words are counted
+                         and truncated depending on `doc_length`
+                       * If tokenizer is 'vectorizer', then the internal CountVectorizer
+                         is used to tokenize the document. These tokens are counted
+                         and truncated depending on `doc_length`
+                       * If tokenizer is a callable, then that callable is used to tokenize
+                         the document. These tokens are counted and truncated depending
+                         on `doc_length`
+
+    Usage:
+
+    To use this, you will need to install the ollama package first:
+
+    `pip install ollama`
+
+    Then, you can use the Ollama representation model as follows:
+
+    ```python
+    from bertopic.representation import Ollama
+    from bertopic import BERTopic
+
+    # Create your representation model
+    representation_model = Ollama("gemma3")
+
+    # Use the representation model in BERTopic on top of the default pipeline
+    topic_model = BERTopic(representation_model=representation_model)
+    ```
+
+    You can also use a custom prompt:
+
+    ```python
+    prompt = "I have the following documents: [DOCUMENTS] \nThese documents are about the following topic: '"
+    representation_model = Ollama("gemma3", prompt=prompt)
+    ```
+    """
+
+    def __init__(
+        self,
+        model: str,
+        prompt: str | None = None,
+        system_prompt: str | None = None,
+        json_schema: Mapping[str, Any] | bool = False,
+        generator_kwargs: Mapping[str, Any] = {},
+        nr_docs: int = 4,
+        diversity: float | None = None,
+        doc_length: int | None = None,
+        tokenizer: Union[str, Callable] | None = None,
+    ):
+        super().__init__(
+            prompt=prompt if prompt is not None else DEFAULT_CHAT_PROMPT,
+            nr_docs=nr_docs,
+            diversity=diversity,
+            doc_length=doc_length,
+            tokenizer=tokenizer,
+        )
+
+        # Ollama specific parameters
+        self.model = model
+        self.system_prompt = DEFAULT_SYSTEM_PROMPT if system_prompt is None else system_prompt
+        self.json_schema = DEFAULT_JSON_SCHEMA if json_schema is True else json_schema
+        self.generator_kwargs = generator_kwargs
+
+    def extract_topics(
+        self,
+        topic_model,
+        documents: pd.DataFrame,
+        c_tf_idf: csr_matrix,
+        topics: Mapping[str, List[Tuple[str, float]]],
+    ) -> Mapping[str, List[Tuple[str, float]]]:
+        """Extract topics.
+
+        Arguments:
+            topic_model: A BERTopic model
+            documents: All input documents
+            c_tf_idf: The topic c-TF-IDF representation
+            topics: The candidate topics as calculated with c-TF-IDF
+
+        Returns:
+            updated_topics: Updated topic representations
+        """
+        # Extract the top n representative documents per topic
+        repr_docs_mappings, _, _, _ = topic_model._extract_representative_docs(
+            c_tf_idf, documents, topics, 500, self.nr_docs, self.diversity
+        )
+
+        # Generate using Ollama's Language Model
+        updated_topics = {}
+        for topic, docs in tqdm(repr_docs_mappings.items(), disable=not topic_model.verbose):
+            prompt = self._create_prompt(docs=docs, topic=topic, topics=topics, topic_model=topic_model)
+            self.prompts_.append(prompt)
+
+            # Call Ollama API
+            messages = [
+                {"role": "system", "content": self.system_prompt},
+                {"role": "user", "content": prompt},
+            ]
+            kwargs = {
+                "model": self.model,
+                "messages": messages,
+                **self.generator_kwargs,
+            }
+            response: ChatResponse = chat(**kwargs)
+
+            # Update labels
+            if self.json_schema:
+                try:
+                    label = json.loads(response.message.content)["topic_label"]
+                except (JSONDecodeError, KeyError):
+                    label = response.message.content.strip().replace("topic: ", "")
+            else:
+                label = response.message.content.strip().replace("topic: ", "")
+            updated_topics[topic] = [(label, 1)]
+
+        return updated_topics
diff --git a/bertopic/representation/_openai.py b/bertopic/representation/_openai.py
index 71eb8c9a..4f0ecfe8 100644
--- a/bertopic/representation/_openai.py
+++ b/bertopic/representation/_openai.py
@@ -4,50 +4,14 @@
 from tqdm import tqdm
 from scipy.sparse import csr_matrix
 from typing import Mapping, List, Tuple, Any, Union, Callable
-from bertopic.representation._base import BaseRepresentation
+from bertopic.representation._base import LLMRepresentation
 from bertopic.representation._utils import (
     retry_with_exponential_backoff,
-    truncate_document,
-    validate_truncate_document_parameters,
 )
+from bertopic.representation._prompts import DEFAULT_SYSTEM_PROMPT, DEFAULT_CHAT_PROMPT
 
