Gemini API Features

The release of Google's Gemini Pro 1.5 is, IMO, the biggest piece of A.I. news yet this year. The LLM has a gigantic million-token context window, multimodal inputs (text, code, image, audio, video) and GPT-4-like capabilities despite being much smaller and faster. Key Features 1. Despite being a mid-size model (so much faster and cheaper), its capabilities rival the full-size models Gemini Ultra 1.0 and GPT-4, which are the two most capable LLMs available today. 2. At a million tokens, its context window demolishes Claude 2, the foundation LLM with the next longest context window (Claude 2's is only a fifth of the size at 200k). A million tokens corresponds to 700,000 words (seven lengthy novels) and Gemini Pro 1.5 accurately retrieves needles from this vast haystack 99% of the time! 3. Accepts text, code, images, audio (a million tokens corresponds to 11 hours of audio), and video (1MM tokens = an hour of video). Today's episode contains an example of Gemini Pro 1.5 answering my questions about a 54-minute-long video with astounding accuracy and grace. How did Google pull this off? • Gemini Pro 1.5 is a Mixture-of-Experts (MoE) architecture, routing your input to specialized submodels (e.g., one for math, one for code, etc.), depending on the broad topic of your input. This allows for focused processing and explains both the speed gains and high capability level despite being a mid-size model. • While OpenAI also uses the MoE approach in GPT-4, Google seems to have achieved greater efficiency with the approach. This edge may stem from Google's pioneering work on MoE (Google were the first to publish on MoE, way back in 2017) and their resultant deep in-house expertise on the topic. • Training-data quality is also a likely factor in Google's success. What's next? • Google has 10-million-token context-windows in testing. That order-of-magnitude jump would correspond to future Gemini releases being able to handle ~70 novels, 100 hours of audio or 10 hours of video. • If Gemini Pro 1.5 can achieve GPT-4-like capabilities, the Gemini Ultra 1.5 release I imagine is in the works may allow Google to leapfrog OpenAI and reclaim their crown as the world's undisputed A.I. champions (unless OpenAI gets GPT-5 out first)! Want access? • Gemini Pro 1.5 is available with a 128k context window through Google AI Studio and (for enterprise customers) through Google Cloud's Vertex AI. • There's a waitlist for access to the million-token version (I had access through the early-tester program). Check out today's episode (#762) for more detail on all of the above (including Gemini 1.5 Pro access/waitlist links). The Super Data Science Podcast is available on all major podcasting platforms and a video version is on YouTube. #superdatascience #machinelearning #ai #llms #geminipro #geminiultra

