diff --git a/docs/_src/tutorials/tutorials/11.md b/docs/_src/tutorials/tutorials/11.md
index b6a30ec02..4d0e434e3 100644
--- a/docs/_src/tutorials/tutorials/11.md
+++ b/docs/_src/tutorials/tutorials/11.md
@@ -224,7 +224,7 @@ with the keyword based `ElasticsearchRetriever`.
 See our [documentation](https://haystack.deepset.ai/docs/latest/retrievermd) to understand why
 we might want to combine a dense and sparse retriever.
 
-![image]()
+![image](https://github.com/deepset-ai/haystack/blob/master/docs/_src/img/tutorial11_custompipelines_pipeline_ensemble.png?raw=true)
 
 Here we use a `JoinDocuments` node so that the predictions from each retriever can be merged together.
 
@@ -285,7 +285,7 @@ Decision Nodes help you route your data so that only certain branches of your `P
 One popular use case for such query classifiers is routing keyword queries to Elasticsearch and questions to EmbeddingRetriever + Reader.
 With this approach you keep optimal speed and simplicity for keywords while going deep with transformers when it's most helpful.
 
-![image]()
+![image](https://github.com/deepset-ai/haystack/blob/master/docs/_src/img/tutorial11_decision_nodes_pipeline_classifier.png?raw=true)
 
 Though this looks very similar to the ensembled pipeline shown above,
 the key difference is that only one of the retrievers is run for each request.
diff --git a/tutorials/Tutorial11_Pipelines.ipynb b/tutorials/Tutorial11_Pipelines.ipynb
index 6b94fd263..2ecd7087b 100644
--- a/tutorials/Tutorial11_Pipelines.ipynb
+++ b/tutorials/Tutorial11_Pipelines.ipynb
@@ -438,7 +438,7 @@
     "See our [documentation](https://haystack.deepset.ai/docs/latest/retrievermd) to understand why\n",
     "we might want to combine a dense and sparse retriever.\n",
     "\n",
-    "![image]()\n",
+    "![image](https://github.com/deepset-ai/haystack/blob/master/docs/_src/img/tutorial11_custompipelines_pipeline_ensemble.png?raw=true)\n",
     "\n",
     "Here we use a `JoinDocuments` node so that the predictions from each retriever can be merged together."
    ]
@@ -537,7 +537,7 @@
     "One popular use case for such query classifiers is routing keyword queries to Elasticsearch and questions to EmbeddingRetriever + Reader.\n",
     "With this approach you keep optimal speed and simplicity for keywords while going deep with transformers when it's most helpful.\n",
     "\n",
-    "![image]()\n",
+    "![image](https://github.com/deepset-ai/haystack/blob/master/docs/_src/img/tutorial11_decision_nodes_pipeline_classifier.png?raw=true)\n",
     "\n",
     "Though this looks very similar to the ensembled pipeline shown above,\n",
     "the key difference is that only one of the retrievers is run for each request.\n",