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Fast Company
04-06-2025
- Business
- Fast Company
Optimizing AI apps in a million-token world
The context size problem in large language models is nearly solved. In recent months, models like GPT-4.1, LLaMA 4, and DeepSeek V3 have reached context windows ranging from hundreds of thousands to millions of tokens. We're entering a phase where entire documents, threads, and histories can fit into a single prompt. It marks real progress—but it also brings new questions about how we structure, pass, and prioritize information. WHAT IS CONTEXT SIZE (AND WHY WAS IT A CHALLENGE)? Context size defines how much text a model can process in one go, and is measured in tokens, which are small chunks of text, like words or parts of words. It shaped the way we worked with LLMs: splitting documents, engineering recursive prompts, summarizing inputs—anything to avoid truncation. Now, models like LLaMA 4 Scout can handle up to 10 million tokens, and DeepSeek V3 and GPT-4.1 go beyond 100K and 1M respectively. With those capabilities, many of those older workarounds can be rethought or even removed. FROM BOTTLENECK TO CAPABILITY This progress unlocks new interaction patterns. We're seeing applications that can reason and navigate across entire contracts, full Slack threads, or complex research papers. These use cases were out of reach not long ago. However, just because models can read more does not mean they automatically make better use of that data. The paper ' Why Does the Effective Context Length of LLMs Fall Short? ' examines this gap. It shows that LLMs often attend to only part of the input, especially the more recent or emphasized sections, even when the prompt is long. Another study, ' Explaining Context Length Scaling and Bounds for Language Models,' explores why increasing the window size does not always lead to better reasoning. Both pieces suggest that the problem has shifted from managing how much context a model can take to guiding how it uses that context effectively. Think of it this way: Just because you can read every book ever written about World War I doesn't mean you truly understand it. You might scan thousands of pages, but still fail to retain the key facts, connect the events, or explain the causes and consequences with clarity. What we pass to the model, how we organize it, and how we guide its attention are now central to performance. These are the new levers of optimization. CONTEXT WINDOW ≠ TRAINING TOKENS A model's ability to accept a large context does not guarantee that it has been trained to handle it well. Some models were exposed only to shorter sequences during training. That means even if they accept 1M tokens, they may not make meaningful use of all that input. This gap affects reliability. A model might slow down, hallucinate, or misinterpret input if overwhelmed with too much or poorly organized data. Developers need to verify if the model was fine tuned for long contexts, or simply adapted to accept them. WHAT CHANGES FOR ENGINEERS With these new capabilities, developers can move past earlier limitations. Manual chunking, token trimming, and aggressive summarization become less critical. But this does not remove the need for data prioritization. Prompt compression, token pruning, and retrieval pipelines remain relevant. Techniques like prompt caching help reuse portions of prompts to save costs. Mixture-of-experts (MoE) models, like those used in LLaMA 4 and DeepSeek V3, optimize compute by activating only relevant components. Engineers also need to track what parts of a prompt the model actually uses. Output quality alone does not guarantee effective context usage. Monitoring token relevance, attention distribution, and consistency over long prompts are new challenges that go beyond latency and throughput. IT IS ALSO A PRODUCT AND UX ISSUE For end users, the shift to larger contexts introduces more freedom—and more ways to misuse the system. Many users drop long threads, reports, or chat logs into a prompt and expect perfect answers. They often do not realize that more data can sometimes cloud the model's reasoning. Product design must help users focus. Interfaces should clarify what is helpful to include and what is not. This might mean offering previews of token usage, suggestions to refine inputs, or warnings when the prompt is too broad. Prompt design is no longer just a backend task, but rather part of the user journey. THE ROAD AHEAD: STRUCTURE OVER SIZE Larger context windows open important doors. We can now build systems that follow extended narratives, compare multiple documents, or process timelines that were previously out of reach. But clarity still matters more than capacity. Models need structure to interpret, not just volume to consume. This changes how we design systems, how we shape user input, and how we evaluate performance. The goal is not to give the model everything. It is to give it the right things, in the right order, with the right signals. That is the foundation of the next phase of progress in AI systems.
Time Business News
15-05-2025
- Time Business News
Switching AI Models Mid-Task: How Multi-Model Platforms Boost Productivity
In the fast-paced world of digital work, we've grown used to switching tools to get the job done—Photoshop for visuals, Notion for planning, VS Code for development. But when it comes to AI, many users are still stuck with a single-model mindset. Whether you're a copywriter fine-tuning tone, a coder debugging logic, or a student balancing summarization with creative flair, the truth is: no one AI model is best for everything. That's where multi-model AI platforms come in—and they're quietly reshaping how power users work. Let's say you're writing an article. You want Claude's natural tone for introductions, GPT-4's structure for body paragraphs, and maybe Gemini's SEO-style tweaks at the end. But if your AI chat platform only runs one model, you're out of luck. Worse, switching tools mid-project means copying and pasting content between tabs, losing context, or restarting conversations—killing the productivity boost AI promised in the first place. View More: Multi-model AI platforms solve this by allowing seamless switching between models within the same chat session. No lost prompts, no split workflows. Just intelligent, efficient back-and-forth with the models that are best for the task at hand. Need GPT-4's logic for structuring your research, but prefer Claude's nuance for phrasing? Toggle models on the fly. Want LLaMA 4 Scout for lightning-fast drafts, and Gemini 2.5 Pro for refining them? You can. This kind of flexibility isn't just nice—it's transformative. The more you can mix and match models, the more you start thinking in workflows, not tools. Here's a real example from my own workflow: Morning : Use Claude to brainstorm content ideas with a more 'human' tone. : Use Claude to brainstorm content ideas with a more 'human' tone. Midday : Switch to GPT-4 for outlining and longform generation—its structure is unbeatable. : Switch to GPT-4 for outlining and longform generation—its structure is unbeatable. Afternoon: Jump to Scout or Gemini to generate quick variations, especially for marketing snippets or meta descriptions. Each model does what it's best at—and together, they help me ship faster, with better quality. When people ask 'What's the best AI for productivity?' I think they're asking the wrong question. The real answer is: it's not about choosing one model—it's about using the right model at the right time. That's why tools that act as AI model aggregators are so powerful. They don't just connect you to Claude or GPT—they let you orchestrate both (and more) in a single space, saving hours of copy-paste frustration and letting you stay in the creative flow. I've been using LeemerChat for this exact reason. It lets me switch between GPT-4.1, Claude 3.7 Sonnet, Gemini 2.5 Pro, and LLaMA 4 Scout without losing context. It's like having a team of expert assistants, each jumping in when they're most useful. The future of AI productivity isn't just faster models—it's smarter workflows. And smart workflows demand flexibility. If you've only ever used a single AI model for everything, you're missing out on the power of pairing strengths, mitigating weaknesses, and truly tailoring your process. In the same way that creative pros use a suite of tools, power users are now building their own multi-model AI stacks. And with platforms like LeemerChat making that easier than ever, switching between AI models might be the biggest productivity hack of the year. TIME BUSINESS NEWS