𝐌𝐨𝐬𝐭 𝐩𝐞𝐨𝐩𝐥𝐞 𝐮𝐬𝐞 𝐆𝐞𝐦𝐢𝐧𝐢 𝐟𝐨𝐫 𝐬𝐢𝐦𝐩𝐥𝐞 𝐩𝐫𝐨𝐦𝐩𝐭𝐬… 𝐛𝐮𝐭 𝐢𝐭𝐬 𝐫𝐞𝐚𝐥 𝐩𝐨𝐰𝐞𝐫 𝐜𝐨𝐦𝐞𝐬 𝐟𝐫𝐨𝐦 𝐬𝐭𝐚𝐜𝐤𝐢𝐧𝐠 𝐢𝐭𝐬 𝐬𝐤𝐢𝐥𝐥𝐬 𝐭𝐨𝐠𝐞𝐭𝐡𝐞𝐫. This Gemini Skill Blueprint breaks down every major capability - what it does, when to use it, and why it gives you an unfair productivity advantage. 𝐇𝐞𝐫𝐞’𝐬 𝐰𝐡𝐚𝐭 𝐆𝐞𝐦𝐢𝐧𝐢 𝐜𝐚𝐧 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐝𝐨 𝐰𝐡𝐞𝐧 𝐮𝐬𝐞𝐝 𝐭𝐨 𝐢𝐭𝐬 𝐟𝐮𝐥𝐥 𝐩𝐨𝐭𝐞𝐧𝐭𝐢𝐚𝐥: - Gemini Apps → Build custom internal tools, assistants, dashboards, and automations using Workspace + APIs. - Advanced Search → Real-time, verified insights with AI reasoning (perfect for research and trend analysis). - Workspace Integration → Use Gemini across Docs, Sheets, Gmail, Drive, and Calendar to turn Google Workspace into an AI-powered system. - Multimodal Reasoning → Upload PDFs, images, code, audio, or videos and get structured analysis instantly. - Data Analysis Mode → Detect anomalies, create charts, summarize insights, and forecast KPIs. - Workspace Automations → Auto-organize files, update Sheets, clean your inbox, schedule tasks, and more. - Code Assist → Debug, write, and optimize code across languages. - Deep Research Mode → Long-form, citation-backed reasoning for competitor research, reports, or market analysis. - File Intelligence → Extract insights from complex documents (contracts, spreadsheets, presentations). - Multilingual Edge → Translate, localize, and rewrite content with cultural accuracy. - Memory (Coming Soon) → Persistent preferences and personalized outputs. - Collaboration Mode → Co-edit documents, brainstorm, and build presentations in real time. - Extensions & APIs → Pull live data from Maps, YouTube, Flights, and more. - Instant Actions → One-click commands like “clean inbox,” “organize Drive,” or “create project plan.” 𝐓𝐡𝐞 𝐁𝐥𝐮𝐞𝐩𝐫𝐢𝐧𝐭 𝐚𝐥𝐬𝐨 𝐢𝐧𝐜𝐥𝐮𝐝𝐞𝐬 𝐏𝐨𝐰𝐞𝐫 𝐓𝐢𝐩𝐬 𝐥𝐢𝐤𝐞: • Stack features for maximum efficiency • Upload before asking for richer results • Turn processes into Gemini Apps • Treat Gemini like a teammate, not a tool If you want to work faster, think clearer, and automate more in 2026, this is the roadmap you need. ♻️ Repost this to help your network get started ➕ Follow Prem N. for more

Two months ago, I ran a workshop for non-technical folks on how to start vibe-coding, building small apps and prototypes without getting lost in advanced dev tools like Replit or Lovable. My advice back then: before diving into any platform, spend one focused hour learning what AI tools like ChatGPT, Claude, Copilot, or Gemini can actually do for you. Understand things like: 🔎 Search vs. Deep Search. When to use each. 🪟 Context windows. How much the AI can “remember” in a single chat, and how that shapes the results. 🤔 Choosing thinking vs. non-thinking models. When speed beats depth (though GPT-5 removed this week 😥 🤖 Built-in mini-apps: Copilot Agents, Artifacts, GPTs, Gems (different names, same idea). 👨🎨 Canvas UIs for coding. Editing and chatting with your code side-by-side. Fast-forward to this week: I spent a few late nights building a hackathon prototype using Claude and Gemini… and the advice feels even more relevant now. Some takeaways: 🪟 Claude is amazing for coding… until you hit its limited context window on the free tier. Once the chat’s full, you’re stuck copy-pasting your code into a fresh conversation every time. 📚 Gemini Pro gives you 1 million tokens (~1,500 text pages). Unless you’re writing a novel, that’s more than enough. 🤝 Gemini can now call its own API in your generated code and even has a “+ Add Gemini features” button for your app. 👨🎨The Canvas UI is great: you can tweak code and ask questions in an overlay box without starting over. Pro tip: If you only want a small change or an explanation, tell Gemini “don’t rewrite the whole code” or it might redo everything. If you’ve been curious about AI-assisted coding, grab a free evening and try your favorite model. You’ll be surprised how far you can get in just a few hours. I also highly encourage people to watch this video from Andrej Karpathy https://lnkd.in/eceviTZE on how to use LLMs