 
-DEFAULT_CHAT_PROMPT = """You will extract a short topic label from given documents and keywords.
-Here are two examples of topics you created before:
-
-# Example 1
-Sample texts from this topic:
-- Traditional diets in most cultures were primarily plant-based with a little meat on top, but with the rise of industrial style meat production and factory farming, meat has become a staple food.
-- Meat, but especially beef, is the worst food in terms of emissions.
-- Eating meat doesn't make you a bad person, not eating meat doesn't make you a good one.
-
-Keywords: meat beef eat eating emissions steak food health processed chicken
-topic: Environmental impacts of eating meat
-
-# Example 2
-Sample texts from this topic:
-- I have ordered the product weeks ago but it still has not arrived!
-- The website mentions that it only takes a couple of days to deliver but I still have not received mine.
-- I got a message stating that I received the monitor but that is not true!
-- It took a month longer to deliver than was advised...
-
-Keywords: deliver weeks product shipping long delivery received arrived arrive week
-topic: Shipping and delivery issues
-
-# Your task
-Sample texts from this topic:
-[DOCUMENTS]
-
-Keywords: [KEYWORDS]
-
-Based on the information above, extract a short topic label (three words at most) in the following format:
-topic: <topic_label>
-"""
-
-DEFAULT_SYSTEM_PROMPT = "You are an assistant that extracts high-level topics from texts."
-
-
-class OpenAI(BaseRepresentation):
+class OpenAI(LLMRepresentation):
     r"""Using the OpenAI API to generate topic labels based
     on one of their Completion of ChatCompletion models.
 
@@ -60,12 +24,12 @@ class OpenAI(BaseRepresentation):
         generator_kwargs: Kwargs passed to `openai.Completion.create`
                           for fine-tuning the output.
         prompt: The prompt to be used in the model. If no prompt is given,
-                `self.default_prompt_` is used instead.
+                `bertopic.representation._prompts.DEFAULT_CHAT_PROMPT` is used instead.
                 NOTE: Use `"[KEYWORDS]"` and `"[DOCUMENTS]"` in the prompt
                 to decide where the keywords and documents need to be
                 inserted.
         system_prompt: The system prompt to be used in the model. If no system prompt is given,
-                       `self.default_system_prompt_` is used instead.
+                       `bertopic.representation._prompts.DEFAULT_SYSTEM_PROMPT` is used instead.
         delay_in_seconds: The delay in seconds between consecutive prompts
                           in order to prevent RateLimitErrors.
         exponential_backoff: Retry requests with a random exponential backoff.
@@ -143,41 +107,22 @@ def __init__(
         diversity: float | None = None,
         doc_length: int | None = None,
         tokenizer: Union[str, Callable] | None = None,
-        **kwargs,
     ):
+        super().__init__(
+            prompt=prompt if prompt is not None else DEFAULT_CHAT_PROMPT,
+            nr_docs=nr_docs,
+            diversity=diversity,
+            doc_length=doc_length,
+            tokenizer=tokenizer,
+        )
+
+        # OpenAI specific parameters
         self.client = client
         self.model = model
-
-        if prompt is None:
-            self.prompt = DEFAULT_CHAT_PROMPT
-        else:
-            self.prompt = prompt
-
-        if system_prompt is None:
-            self.system_prompt = DEFAULT_SYSTEM_PROMPT
-        else:
-            self.system_prompt = system_prompt
-
-        self.default_prompt_ = DEFAULT_CHAT_PROMPT
-        self.default_system_prompt_ = DEFAULT_SYSTEM_PROMPT
+        self.system_prompt = system_prompt if system_prompt is not None else DEFAULT_SYSTEM_PROMPT
         self.delay_in_seconds = delay_in_seconds
         self.exponential_backoff = exponential_backoff
-        self.nr_docs = nr_docs
-        self.diversity = diversity
-        self.doc_length = doc_length
-        self.tokenizer = tokenizer
-        validate_truncate_document_parameters(self.tokenizer, self.doc_length)
-
-        self.prompts_ = []
-
         self.generator_kwargs = generator_kwargs
-        if self.generator_kwargs.get("model"):
-            self.model = generator_kwargs.get("model")
-            del self.generator_kwargs["model"]
-        if self.generator_kwargs.get("prompt"):
-            del self.generator_kwargs["prompt"]
-        if not self.generator_kwargs.get("stop"):
-            self.generator_kwargs["stop"] = "\n"
 
     def extract_topics(
         self,
@@ -205,8 +150,7 @@ def extract_topics(
         # Generate using OpenAI's Language Model
         updated_topics = {}
         for topic, docs in tqdm(repr_docs_mappings.items(), disable=not topic_model.verbose):
-            truncated_docs = [truncate_document(topic_model, self.doc_length, self.tokenizer, doc) for doc in docs]
-            prompt = self._create_prompt(truncated_docs, topic, topics)
+            prompt = self._create_prompt(docs=docs, topic=topic, topics=topics, topic_model=topic_model)
             self.prompts_.append(prompt)
 
