RAG update

AI/Generative AI & LLM's/W3-ReinforcementLearning=LLM-poweredApplications.md

@@ -44,4 +44,31 @@ The clarity of your instructions can make a big difference on the quality of the
 
 [Constitutional AI: Harmlessness from AI Feedback paper](images/ConstitutionalAI.pdf)
 
+## LLM Powered applications
+
+### Introduction Model optimizations for deployment
+
+Increase performance -> reduce LLM size, which reduces inference latency
+The challenge is to reduce the size of the model while still maintaining
+model performance.
+
+![Model Optimizations Techniques](images/optimizationsTechniques1.png)]
+
+[Video has a lot of information](images/ModelOptimizationsfordeployment.mp4)
+
+![Generative AI Project Lifecycle Cheat Sheet](images/GenerativeAIProjectLifecycleCheatSheet.png)
+
+~[LLM-Powered Appplication](images/PowerApplications1.png)
+
+Langchain is an example of Orchestration Library
+
+Retrieval Augmented Generation (**RAG**) is a great way to overcome the knowledge cutoff (because the world has changes since the model was trained with data current to that date) issue and help the model update its understanding of the world.
+
+![RAG](images/RAG1.png)
+
+[Facebook RAG paper](images/RAG_Paper.pdf)
+
+The external data store could be a vector store,a SQL database, CSV files, Wikis or other data storage format.
+
+![RAG](images/RAG2.png)