Update README.md

README.md

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----
-title: RAG Implementation Notebook
-emoji: 🔍
-colorFrom: blue
-colorTo: purple
-sdk: gradio
-sdk_version: 3.50.2
-app_file: app.py
-pinned: false
----
-
-# RAG Implementation Notebook
-
-This space contains a Jupyter notebook demonstrating a Retrieval Augmented Generation (RAG) implementation using OpenAI's API and Hugging Face models.
-
-## Features
-- PDF document processing
-- Text chunking and embedding
-- Vector database implementation
-- RAG pipeline with context-aware responses
-
-## How to Use
-1. Clone this repository
-2. Install the requirements: `pip install -r requirements.txt`
-3. Open the notebook: `jupyter notebook Pythonic_RAG_Assignment.ipynb`
-
-## Requirements
-See `requirements.txt` for the complete list of dependencies.
-
-# 🧑‍💻 What is [AI Engineering](https://maven.com/aimakerspace/ai-eng-bootcamp)?
-
-AI Engineering refers to the industry-relevant skills that data science and engineering teams need to successfully **build, deploy, operate, and improve Large Language Model (LLM) applications in production environments**.  
-
-In practice, this requires understanding both prototyping and production deployments.
-
-During the *prototyping* phase, Prompt Engineering, Retrieval Augmented Generation (RAG), Agents, and Fine-Tuning are all necessary tools to be able to understand and leverage. Prototyping includes:
-1. Building RAG Applications
-2. Building with Agent and Multi-Agent Frameworks
-3. Fine-Tuning LLMs & Embedding Models
-4. Deploying LLM Prototype Applications to Users
-
-When *productionizing* LLM application prototypes, there are many important aspects ensuring helpful, harmless, honest, reliable, and scalable solutions for your customers or stakeholders. Productionizing includes:
-1. Evaluating RAG and Agent Applications
-2. Improving Search and Retrieval Pipelines for Production
-3. Monitoring Production KPIs for LLM Applications
-4. Setting up Inference Servers for LLMs and Embedding Models
-5. Building LLM Applications with Scalable, Production-Grade Components
-
-This bootcamp builds on our two previous courses, [LLM Engineering](https://maven.com/aimakerspace/llm-engineering) and [LLM Operations](https://maven.com/aimakerspace/llmops) 👇
-
-- Large Language Model Engineering (LLM Engineering) refers to the emerging best-practices and tools for pretraining, post-training, and optimizing LLMs prior to production deployment.  Pre- and post-training techniques include unsupervised pretraining, supervised fine-tuning, alignment, model merging, distillation, quantization. and others.
-    
-- Large Language Model Ops (LLM Ops, or LLMOps (as from [WandB](https://docs.wandb.ai/guides/prompts) and [a16z](https://a16z.com/emerging-architectures-for-llm-applications/))) refers to the emerging best-practices, tooling, and improvement processes used to manage production LLM applications throughout the AI product lifecycle.  LLM Ops is a subset of Machine Learning Operations (MLOps) that focuses on LLM-specific infrastructure and ops capabilities required to build, deploy, monitor, and scale complex LLM applications in production environments.  _This term is being used much less in industry these days._
-
-# 🏆 **Grading and Certification**
-
-To become **AI-Makerspace Certified**, which will open you up to additional opportunities for full and part-time work within our community and network, you must:
-
-1. Complete all project assignments.
-2. Complete a project and present during Demo Day.
-3. Receive at least an 85% total grade in the course.
-
-If you do not complete all assignments, participate in Demo Day, or maintain a high-quality standard of work, you may still be eligible for a *certificate of completion* if you miss no more than 2 live sessions.
-
-# 📚 About
-
-This GitHub repository is your gateway to mastering the art of AI Engineering.  ***All assignments for the course will be released here for your building, shipping, and sharing adventures!***
-
-# 🙏 Contributions
-
-We believe in the power of collaboration. Contributions, ideas, and feedback are highly encouraged! Let's build the ultimate resource for AI Engineering together.
-
-Please to reach out with any questions or suggestions. 
