RAG

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Rethinking Enterprise Search: How Cortex Search Turns Data into Business Impact 

ai, AI (Artificial Intelligence), Artificial Intelligence, Beginner, RAG

According to Stack Overflow and Atlassian, developers lose between 6 and 10 hours every week searching for information or clarifying unclear documentation. For a 50-developer team, that adds up to $675,000–$1.1 million in wasted productivity every year. This is not just a tooling issue. It is a retrieval problem.Enterprises have plenty of data but lack […]

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Fine-Tuning vs RAG vs Prompt Engineering 

ai, AI (Artificial Intelligence), Artificial Intelligence, Beginner, prompt engineering, RAG

AI demos often look impressive, delivering fast responses, polished communication, and strong performance in controlled environments. But once real users interact with the system, issues surface like hallucinations, inconsistent tone, and answers that should never be given. What seemed ready for production quickly creates friction and exposes the gap between demo success and real-world reliability.

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How BM25 and RAG Retrieve Information Differently?

ai, AI (Artificial Intelligence), Artificial Intelligence, Editors Pick, RAG, Staff, Technology, Tutorials

When you type a query into a search engine, something has to decide which documents are actually relevant — and how to rank them. BM25 (Best Matching 25), the algorithm powering search engines like Elasticsearch and Lucene, has been the dominant answer to that question for decades.  It scores documents by looking at three things:

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PageIndex vs Traditional RAG: A Better Way to Build Document Chatbots

ai, AI (Artificial Intelligence), Artificial Intelligence, Beginner, chatbot, RAG

What if the way we build AI document chatbots today is flawed? Most systems use RAG. They split documents into chunks, create embeddings, and retrieve answers using similarity search. It works in demos but often fails in real use. It misses obvious answers or picks the wrong context. Now there is a new approach called

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RAG vs. Context Stuffing: Why selective retrieval is more efficient and reliable than dumping all data into the prompt

agentic ai, ai, AI (Artificial Intelligence), Artificial Intelligence, Editors Pick, RAG, Staff, Technology

Large context windows have dramatically increased how much information modern language models can process in a single prompt. With models capable of handling hundreds of thousands—or even millions—of tokens, it’s easy to assume that Retrieval-Augmented Generation (RAG) is no longer necessary. If you can fit an entire codebase or documentation library into the context window,

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VectifyAI Launches Mafin 2.5 and PageIndex: Achieving 98.7% Financial RAG Accuracy with a New Open-Source Vectorless Tree Indexing.

agentic ai, ai, AI (Artificial Intelligence), AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Machine Learning, New Releases, RAG, Tech News, Technology

Building a Retrieval-Augmented Generation (RAG) pipeline is easy; building one that doesn’t hallucinate during a 10-K audit is nearly impossible. For devs in the financial sector, the ‘standard’ vector-based RAG approach—chunking text and hoping for the best—often results in a ‘text soup’ that loses the vital structural context of tables and balance sheets. VectifyAI is

VectifyAI Launches Mafin 2.5 and PageIndex: Achieving 98.7% Financial RAG Accuracy with a New Open-Source Vectorless Tree Indexing. Read More »

Google AI Releases Gemini 3.1 Pro with 1 Million Token Context and 77.1 Percent ARC-AGI-2 Reasoning for AI Agents

agentic ai, ai, AI (Artificial Intelligence), AI Agents, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Machine Learning, New Releases, RAG, Tech News, Technology

Google has officially shifted the Gemini era into high gear with the release of Gemini 3.1 Pro, the first version update in the Gemini 3 series. This release is not just a minor patch; it is a targeted strike at the ‘agentic’ AI market, focusing on reasoning stability, software engineering, and tool-use reliability. For devs,

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A Coding Implementation to Design a Stateful Tutor Agent with Long-Term Memory, Semantic Recall, and Adaptive Practice Generation

agentic ai, ai, AI (Artificial Intelligence), AI Agents, Artificial Intelligence, Editors Pick, RAG, Technology, Tutorials

In this tutorial, we build a fully stateful personal tutor agent that moves beyond short-lived chat interactions and learns continuously over time. We design the system to persist user preferences, track weak learning areas, and selectively recall only relevant past context when responding. By combining durable storage, semantic retrieval, and adaptive prompting, we demonstrate how

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RAG vs. Fine-Tuning: Which One Suits Your LLM?

ai, AI (Artificial Intelligence), Artificial Intelligence, Large Language Models, LLM, RAG, Shaip Blogs

Large Language Models (LLMs) such as GPT-4 and Llama 3 have affected the AI landscape and performed wonders ranging from customer service to content generation. However, adapting these models for specific needs usually means choosing between two powerful techniques: Retrieval-Augmented Generation (RAG) and fine-tuning. While both these approaches enhance LLMs, they are articulate towards different

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40 RAG Interview Questions and Answers

ai, AI (Artificial Intelligence), Artificial Intelligence, Beginner, Interview Prep, interview questions, RAG

Retrieval-Augmented Generation, or RAG, has become the backbone of most serious AI systems in the real world. The reason is simple: large language models are great at reasoning and writing, but terrible at knowing the objective truth. RAG fixes that by giving models a live connection to knowledge. What follows are interview-ready question that could

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