🚀 Combining Large Context Window Models with Graph RAG for State-of-the-Art AI Integrations 🌐 As AI systems evolve, handling complex datasets and interconnected knowledge requires innovative approaches. In my latest blog, I explore how large context window models like Gemini 1.5 Pro combined with Graph Retrieval-Augmented Generation (Graph RAG) and context caching can strike the right balance between performance, latency, cost, and accuracy. 🔍 Key Takeaways: Improved Latency: Large context windows reduce the need for repeated queries, speeding up response times. Reduced Token Costs: Cache the graph and pass it through the large context window to minimize token usage. Better Contextual Reasoning: Hold more relationships in memory for accurate multi-hop reasoning in complex domains. 💡 Best Practices: Use pre-constructed graphs for static knowledge sources. Leverage context caching for frequently accessed data to improve efficiency. In dynamic domains, employ a hybrid approach with real-time updates for relevant sections of the graph. 🎯 Real-World Applications: Customer Support Systems: Cache frequently asked questions for quicker resolutions and reduced costs. E-Commerce Recommendations: Combine static product catalogs with real-time updates based on user behavior. Scientific Research: Connect related findings for faster and more accurate reasoning. By combining Graph RAG, Text2Emb, and Gemini for large context windows, we can achieve more powerful and dynamic retrieval, reducing latency and costs while enhancing AI's reasoning capabilities. 🧠 The future of AI lies in fine-tuning these approaches for specific use cases—be it real-time analytics, healthcare, or legal research. Check out the full blog for a deeper dive into best practices, trade-offs, and real-world use cases. 👇 #AI #MachineLearning #GraphRAG #GenerativeAI #AIResearch #ContextCaching #LargeContextModels #Gemini15Pro #AIIntegration

If you want to master the Gemini AI Ecosystem here is the guide Google forgot to give us on the 25 tools, models, workflows, prompts, and agents that produce awesome results. Google did a weak job teaching people about their new AI products, so most users only touch the chat box. The real leverage comes from better models, better workspaces, and agentic execution. Gemini is not one product. It’s an ecosystem of models, workspaces, grounding tools, creative engines, and agent systems. The attached PDF is my practical guide and visual cheat sheet. The hidden Gemini toolbox (use this as a checklist) You do not need all of these. You need the right 5 for your job. 1) Models and thinking modes Gemini 3 Fast Gemini 3 Thinking Gemini 3 Pro Gemini 3 Deep Think Thinking Time modes: Fast, Thinking, Deep Think Rule of thumb Fast = quick drafts, simple Q&A, routine rewrites Thinking = planning, multi-step tasks, solid reasoning Deep Think = hard logic, debugging, high-stakes decisions (the PDF frames this as slowing down to be right) 2) Context and grounding (how you reduce hallucinations) - 1M+ token context window (handles 5X the inputs of other tools like ChatGPT) - Native multimodality: text, code, audio, video - NotebookLM for research and an amazing content studio 3) Build and ship outputs (where work becomes real) - Vibe coding: describe it, build it - Gemini Canvas split-screen workspace: draft + output side-by-side - Canvas: automatic slide decks - Canvas: web prototyping - Canvas: visual infographics - AI Studio for building apps - Dynamic View for dashboards / interactive apps - Visual Layout for magazine-style designs 4) Research that does not fall apart - Deep Research: autonomous analyst workflow (plan → browse → synthesize) - Fan-Out Search AI Mode for complex questions - NotebookLM: instant citations and grounded summaries 5) Creative production - Imagen 4 for photorealistic images - Veo 3.1 for video generation - Nano Banana Pro for typography + brand consistency - Grounding in Image Gen for strict brand consistency 6) Reusable specialists and agents - Gemini Gems: reusable specialists you build once - Agent Mode: autonomous multi-step work - Antigravity platform for orchestrating agents (research agent, coding agent, review agent; research → plan → execute → iterate) - AI Studio for building apps and API access The CPFO prompt framework (this is the prompt upgrade that works) Context + Persona + Format + Objective Check out the complete guide attached! ♻️ Like, Repost and share with your network to help everyone learn how to AI