diff --git a/topic3/README.md b/topic3/README.md
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+++ b/topic3/README.md
@@ -27,3 +27,14 @@
   [**Advanced RAG issues**](https://nebius-academy.github.io/knowledge-base/advanced-rag-issues/)
 
   **RAPTOR demo** [colab link](https://colab.research.google.com/github/Nebius-Academy/LLM-Engineering-Essentials/blob/main/topic3/3.4_RAPTOR_demo.ipynb)
+
+
+## Project part: deploying an RAG service
+
+The project materials are in the [rag_service](https://github.com/Nebius-Academy/LLMOps-Essentials/tree/rag_service) branch of the project repo. For deployment guide, see the usual [Deployment manual](https://github.com/Nebius-Academy/LLMOps-Essentials/blob/main/DEPLOYMENT_MANUAL.md).
+
+The following video will help you to understand how the code is structured and how the service works:
+
+1. [Creating a RAG gradio service](https://youtu.be/ep-IOGHnrqg)
+
+**Tasks for you:** see [topic_3_project_task.md](https://github.com/Nebius-Academy/LLM-Engineering-Essentials/new/main/topic3/topic_3_project_task.md)
diff --git a/topic3/topic_3_project_task.md b/topic3/topic_3_project_task.md
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+# Topic 3 project task
+
+1. First, you will need a database to experiment with. The RAG project uses LanceDB, as described in the `config` file. We suggest you to work with the documentation of the `transformers` library, but you can choose a different corpus. Just make sure that your chunking is suitable for the chosen document type.
+    
+    Download the [markdown_to_text.py](https://drive.google.com/file/d/1Q6wtX9Ldu7P1BadGROW-kxeCB66fr8Y0/view) file to your VM.
+    
+    Clone `https://github.com/huggingface/transformers` to your VM and run `markdown_to_text.py` script to extract raw text from `transformers/docs/source/en/`. This is the script you need to run:
+    
+    `python prep_scripts/markdown_to_text.py --input-dir transformers/docs/source/en/ --output-dir docs`
+    
+    This will be your knowledge base, you don't need it to be a part of your repository.
+    
+    Use the `add_to_rag_db` endpoint to load every text into the database. Make several RAG API calls to check that the pipeline is working.
+    
+2. Try using a reranker. 
+    
+    The reranker is defined in the
+    
+    ```
+    rerank:
+        container_name: rerank_service
+        image: [ghcr.io/huggingface/text-embeddings-inference:cpu-1.5](http://ghcr.io/huggingface/text-embeddings-inference:cpu-1.5)
+        volumes:
+            - ./data:/data
+        command: ["--model-id", "mixedbread-ai/mxbai-rerank-base-v1"]
+    ```
+    
+    part of `rag_service/docker-compose.yaml`. As you can see, the defalt reranker is [mixedbread-ai/mxbai-rerank-base-v1](https://huggingface.co/mixedbread-ai/mxbai-rerank-base-v1). However, by default it is not used, which is established by the parameter `use_reranker: bool = False` of the constructor `class RAGRequest(BaseModel)` in the file `rag_service/src/main.py`. You need to make it `True` to switch on reranking.
+    
+    Also, you can change the parameters `top_k_retrieve`, `top_k_rank` , if necessary.
+    
+    - Compare how the retrieved context changes after adding a reranker. For that, try at least 10 different prompts. If you're generous with your time and API budget, try LLM-as-a-Judge.
+    - Measure the responce time for RAG with and without a reranker for at least 10 different prompts and for at least 3 different values of `top_k_rank`.
+    - Analyze pros and cons of using a reranker, based on relevance of the top-k documents and the response time.
+    
+3. Try at least three different LLMs and compare the results.
+    
+    As in the previous task, try at least 10 different prompts for each LLM or try LLM-as-a-Judge. 
+    
+4. Put together a simple evaluation dataset of 20 questions (+optionally answers) - you could do it manually or generate with an LLM. Use the [LLM-as-a-Judge](https://huggingface.co/learn/cookbook/en/llm_judge) approach to quantitatively evaluate your best setup. 
+    
+    [Bonus] Explore the [Ragas](https://docs.ragas.io/en/stable/) docs for possible evaluation setups.
+
+5. Try a different embedding model; for example one from the top of the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard) and justify your choice. If things are getting slow, switch to a gpu - don’t forget to switch to a corresponding [tei container](https://github.com/huggingface/text-embeddings-inference?tab=readme-ov-file#docker-images).
+    
+    The encoder is defined in this part
+    
+    ```
+    embed:
+        container_name: embed_service
+        image: [ghcr.io/huggingface/text-embeddings-inference:cpu-1.5](http://ghcr.io/huggingface/text-embeddings-inference:cpu-1.5)
+        volumes:
+            - ./data:/data
+        command: ["--model-id", "BAAI/bge-small-en-v1.5"]
+    ```
+    
+    of `rag_service/docker-compose.yaml`. As you can see, the defalt encoder is [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5).
+    
+    Analyze the relevance of the retrieved documents and how the embedding time differs between models.
+
+6. [Bonus] Adjust the RAG setup to work smoothly for a multi-turn conversation.