Shravan KumarMastering RAG: A Deep Dive into RetrieverHere is the sample RAG architecture.1d ago1d ago
Shravan KumarMastering RAG: A Deep Dive into EmbeddingsHere is the sample RAG architecture.1d ago1d ago
Shravan KumarMastering RAG: A Deep Dive into Text SplittingHere we will try to master with a complete deep dive on different topics which are essesntial for succesful RAG implementation. Here is the…1d ago1d ago
Shravan KumarRetrieval Augmented Generation (RAG) — Basics (Part-2)Please look at the part-1 blog here.4d ago4d ago
Shravan KumarRetrieval Augmented Generation (RAG) — Basics Part-1Here we are going to discuss on — What is RAG? How do we use it? What is the high level overview about this RAG.5d ago5d ago
Shravan KumarDifferent Chain Types using LangChainLangChain provides various chain types that allow developers to build and customize workflows for natural language processing tasks. These…6d ago6d ago
Shravan KumarDifferent Prompt Templates using LangChainIn LangChain, a Prompt Template is a structured way to define prompts that are sent to language models. Prompt Templates allow you to…Aug 12Aug 12
Shravan KumarDifferent ChatModel Applications using LangChainLangChain is a versatile library that provides a framework for building applications using large language models (LLMs). It is designed to…Aug 12Aug 12
Shravan KumarOverview on AI AgentsThere are 2 major topics we need to know about when it comes to AI Agents — They are Tools and Agents. Let us know first about Tools.Aug 1Aug 1
Shravan KumarMeta Llama 3: The most capable openly available LLM to date and It’s Application💥 𝐁𝐨𝐨𝐦 𝐢𝐧 𝐭𝐡𝐞 𝐀𝐈 𝐖𝐨𝐫𝐥𝐝: 𝐋𝐥𝐚𝐦𝐚 𝟑 𝐢𝐬 𝐇𝐞𝐫𝐞! 💥Apr 26Apr 26