diff --git a/docs/configuration.md b/docs/configuration.md
index 9875b9cd..bfbd200a 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -109,7 +109,7 @@ See the [Pydantic AI documentation](https://ai.pydantic.dev/models/) for the com
 
 Reranking improves search quality by re-ordering the initial search results using specialized models. When enabled, the system retrieves more candidates (3x the requested limit) and then reranks them to return the most relevant results.
 
-Reranking is **automatically enabled** by default using Ollama, or if you install the appropriate reranking provider package.
+Reranking is **disabled by default** for faster searches. You can enable it by configuring a reranking provider.
 
 ### Disabling Reranking
 
diff --git a/docs/python.md b/docs/python.md
index fa3f5f03..c1f9af5a 100644
--- a/docs/python.md
+++ b/docs/python.md
@@ -101,9 +101,9 @@ async for doc_id in client.rebuild_database():
 
 ## Searching Documents
 
-The search method performs native hybrid search (vector + full-text) using LanceDB with **reranking enabled by default** for improved relevance:
+The search method performs native hybrid search (vector + full-text) using LanceDB with optional reranking for improved relevance:
 
-Basic hybrid search (default, with reranking):
+Basic hybrid search (default):
 ```python
 results = await client.search("machine learning algorithms", limit=5)
 for chunk, score in results:
diff --git a/src/haiku/rag/config.py b/src/haiku/rag/config.py
index 78328fe6..31eee040 100644
--- a/src/haiku/rag/config.py
+++ b/src/haiku/rag/config.py
@@ -19,7 +19,7 @@ class AppConfig(BaseModel):
     EMBEDDINGS_MODEL: str = "mxbai-embed-large"
     EMBEDDINGS_VECTOR_DIM: int = 1024
 
-    RERANK_PROVIDER: str = "ollama"
+    RERANK_PROVIDER: str = ""
     RERANK_MODEL: str = "qwen3"
 
     QA_PROVIDER: str = "ollama"