diff --git a/README.md b/README.md
index d7cdc48e1..b7b72cb7e 100644
--- a/README.md
+++ b/README.md
@@ -22,6 +22,31 @@ The key features of the Intelligence Layer are:
 4. [Use-case index](#use-case-index)
 5. [How to make your own use-case](#how-to-make-your-own-use-case)
 
+To give you a starting point for using the Intelligence Layer, we provide some pre-configured `Task`s that are ready to use out-of-the-box, as well as an accompanying "Getting started" guide in the form of Jupyter Notebooks.
+
+| Type      | Task                                                                                              | Description                                                               |
+| --------- | ------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------- |
+| Classify  | [EmbeddingBasedClassify](./src/intelligence_layer/use_cases/classify/embedding_based_classify.py) | Classify a text of limited size that fits into the models context size with a single class where each class is defined by multiple examples. |
+| Classify  | [SingleLabelClassify](./src/intelligence_layer/use_cases/classify/single_label_classify.py)       | Classify a text of limited size that fits into the models context size with a single class where each class is defined just by its name using zero-shot prompting. |
+| QA        | [LongContextQa](./src/intelligence_layer/use_cases/qa/long_context_qa.py)                         | Answer a question based on one document of any length. |
+| QA        | [MultipleChunkQa](./src/intelligence_layer/use_cases/qa/multiple_chunk_qa.py)                     | Answer a question based a list of text where each element is of limited size that fits into the models context. |
+| QA        | [RetrieverBasedQa](./src/intelligence_layer/use_cases/qa/retriever_based_qa.py)                   | Answer a question based on a document base that is accessed through a [BaseRetriever](...) implementation. |
+| QA        | [SingleChunkQa](./src/intelligence_layer/use_cases/qa/single_chunk_qa.py)                         | Answer a question based on a text of limited size that fits into the models context. |
+| Search    | [QdrantSearch](./src/intelligence_layer/use_cases/search/qdrant_search.py)                        | Search through texts given a query and some filters (Move to core?).           |
+| Search    | [Search](./src/intelligence_layer/use_cases/search/search.py)                                     | Search a document based for document chunks that fit to a given query by using a [BaseRetriever](...) implementation. |
+| Summarize | [ShortBodySummarize](./src/intelligence_layer/use_cases/summarize/summarize.py)                   | Summarize a text of limited size that fits into the models context size into a short body text. |
+
+### How to make your own
+
+Note that we do not expect the above use cases to solve all of your issues.
+Instead, we encourage you to think of our pre-configured use cases as a foundation to fast-track your development process.
+By leveraging these tasks, you gain insights into the framework's capabilities and best practices.
+
+We encourage you to copy and paste these use cases directly into your own project.
+From here, you can customize everything, including the prompt, model, and more intricate functional logic.
+This not only saves you time but also ensures you're building on a tried and tested foundation.
+Therefore, think of these use-cases as stepping stones, guiding you towards crafting tailored solutions that best fit your unique requirements.
+
 ## Getting started
 
 Not sure where to start? Familiarize yourself with the Intelligence Layer using the below notebooks.