             # Delay
@@ -239,33 +183,6 @@ def extract_topics(
 
         return updated_topics
 
-    def _create_prompt(self, docs, topic, topics):
-        keywords = next(zip(*topics[topic]))
-
-        # Use the Default Chat Prompt
-        if self.prompt == DEFAULT_CHAT_PROMPT:
-            prompt = self.prompt.replace("[KEYWORDS]", ", ".join(keywords))
-            prompt = self._replace_documents(prompt, docs)
-
-        # Use a custom prompt that leverages keywords, documents or both using
-        # custom tags, namely [KEYWORDS] and [DOCUMENTS] respectively
-        else:
-            prompt = self.prompt
-            if "[KEYWORDS]" in prompt:
-                prompt = prompt.replace("[KEYWORDS]", ", ".join(keywords))
-            if "[DOCUMENTS]" in prompt:
-                prompt = self._replace_documents(prompt, docs)
-
-        return prompt
-
-    @staticmethod
-    def _replace_documents(prompt, docs):
-        to_replace = ""
-        for doc in docs:
-            to_replace += f"- {doc}\n"
-        prompt = prompt.replace("[DOCUMENTS]", to_replace)
-        return prompt
-
 
 def chat_completions_with_backoff(client, **kwargs):
     return retry_with_exponential_backoff(
diff --git a/bertopic/representation/_prompts.py b/bertopic/representation/_prompts.py
new file mode 100644
index 00000000..268c4bb1
--- /dev/null
+++ b/bertopic/representation/_prompts.py
@@ -0,0 +1,46 @@
+DEFAULT_COMPLETION_PROMPT = "What are these documents about? Please give a single label."
+DEFAULT_CHAT_PROMPT = """You will extract a short topic label from given documents and keywords.
+Here are two examples of topics you created before:
+
+# Example 1
+Sample texts from this topic:
+- Traditional diets in most cultures were primarily plant-based with a little meat on top, but with the rise of industrial style meat production and factory farming, meat has become a staple food.
+- Meat, but especially beef, is the worst food in terms of emissions.
+- Eating meat doesn't make you a bad person, not eating meat doesn't make you a good one.
+
+Keywords: meat beef eat eating emissions steak food health processed chicken
+topic: Environmental impacts of eating meat
+
+# Example 2
+Sample texts from this topic:
+- I have ordered the product weeks ago but it still has not arrived!
+- The website mentions that it only takes a couple of days to deliver but I still have not received mine.
+- I got a message stating that I received the monitor but that is not true!
+- It took a month longer to deliver than was advised...
+
+Keywords: deliver weeks product shipping long delivery received arrived arrive week
+topic: Shipping and delivery issues
+
+# Your task
+Sample texts from this topic:
+[DOCUMENTS]
+
+Keywords: [KEYWORDS]
+
+Based on the information above, extract a short topic label (three words at most) in the following format:
+topic: <topic_label>
+"""
+
+DEFAULT_SYSTEM_PROMPT = "You are an assistant that extracts high-level topics from texts."
+DEFAULT_JSON_SCHEMA = {
+    "properties": {
+        "topic_label": {
+            "description": "A short, human-readable label that summarizes the main topic.",
+            "title": "Topic Label",
+            "type": "string",
+        }
+    },
+    "required": ["topic_label"],
+    "title": "Topic",
+    "type": "object",
+}
diff --git a/bertopic/representation/_textgeneration.py b/bertopic/representation/_textgeneration.py
index 8c8e36f6..e9639fa4 100644
--- a/bertopic/representation/_textgeneration.py
+++ b/bertopic/representation/_textgeneration.py
@@ -4,17 +4,11 @@
 from transformers import pipeline, set_seed
 from transformers.pipelines.base import Pipeline
 from typing import Mapping, List, Tuple, Any, Union, Callable
-from bertopic.representation._base import BaseRepresentation
-from bertopic.representation._utils import truncate_document, validate_truncate_document_parameters
+from bertopic.representation._base import LLMRepresentation
+from bertopic.representation._prompts import DEFAULT_CHAT_PROMPT
 
 
-DEFAULT_PROMPT = """
-I have a topic described by the following keywords: [KEYWORDS].
-The name of this topic is:
-"""
-
-
-class TextGeneration(BaseRepresentation):
+class TextGeneration(LLMRepresentation):
     """Text2Text or text generation with transformers.
 