-
-Happy coding! 🚀🚀🚀
-
+---
+title: RAG Implementation Notebook
+emoji: 🔍
+colorFrom: blue
+colorTo: purple
+sdk: gradio
+sdk_version: 5.23.3
+app_file: app.py
+pinned: false
+---
+
+# RAG Implementation Notebook
+
+This space contains a Jupyter notebook demonstrating a Retrieval Augmented Generation (RAG) implementation using OpenAI's API and Hugging Face models.
+
+## Features
+- PDF document processing
+- Text chunking and embedding
+- Vector database implementation
+- RAG pipeline with context-aware responses
+
+## How to Use
+1. Clone this repository
+2. Install the requirements: `pip install -r requirements.txt`
+3. Open the notebook: `jupyter notebook Pythonic_RAG_Assignment.ipynb`
+
+## Requirements
+See `requirements.txt` for the complete list of dependencies.
+
+# 🧑‍💻 What is [AI Engineering](https://maven.com/aimakerspace/ai-eng-bootcamp)?
+
+AI Engineering refers to the industry-relevant skills that data science and engineering teams need to successfully **build, deploy, operate, and improve Large Language Model (LLM) applications in production environments**.  
+
+In practice, this requires understanding both prototyping and production deployments.
+
+During the *prototyping* phase, Prompt Engineering, Retrieval Augmented Generation (RAG), Agents, and Fine-Tuning are all necessary tools to be able to understand and leverage. Prototyping includes:
+1. Building RAG Applications
+2. Building with Agent and Multi-Agent Frameworks
+3. Fine-Tuning LLMs & Embedding Models
+4. Deploying LLM Prototype Applications to Users
+
+When *productionizing* LLM application prototypes, there are many important aspects ensuring helpful, harmless, honest, reliable, and scalable solutions for your customers or stakeholders. Productionizing includes:
+1. Evaluating RAG and Agent Applications
+2. Improving Search and Retrieval Pipelines for Production
+3. Monitoring Production KPIs for LLM Applications
+4. Setting up Inference Servers for LLMs and Embedding Models
+5. Building LLM Applications with Scalable, Production-Grade Components
+
+This bootcamp builds on our two previous courses, [LLM Engineering](https://maven.com/aimakerspace/llm-engineering) and [LLM Operations](https://maven.com/aimakerspace/llmops) 👇
+
+- Large Language Model Engineering (LLM Engineering) refers to the emerging best-practices and tools for pretraining, post-training, and optimizing LLMs prior to production deployment.  Pre- and post-training techniques include unsupervised pretraining, supervised fine-tuning, alignment, model merging, distillation, quantization. and others.
+    
+- Large Language Model Ops (LLM Ops, or LLMOps (as from [WandB](https://docs.wandb.ai/guides/prompts) and [a16z](https://a16z.com/emerging-architectures-for-llm-applications/))) refers to the emerging best-practices, tooling, and improvement processes used to manage production LLM applications throughout the AI product lifecycle.  LLM Ops is a subset of Machine Learning Operations (MLOps) that focuses on LLM-specific infrastructure and ops capabilities required to build, deploy, monitor, and scale complex LLM applications in production environments.  _This term is being used much less in industry these days._
+
+# 🏆 **Grading and Certification**
+
+To become **AI-Makerspace Certified**, which will open you up to additional opportunities for full and part-time work within our community and network, you must:
+
+1. Complete all project assignments.
+2. Complete a project and present during Demo Day.
+3. Receive at least an 85% total grade in the course.
+
+If you do not complete all assignments, participate in Demo Day, or maintain a high-quality standard of work, you may still be eligible for a *certificate of completion* if you miss no more than 2 live sessions.
+
+# 📚 About
+
+This GitHub repository is your gateway to mastering the art of AI Engineering.  ***All assignments for the course will be released here for your building, shipping, and sharing adventures!***
+
+# 🙏 Contributions
+
+We believe in the power of collaboration. Contributions, ideas, and feedback are highly encouraged! Let's build the ultimate resource for AI Engineering together.
+
+Please to reach out with any questions or suggestions. 
+
+Happy coding! 🚀🚀🚀