RAG

A cascade of offers from OpenAI, Google, and Perplexity—amplified by Airtel and Reliance Jio—signals a deliberate push to convert India’s scale into durable AI usage, data, and future revenue. With more than 900 million internet users, rock-bottom mobile data prices, and a young, mobile-first population, India offers the world’s deepest top-of-funnel for AI adoption. Giving away premium access—such as a year of ChatGPT’s low-cost “Go” tier, Jio’s bundling of Gemini, or Airtel’s tie-up with Perplexity Pro—maximizes trial, habituation, and data collection across diverse languages and contexts. Even a low single-digit conversion rate translates into millions of subscribers, while non-converters still contribute valuable signals that improve models.
Telus is in active talks to bring partners into its data-centre and AI business, signaling a capital-light approach to scale sovereign AI compute in Canada. Partner capital can accelerate GPU procurement, facility buildouts, and interconnect investments while aligning with customers that require sovereign environments distinct from hyperscale public clouds. Management addressed investor concerns about potential AI compute oversupply by emphasizing a modular build strategy, adding capacity in phases as demand materializes. The timing aligns with tightening data-residency requirements, heightened AI adoption, and demand for local alternatives to U.S.-centric infrastructure. This reduces stranded capital risk in a market with volatile GPU supply, rapid chip roadmaps, and evolving workload profiles.
Snap and Perplexity are joining forces to embed a conversational AI search experience directly into Snapchat’s chat interface, signaling a new distribution model for AI and a fresh monetization path for social platforms. Perplexity will integrate its AI-powered answer engine natively into Snapchat, beginning a global rollout in early 2026. Under the agreement, Perplexity will pay Snap $400 million over one year, via a mix of cash and equity, as the integration scales. Snap expects revenue contribution from the partnership to begin in 2026. The move is notable as Snap’s first large-scale integration of an external AI partner directly in-app.
Qualcomm is moving from mobile NPUs into rack-scale AI infrastructure, positioning its AI200 (2026) and AI250 (2027) to challenge Nvidia/AMD on the economics of large-scale inference. The company is translating its Hexagon neural processing unit heritage—refined across phones and PCs—into data center accelerators tuned for inferencing, not training. AI200 and AI250 will ship in liquid-cooled, rack-scale configurations designed to operate as a single logical system. Qualcomm is leaning into that constraint with a redesigned memory subsystem and high-capacity cards supporting up to 768 GB of onboard memory—positioning that as a differentiator versus current GPU offerings.
Unlike metaverse-era headsets that leaned on entertainment and novelty, Galaxy XR makes Google’s Gemini the front door to the interface. In demos, Gemini orchestrated windows in a spatial workspace, answered context-aware questions, and invoked creative tools like Veo for AI-generated video. That tight AI integration is the strategic wedge: Samsung and Google position XR as a bridge to slim, everyday AI glasses developed with eyewear brands Warby Parker and Gentle Monster. The message to developers and enterprises is clear—design for multimodal AI agents first; the form factor will shrink later.
Apple’s new M5 chip is a material step in local AI compute that will ripple into enterprise IT, developer tooling, and edge networking strategies. M5 is built on a third‑generation 3‑nanometer process and reworks Apple’s GPU as the center of gravity for AI. The 10‑core GPU adds a dedicated Neural Accelerator in every core, pushing peak GPU compute for AI to more than four times M4. Unified memory bandwidth jumps to 153 GB/s, and configurations with up to 32 GB allow more and larger models to remain entirely on device. On‑device inference is moving from nice‑to‑have to default, driven by privacy, latency, and cost.
The AI value gap is widening—and it’s now a strategy problem, not a tooling problem. Fresh research shows a small cohort of “future-built” companies converting AI into material P&L impact while most firms lag despite sizable spend. BCG’s 2025 assessment of 1,250 senior executives finds only 5% of companies have the capabilities to consistently generate outsized AI value, with 35% scaling and beginning to see benefits, and a full 60% reporting little to no financial impact to date.
South Korea is funding a national AI stack to reduce dependence on foreign models, protect data, and tune AI to its language and industries. The government has committed ₩530 billion (about $390 million) to five companies building large-scale foundation models: LG AI Research, SK Telecom, Naver Cloud, NC AI, and Upstage. Progress will be reviewed every six months, with underperformers cut and resources concentrated on the strongest until two leaders remain. The policy goal is clear: build world-class, Korean-first AI capability that supports national security, economic competitiveness, and data sovereignty. For telecoms and enterprise IT, this is a shift from “consume global models” to “operate domestic AI platforms” integrated with local data, compliance, and services.
SK Telecom has been named OpenAI’s exclusive B2C partner among Korean carriers as OpenAI opens its Korea office, signaling an aggressive push to scale consumer AI access and localize go-to-market in a strategically important market. The two companies unveiled a promotion for ChatGPT Plus, giving new or returning subscribers who purchase one month two additional months at no cost. While the immediate focus is consumer-facing, SK Telecom indicates the partnership will extend toward business services and potential collaborations across the broader SK Group.
Microsoft is preparing to license Anthropic’s Claude models for Microsoft 365, signaling a multi-model strategy that reduces exclusive reliance on OpenAI across Word, Excel, Outlook, and PowerPoint. According to multiple reports, Microsoft plans to integrate Anthropic’s Claude Sonnet 4 alongside OpenAI’s models to power Microsoft 365 Copilot features, including content generation and slide design in PowerPoint. This is a notable pivot from a single-model default to a best-of-breed approach that routes tasks to the model that performs best for a given function. For enterprises, especially in regulated and mission-critical domains like telecom, the shift implies more resilience, better accuracy for specialized tasks, and new options to optimize for quality, cost, and latency.
Cisco’s Secure AI Factory with NVIDIA, now integrated with VAST Data’s InsightEngine, targets the core blocker to agentic AI at scale: getting proprietary data to models quickly, securely, and at enterprise breadth. The new joint solution aims to collapse RAG pipeline delays from minutes to seconds, reduce integration risk with validated reference designs, and keep every interaction within security and compliance controls. By aligning Cisco’s AI PODs, NVIDIA’s AI Data Platform and DPUs, and VAST’s data intelligence layer, the offering provides a turnkey workload data fabric for production-grade AI agents. Cisco AI PODs now ship with VAST InsightEngine using NVIDIA’s AI Data Platform reference design, turning raw enterprise data into AI-ready indices and vectors in near real time.
AI agents are transforming industries in 2025, but scaling them efficiently without Large Language Models (LLMs) is impossible. LLMs provide critical capabilities such as reasoning, knowledge retrieval, and contextual understanding that power AI automation. This detailed article explores why LLMs are essential for AI agents, the role of Retrieval-Augmented Generation (RAG), optimization strategies, and the best free resources to master LLMs.

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