docs: Add articles (#70)

* docs: Add articles

* docs: prettify articles

* docs: prettify articles

README.md

@@ -19,8 +19,9 @@
     - [2.5. AWS](#25-aws)
 - [3. Install & Usage](#3-install--usage)
 - [4. Video lectures](#4-video-lectures)
-- [5. License](#5-license)
-- [6. Contributors & Teachers](#6-contributors--teachers)
+- [5. Articles](#5-articles)
+- [6. License](#6-license)
+- [7. Contributors & Teachers](#7-contributors--teachers)
 
 ------
 
@@ -223,11 +224,32 @@ Thus, check out the README for every module individually to see how to install &
 </div>
 
 
-## 5. License
+## 5. Articles
+
+`To understand the entire code step-by-step, check out our articles` ↓
+
+### System design
+- [Lesson 1: The LLMs Kit: Build a Production-Ready Real-Time Financial Advisor System Using Streaming Pipelines, RAG, and LLMOps](https://medium.com/decodingml/the-llms-kit-build-a-production-ready-real-time-financial-advisor-system-using-streaming-ffdcb2b50714)
+
+### Feature pipeline
+- [Lesson 2: Why you must choose streaming over batch pipelines when doing RAG in LLM applications](https://medium.com/decoding-ml/why-you-must-choose-streaming-over-batch-pipelines-when-doing-rag-in-llm-applications-3b6fd32a93ff)
+- [Lesson 3: This is how you can build & deploy a streaming pipeline to populate a vector DB for real-time RAG](https://medium.com/decodingml/this-is-how-you-can-build-deploy-a-streaming-pipeline-to-populate-a-vector-db-for-real-time-rag-c92cfbbd4d62)
+
+### Training pipeline
+- [Lesson 4: 5 concepts that must be in your LLM fine-tuning kit](https://medium.com/decodingml/5-concepts-that-must-be-in-your-llm-fine-tuning-kit-59183c7ce60e)
+- [Lesson 5: The secret of writing generic code to fine-tune any LLM using QLoRA](https://medium.com/decodingml/the-secret-of-writing-generic-code-to-fine-tune-any-llm-using-qlora-9b1822f3c6a4)
+- [Lesson 6: From LLM development to continuous training pipelines using LLMOps](https://medium.com/decodingml/from-llm-development-to-continuous-training-pipelines-using-llmops-a3792b05061c)
+
+### Inference pipeline
+- [Lesson 7: Design a RAG LangChain application leveraging the 3-pipeline architecture](https://medium.com/decodingml/design-a-rag-langchain-application-leveraging-the-3-pipeline-architecture-46bcc3cb3500)
+- [Lesson 8: Prepare your RAG LangChain application for production](https://medium.com/decodingml/prepare-your-rag-langchain-application-for-production-5f75021cd381)
+
+
+## 6. License
 
 This course is an open-source project released under the MIT license. Thus, as long you distribute our LICENSE and acknowledge our work, you can safely clone or fork this project and use it as a source of inspiration for whatever you want (e.g., university projects, college degree projects, etc.).
 
-## 6. Contributors & Teachers
+## 7. Contributors & Teachers
 
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