Vikash P

Vikash Pal is an AI/ML Engineer at ScholarEase and Editor for AIML Insights, focusing on machine learning, applied AI workflows, and practical implementation.

Dense Retrieval vs Sparse Retrieval Explained for RAG

RAG, Blog /
Dense retrieval vs sparse retrieval visual showing semantic search, keyword retrieval, embeddings, and AI search systems

Dense Retrieval vs Sparse Retrieval: Understanding Modern AI Search Systems Modern Artificial Intelligence systems increasingly depend on retrieval technologies to improve search quality, contextual understanding, and grounded response generation. Enterprise AI assistants, Retrieval-Augmented Generation (RAG) systems, semantic search platforms, and AI copilots all rely heavily on retrieval infrastructure to access relevant information efficiently. Two retrieval […]

Dense Retrieval vs Sparse Retrieval Explained for RAG Read More »

LLM Interview Questions: Top AI Job Prep Guide

Large Language Models, Blog /
LLM interview questions guide: LLM interview preparation visual showing AI job questions, coding tests, skills, and career readiness

LLM Interview Questions: Top 50 Questions & Answers for 2026 Jobs Large Language Models (LLMs) have created new job roles across AI engineering, product development, prompt engineering, research support, and enterprise automation. As hiring grows, interviews now test more than machine learning theory. Employers want candidates who understand how to build useful AI systems. This

LLM Interview Questions: Top AI Job Prep Guide Read More »

Reranking in RAG: Improve AI Retrieval and Accuracy

RAG, Blog /
Reranking in RAG visual showing semantic retrieval, AI reranking models, vector databases, and contextual relevance scoring

Reranking in RAG: How AI Retrieval Systems Improve Search Accuracy Retrieval-Augmented Generation (RAG) systems have become foundational infrastructure for modern Artificial Intelligence applications. Enterprises increasingly use RAG-powered AI assistants, enterprise search systems, customer support copilots, legal AI platforms, and document intelligence systems to improve AI accuracy and reduce hallucinations. However, even advanced semantic retrieval systems

Reranking in RAG: Improve AI Retrieval and Accuracy Read More »

Metadata Filtering in RAG: Improve AI Retrieval Accuracy

RAG, Blog /
Metadata filtering in RAG visual showing semantic retrieval, enterprise search filters, vector databases, and AI retrieval pipelines

Metadata Filtering in RAG: How AI Retrieval Systems Improve Search Precision Retrieval-Augmented Generation (RAG) systems have become foundational infrastructure for modern AI applications. Enterprises increasingly use RAG-powered AI assistants, enterprise search systems, customer support copilots, and document intelligence platforms to improve AI accuracy and reduce hallucinations. However, retrieval quality remains one of the biggest challenges

Metadata Filtering in RAG: Improve AI Retrieval Accuracy Read More »

Semantic Search vs RAG: Key AI Retrieval Differences

RAG, Blog /
Semantic search vs RAG visual showing embeddings, semantic retrieval, vector databases, and grounded AI response generation

Semantic Search vs RAG: Understanding the Key Differences in AI Retrieval Modern Artificial Intelligence systems increasingly depend on retrieval technologies to improve accuracy, contextual understanding, and enterprise knowledge access. As AI assistants, enterprise copilots, semantic search systems, and document intelligence platforms continue to evolve, two technologies appear repeatedly in modern AI discussions: Semantic Search and

Semantic Search vs RAG: Key AI Retrieval Differences Read More »

Hybrid Search in RAG: Semantic and Keyword Retrieval

RAG, Blog /
Hybrid search in RAG visual showing semantic retrieval, keyword search, embeddings, vector databases, and AI retrieval pipelines

Hybrid Search in RAG: How AI Combines Semantic and Keyword Retrieval Retrieval-Augmented Generation (RAG) systems have transformed modern Artificial Intelligence applications by enabling Large Language Models (LLMs) to retrieve external knowledge before generating responses. This retrieval layer dramatically improves factual grounding, reduces hallucinations, and enables enterprise AI systems to work with real-time information. However, retrieval

Hybrid Search in RAG: Semantic and Keyword Retrieval Read More »

LLM Roadmap for Beginners: Skills and Career Guide

Large Language Models, Blog /
LLM roadmap for beginners: LLM roadmap for beginners showing AI skills, learning milestones, projects, and career growth path

LLM Roadmap for Beginners: Step-by-Step Career Guide in 2026 Large Language Models (LLMs) are creating new opportunities across AI, software, product development, automation, and enterprise technology. Companies need people who understand how to build, use, evaluate, and deploy LLM systems. The good news: you do not need a PhD to start learning. This guide gives

LLM Roadmap for Beginners: Skills and Career Guide Read More »

Vector Database for RAG: Semantic Search Explained

RAG, Blog /
Vector database for RAG visual showing semantic search, embeddings storage, AI retrieval systems, and vector indexing

Vector Database for RAG: How AI Retrieval Systems Store and Search Knowledge Retrieval-Augmented Generation (RAG) has become one of the most important architectures in modern Artificial Intelligence systems. Enterprises increasingly rely on RAG-powered AI assistants, semantic enterprise search platforms, document retrieval systems, and intelligent chatbots to deliver more accurate and grounded responses. But behind nearly

Vector Database for RAG: Semantic Search Explained Read More »

Embeddings for RAG: Semantic Search and AI Retrieval

RAG, Blog /
Embeddings for RAG visual showing semantic search, vector embeddings, AI retrieval systems, and contextual document retrieval

Embeddings for RAG: How AI Retrieval Systems Understand Meaning Retrieval-Augmented Generation (RAG) has become one of the most important architectures in modern Artificial Intelligence systems. Enterprises increasingly rely on RAG-powered AI assistants, enterprise search systems, document retrieval platforms, and intelligent chatbots to deliver more accurate and grounded responses. But one core technology powers nearly every

Embeddings for RAG: Semantic Search and AI Retrieval Read More »

LLM Truthfulness Evaluation: Metrics and Testing Guide

Large Language Models, Blog /
llm truthfulness evaluation explained: LLM truthfulness evaluation dashboard showing fact checking, accuracy metrics, verified sources, and hallucination detection

LLM Truthfulness Evaluation: How to Measure Honest AI Outputs in 2026 Large Language Models (LLMs) can generate fluent answers in seconds, but fluency does not always equal truth. A response may sound confident while containing false facts, invented sources, or misleading reasoning. That is why LLM truthfulness evaluation has become a major priority for AI

LLM Truthfulness Evaluation: Metrics and Testing Guide Read More »

Scroll to Top