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RAG Explained: What Is Retrieval-Augmented Generation?

How To Buy Saas

Retrieval-Augmented Generation (RAG) is a cutting-edge approach in AI that combines large language models (LLMs) with real-time information retrieval to produce more accurate and context-aware outputs. Industry leaders have quickly embraced RAG as a way to build more intelligent AI applications. and real SaaS examples using RAG.

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GTM 139: AI Agents Are Changing Everything — Microsoft’s VP of AI Agents on the New Era of Work and Software | Ray Smith

Sales Hacker

The GTM Podcast is available on any major directory, including: Apple Podcasts Spotify YouTube Ray Smith is the VP of AI Agents at Microsoft. Ray breaks down why the rise of AI agents is a tectonic shift, how businesses are already seeing ROI, and what it means for SaaS, team structure, and go-to-market strategies.

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Best Applicant Tracking Systems for 2025

How To Buy Saas

Importantly, ATS platforms have evolved with AI-driven features , diversity and bias reduction tools , and deep analytics to meet todays hiring challenges. Manatal Best AI-Powered ATS for HR Teams Pricing: Key Features: Ideal Use Case: 5. Greenhouse Best ATS for Data-Driven Recruiting Pricing: Key Features: Ideal Use Case: 7.

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The 3 Best Analytics Companies of 2020

Neil Patel

Your partner will help you come up with winning ad ideas that are rooted in data. SEO – Analyzing the best keywords to target, as well as writing great pieces of content to rank in search engines, similarly reflects the importance of a holistic analytics partner. Data management and reporting.

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Top LLMs in 2025: Best Large Language Models for AI, SaaS & Development

How To Buy Saas

In 2025, foundation models or generative AIs like GPT-4, Claude, Gemini, and open-source LLaMA are reshaping AI research, software development, and SaaS products. They differ in size, training data, capabilities, and openness. They differ in size, training data, capabilities, and openness. Early tests showed Gemini 2.5

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How to Become a Data Scientist [+Tools and Resources]

User Pilot

Businesses need data scientists to make sense of it all and turn it into actionable insights. Data scientist’s main responsibilities The three responsibility pillars of a data scientist encompass Data Acquisition and Engineering, Data Analysis and Modeling, and Communication and Collaboration.

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Data Scientist Career Path

User Pilot

Experience with data visualization tools (e.g., A passion for data-driven problem-solving and a strong work ethic. Bonus points : Experience with cloud platforms (AWS, Azure, GCP). Experience with big data technologies (Hadoop, Spark). Tableau, Power BI). Excellent communication and collaboration skills.

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