diff --git a/README.md b/README.md
index ce71596b3..3136555ee 100644
--- a/README.md
+++ b/README.md
@@ -89,6 +89,9 @@ to see how to use the API in more detail. Check out the [batch generation
 example](https://github.com/ml-explore/mlx-lm/tree/main/mlx_lm/examples/batch_generate_response.py)
 to see how to efficiently generate continuations for a batch of prompts.
 
+Check out the [RAG example](https://github.com/ml-explore/mlx-lm/tree/main/mlx_lm/examples/rag.py)
+to see how to use retrieval-augmented generation to ground LLM responses in external documents.
+
 The `mlx-lm` package also comes with functionality to quantize and optionally
 upload models to the Hugging Face Hub.
 
diff --git a/mlx_lm/examples/rag.py b/mlx_lm/examples/rag.py
new file mode 100644
index 000000000..28553214e
--- /dev/null
+++ b/mlx_lm/examples/rag.py
@@ -0,0 +1,93 @@
+# Copyright © 2025 Apple Inc.
+
+import numpy as np
+from mlx_lm import load, generate
+import mlx.core as mx
+
+
+def retrieve(question, documents, model, tokenizer):
+    # Embed the question
+    question_embedding = get_embedding(question, model, tokenizer)
+
+    # Embed all documents
+    doc_embeddings = [get_embedding(doc, model, tokenizer) for doc in documents]
+
+    # Compute cosine similarity between question and each document
+    similarities = [
+        cosine_similarity(question_embedding, doc_emb) for doc_emb in doc_embeddings
+    ]
+
+    # Return the document with the highest similarity score
+    best_idx = int(np.argmax(similarities))
+    return documents[best_idx]
+
+
+def get_embedding(text, model, tokenizer):
+    # Tokenize the text
+    tokens = tokenizer.encode(text)
+    token_array = mx.array([tokens])
+
+    # Run a full forward pass and get the last hidden state
+    # by extracting from the model's transformer layers
+    hidden = model.model.embed_tokens(token_array)
+    for layer in model.model.layers:
+        hidden = layer(hidden, mask=None, cache=None)
+
+    # Force computation
+    mx.eval(hidden)
+
+    # Mean pool across token dimension to get sentence embedding
+    embedding = np.array(hidden[0].tolist()).mean(axis=0)
+    return embedding
+
+
+def cosine_similarity(a, b):
+    # Compute cosine similarity between two vectors
+    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
+
+
+if __name__ == "__main__":
+    # Specify the checkpoint
+    checkpoint = "mlx-community/Llama-3.2-3B-Instruct-4bit"
+
+    # Load the model and tokenizer
+    model, tokenizer = load(path_or_hf_repo=checkpoint)
+
+    # A list of documents the model does not have direct access to
+    documents = [
+        "MLX is an array framework for machine learning on Apple silicon, developed by Apple.",
+        "The Eiffel Tower is located in Paris, France, and was completed in 1889.",
+        "Photosynthesis is the process by which plants use sunlight, water, and CO2 to produce energy.",
+        "The Python programming language was created by Guido van Rossum and first released in 1991.",
+        "Apple silicon chips use a unified memory architecture where CPU and GPU share the same memory pool.",
+    ]
+
+    # The user question
+    question = "What is MLX and who made it?"
+
+    # Retrieve the most relevant document using embedding similarity
+    retrieved_doc = retrieve(question, documents, model, tokenizer)
+
+    # Build the prompt with the retrieved context injected
+    prompt = f"""Use the following context to answer the question.
+
+Context: {retrieved_doc}
+
+Question: {question}
+Answer:"""
+
+    # Format using the chat template
+    conversation = [{"role": "user", "content": prompt}]
+    formatted_prompt = tokenizer.apply_chat_template(
+        conversation=conversation,
+        add_generation_prompt=True,
+    )
+
+    # Generate the answer
+    response = generate(
+        model=model,
+        tokenizer=tokenizer,
+        prompt=formatted_prompt,
+        max_tokens=300,
+        verbose=True,
+    )
diff --git a/tests/test_rag.py b/tests/test_rag.py
new file mode 100644
index 000000000..aa1781bbc
--- /dev/null
+++ b/tests/test_rag.py
@@ -0,0 +1,79 @@
+# Copyright © 2025 Apple Inc.
+
+import unittest
+from unittest.mock import MagicMock, patch
+
+import numpy as np
+
+from mlx_lm.examples.rag import cosine_similarity, retrieve
+
+
+class TestCosineSimilarity(unittest.TestCase):
+
+    def test_identical_vectors(self):
+        # Identical vectors should have similarity of 1.0
+        a = np.array([1.0, 2.0, 3.0])
+        self.assertAlmostEqual(cosine_similarity(a, a), 1.0, places=5)
+
+    def test_orthogonal_vectors(self):
+        # Orthogonal vectors should have similarity of 0.0
+        a = np.array([1.0, 0.0])
+        b = np.array([0.0, 1.0])
+        self.assertAlmostEqual(cosine_similarity(a, b), 0.0, places=5)
+
+    def test_opposite_vectors(self):
+        # Opposite vectors should have similarity of -1.0
+        a = np.array([1.0, 0.0])
+        b = np.array([-1.0, 0.0])
+        self.assertAlmostEqual(cosine_similarity(a, b), -1.0, places=5)
+
+
+class TestRetrieve(unittest.TestCase):
+
+    def setUp(self):
+        self.documents = [
+            "MLX is an array framework for machine learning on Apple silicon, developed by Apple.",
+            "The Eiffel Tower is located in Paris, France, and was completed in 1889.",
+            "Photosynthesis is the process by which plants use sunlight, water, and CO2 to produce energy.",
+        ]
+
+    def test_retrieves_most_similar_document(self):
+        # Mock get_embedding to return controlled vectors
+        # Question embedding is closest to document 0
+        embeddings = {
+            "what is mlx?": np.array([1.0, 0.0, 0.0]),
+            self.documents[0]: np.array([0.9, 0.1, 0.0]),  # most similar
+            self.documents[1]: np.array([0.0, 1.0, 0.0]),
+            self.documents[2]: np.array([0.0, 0.0, 1.0]),
+        }
+
+        mock_model = MagicMock()
+        mock_tokenizer = MagicMock()
+
+        with patch(
+            "mlx_lm.examples.rag.get_embedding",
+            side_effect=lambda text, m, t: embeddings[text],
+        ):
+            result = retrieve(
+                "what is mlx?", self.documents, mock_model, mock_tokenizer
+            )
+
+        self.assertEqual(result, self.documents[0])
+
+    def test_single_document_always_returned(self):
+        # With a single document, it should always be returned
+        single_doc = ["Only document."]
+        mock_model = MagicMock()
+        mock_tokenizer = MagicMock()
+
+        with patch(
+            "mlx_lm.examples.rag.get_embedding",
+            return_value=np.array([1.0, 0.0]),
+        ):
+            result = retrieve("any question", single_doc, mock_model, mock_tokenizer)
+
+        self.assertEqual(result, single_doc[0])
+
+
+if __name__ == "__main__":
+    unittest.main()