diff --git a/farm_haystack/indexing/io.py b/farm_haystack/indexing/io.py
index ea00db2c9..a9dd70c56 100644
--- a/farm_haystack/indexing/io.py
+++ b/farm_haystack/indexing/io.py
@@ -27,7 +27,7 @@ def write_documents_to_db(document_dir, clean_func=None):
             db.session.add(doc)
             db.session.commit()
         n_docs += 1
-    logger.info(f"Wrote {n_docs} to DB")
+    logger.info(f"Wrote {n_docs} docs to DB")
 
 
 def fetch_archive_from_http(url, output_dir, proxies=None):
diff --git a/farm_haystack/retriever/tfidf.py b/farm_haystack/retriever/tfidf.py
index a7745557f..d371f84af 100644
--- a/farm_haystack/retriever/tfidf.py
+++ b/farm_haystack/retriever/tfidf.py
@@ -66,7 +66,7 @@ class TfidfRetriever(BaseRetriever):
                     Paragraph(document_id=doc.id, paragraph_id=p_id, text=(p,))
                 )
                 p_id += 1
-        logger.info(f"Found {len(documents)} candidates in DB")
+        logger.info(f"Found {len(paragraphs)} candidate passages from {len(documents)} docs in DB")
         return paragraphs
 
     def retrieve(self, query, candidate_doc_ids=None, top_k=10):
diff --git a/tutorial.ipynb b/tutorials/Tutorial1_Basic_QA_Pipeline.ipynb
similarity index 66%
rename from tutorial.ipynb
rename to tutorials/Tutorial1_Basic_QA_Pipeline.ipynb
index 365486227..f186af2ab 100644
--- a/tutorial.ipynb
+++ b/tutorials/Tutorial1_Basic_QA_Pipeline.ipynb
@@ -2,23 +2,13 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
     "pycharm": {
      "is_executing": false
     }
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "11/11/2019 18:43:25 - INFO - root -   Using localhost sqlite as the database backend. as Database not configured. Add a qa_config.py file in the Python path with DATABASE_URL set.Continuing with the default sqlite on localhost.\n",
-      "I1111 18:43:25.533496 140652943304512 file_utils.py:39] PyTorch version 1.3.0 available.\n",
-      "I1111 18:43:25.611577 140652943304512 modeling_xlnet.py:194] Better speed can be achieved with apex installed from https://www.github.com/nvidia/apex .\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from farm_haystack.reader.adaptive_model import FARMReader\n",
     "from farm_haystack.retriever.tfidf import TfidfRetriever\n",
@@ -28,66 +18,77 @@
     "from farm_haystack.utils import print_answers"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Task: Question Answering for Game of Thrones\n",
+    "\n",
+    "<img style=\"float: right;\" src=\"https://upload.wikimedia.org/wikipedia/en/d/d8/Game_of_Thrones_title_card.jpg\">\n",
+    "\n",
+    "Question Answering can be used in a variety of use cases. A very common one:  Using it to navigate through complex knowledge bases or long documents (\"search setting\").\n",
+    "\n",
+    "A \"knowledge base\" could for example be your website, an internal wiki or a collection of financial reports. \n",
+    "In this tutorial we will work on a slightly different domain: \"Game of Thrones\". \n",
+    "\n",
+    "Let's see how we can use a bunch of wikipedia articles to answer a variety of questions about the \n",
+    "marvellous seven kingdoms...  \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Indexing & cleaning documents"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {
     "pycharm": {
      "is_executing": false
     }
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "I1111 18:43:25.834958 140652943304512 io.py:46] Fetching from https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip to `data/article_txt_got`\n",
-      "\r",
-      "  0%|          | 0/1167348 [00:00<?, ?B/s]\r",
-      "100%|██████████| 1167348/1167348 [00:00<00:00, 13662984.26B/s]\n",
-      "I1111 18:43:26.548232 140652943304512 io.py:31] Wrote 517 to DB\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Init a database (default: sqllite)\n",
     "from farm_haystack.database import db\n",
     "db.create_all()\n",
     "\n",
     "# Let's first get some documents that we want to query\n",
-    "# Here: Wikipedia articles for Game of Thrones\n",
-    "\n",
+    "# Here: 517 Wikipedia articles for Game of Thrones\n",
     "doc_dir = \"data/article_txt_got\"\n",
     "s3_url = \"https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip\"\n",
-    "\n",
     "fetch_archive_from_http(url=s3_url, output_dir=doc_dir)\n",
     "\n",
-    "# We supply a function to clean the docs and write them afterwards to our DB\n",
+    "# Now, let's write the docs to our DB. \n",
+    "# You can supply a cleaning/transformation function that is applied to each doc (e.g. to remove headers)\n",
+    "# It must take a str as input, and return a str.\n",
+    "# Our 517 documents also get splitted into 2068 smaller paragraphs here. \n",
     "write_documents_to_db(document_dir=doc_dir, clean_func=clean_wiki_text)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initalize Reader, Retriever & Finder"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
     "pycharm": {
      "is_executing": false,
      "name": "#%%\n"
     }
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "I1111 18:43:26.589386 140652943304512 tfidf.py:68] Found 517 candidates in DB\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# A retriever identifies the k most promising chunks of text that might contain the answer for our question\n",
     "# Retrievers use some simple but fast algorithm, here: TF-IDF\n",
-    "\n",
     "retriever = TfidfRetriever()"
    ]
   },
@@ -117,10 +118,16 @@
    "outputs": [],
    "source": [
     "# The Finder sticks together retriever and retriever in a pipeline to answer our actual questions \n",
-    "\n",
     "finder = Finder(reader, retriever)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Voilá! Ask a question!"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -131,11 +138,17 @@
    },
    "outputs": [],
    "source": [
-    "# Voilá! Ask a question!\n",
-    "\n",
+    "prediction = finder.get_answers(question=\"Who is the father of Arya Stark?\", top_k_reader=10, top_k_retriever=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "#prediction = finder.get_answers(question=\"Who created the Dothraki vocabulary?\", top_k_reader=5)\n",
-    "#prediction = finder.get_answers(question=\"Who is the sister of Sansa?\", top_k_reader=5)\n",
-    "prediction = finder.get_answers(question=\"Who is the father of Arya Stark?\", top_k_reader=3, top_k_retriever=5)"
+    "#prediction = finder.get_answers(question=\"Who is the sister of Sansa?\", top_k_reader=5)"
    ]
   },
   {
@@ -151,19 +164,6 @@
    "source": [
     "print_answers(prediction, details=\"minimal\")"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "\n"
-   ]
   }
  ],
  "metadata": {
@@ -182,7 +182,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.3"
   },
   "pycharm": {
    "stem_cell": {