     Arguments:
@@ -23,7 +17,7 @@ class TextGeneration(BaseRepresentation):
                For example, `pipeline('text-generation', model='gpt2')`. If a string
                is passed, "text-generation" will be selected by default.
         prompt: The prompt to be used in the model. If no prompt is given,
-                `self.default_prompt_` is used instead.
+                `bertopic.representation._prompts.DEFAULT_CHAT_PROMPT` is used instead.
                 NOTE: Use `"[KEYWORDS]"` and `"[DOCUMENTS]"` in the prompt
                 to decide where the keywords and documents need to be
                 inserted.
@@ -93,8 +87,18 @@ def __init__(
         doc_length: int | None = None,
         tokenizer: Union[str, Callable] | None = None,
     ):
+        super().__init__(
+            prompt=prompt if prompt is not None else DEFAULT_CHAT_PROMPT,
+            nr_docs=nr_docs,
+            diversity=diversity,
+            doc_length=doc_length,
+            tokenizer=tokenizer,
+        )
+
+        # Transformer specific parameters
         self.random_state = random_state
         set_seed(random_state)
+
         if isinstance(model, str):
             self.model = pipeline("text-generation", model=model)
         elif isinstance(model, Pipeline):
@@ -105,16 +109,7 @@ def __init__(
                 "pass is either a string referring to a"
                 "HF model or a `transformers.pipeline` object."
             )
-        self.prompt = prompt if prompt is not None else DEFAULT_PROMPT
-        self.default_prompt_ = DEFAULT_PROMPT
         self.pipeline_kwargs = pipeline_kwargs
-        self.nr_docs = nr_docs
-        self.diversity = diversity
-        self.doc_length = doc_length
-        self.tokenizer = tokenizer
-        validate_truncate_document_parameters(self.tokenizer, self.doc_length)
-
-        self.prompts_ = []
 
     def extract_topics(
         self,
@@ -134,23 +129,14 @@ def extract_topics(
         Returns:
             updated_topics: Updated topic representations
         """
-        # Extract the top 4 representative documents per topic
-        if self.prompt != DEFAULT_PROMPT and "[DOCUMENTS]" in self.prompt:
-            repr_docs_mappings, _, _, _ = topic_model._extract_representative_docs(
-                c_tf_idf, documents, topics, 500, self.nr_docs, self.diversity
-            )
-        else:
-            repr_docs_mappings = {topic: None for topic in topics.keys()}
+        # Extract the top n representative documents per topic
+        repr_docs_mappings, _, _, _ = topic_model._extract_representative_docs(
+            c_tf_idf, documents, topics, 500, self.nr_docs, self.diversity
+        )
 
         updated_topics = {}
         for topic, docs in tqdm(repr_docs_mappings.items(), disable=not topic_model.verbose):
-            # Prepare prompt
-            truncated_docs = (
-                [truncate_document(topic_model, self.doc_length, self.tokenizer, doc) for doc in docs]
-                if docs is not None
-                else docs
-            )
-            prompt = self._create_prompt(truncated_docs, topic, topics)
+            prompt = self._create_prompt(docs=docs, topic=topic, topics=topics, topic_model=topic_model)
             self.prompts_.append(prompt)
 
             # Extract result from generator and use that as label
@@ -165,24 +151,3 @@ def extract_topics(
             updated_topics[topic] = topic_description
 
         return updated_topics
-
-    def _create_prompt(self, docs, topic, topics):
-        keywords = ", ".join(next(zip(*topics[topic])))
-
-        # Use the default prompt and replace keywords
-        if self.prompt == DEFAULT_PROMPT:
-            prompt = self.prompt.replace("[KEYWORDS]", keywords)
-
-        # Use a prompt that leverages either keywords or documents in
-        # a custom location
-        else:
-            prompt = self.prompt
-            if "[KEYWORDS]" in prompt:
-                prompt = prompt.replace("[KEYWORDS]", keywords)
-            if "[DOCUMENTS]" in prompt:
-                to_replace = ""
-                for doc in docs:
-                    to_replace += f"- {doc}\n"
-                prompt = prompt.replace("[DOCUMENTS]", to_replace)
-
-        return prompt