Nomidl - Lets Jump into AI world

Top AI Tools for Prompt Generators in 2025

Let’s face it – staring at a blank page is nobody’s idea of fun. Whether you’re a marketer trying to craft the perfect tweet or a blogger battling writer’s block, we’ve all been there. That’s where AI prompt tools come in, and boy, have they changed the game! I’ve spent countless hours testing these tools…

Building a Production-Ready RAG Application with Milvus and LangChain

A modern approach that merges information retrieval and natural language generation to deliver precise and contextually fitting answers. Advanced techniques like query rewriting and reranking will be integrated to elevate the application’s efficiency. Furthermore, practical coding instances with sample documents will be presented to demonstrate the implementation Introduction to RAG RAG is a novel method…

Build Robust RAG System with Qdrant Vector: Advanced Techniques

Introduction Recent advances in AI & ML have significantly transformed information retrieval and data processing. Another important feature is the RAG model, which combines standardized retrieval techniques with powerful generative models to produce more accurate and contextually relevant responses. When paired with a robust vector database like Qdrant, RAG can be further optimized to handle…

Revolutionizing Query Rewrite and Extension: RAG Advanced Approach with HyDE

Introduction In the present-day times of database management and information retrieval, enhancement of query processing is very important. Techniques containing query rewrite and extension play an important role in optimizing search operations. In this article, we delve into the innovative approach of RAG (Recursive Aggregation Graph) Advanced, coupled with HyDE, exploring its principles, applications, advantages…

Leveraging RAG Rerank Technique for Prompt Compression and Retrieving Correct Responses

Introduction: The utilization of Large Language Models has increased across various domains of natural language processing. As these models develop, their increased size and complexity present important challenges concerning efficiency, prompt interaction, and response accuracy. Addressing these challenges, the RAG rerank technique emerges as a crucial solution, combining the strengths of retrieval and generation models.…

Introduction to Retrieval Augmented Generation (RAG)

In today’s field of artificial intelligence, where language models are highly valued, one of the most critical requirements is to ensure that the answers generated can be reliably accurate. Retrieval Augmented Generation (RAG) is an innovative artificial intelligence system that aims to improve the quality of responses produced by LLM using additional data sources. But…

Document Loaders in Langchain

In this article, we will be looking at multiple ways which langchain uses to load document to bring information from various sources and prepare it for processing. These loaders act like data connectors, fetching information and converting it into a format Langchain understands. There are a lot of document loaders in LangChain and you can…

A Practitioners Guide to Retrieval Augmented Generation (RAG)

“The power of artificial intelligence is the power to transform humans.” – Fei-Fei Li Welcome to our comprehensive guide on Retrieval Augmented Generation (RAG), a revolutionary technique that combines powerful search capabilities with generative AI to enhance AI systems like langchain, a leading large language model. In this guide, we will explore the concept of…

Building First Prompt Templates with OpenAI Language Models

Introduction In this blog post, we will explore how to build your first prompt and prompt templates using OpenAI Language Models. Language models are powerful tools that can generate text based on the input provided to them. By creating prompts and prompt templates, we can guide the language models to generate specific types of text…

Exploring LangChain: The Framework for Building AI Applications

In this article we will be exploring the framework that we can use to integrate AI intoour applications and build AI-powered applications. AI is being widely used, and it is the future, regardless of the type of application. I’llalso explain the kinds of applications you can build with AI. Integrating AI hasbecome a must to…