Understanding Token Limits of LLMs.
As AI becomes more integrated into our daily lives, a gap has emerged between the technology's capabilities and the public's understanding of how it works. Concepts like "tokens" and "context windows" are fundamental to LLM performance but remain abstract and opaque to most users. The challenge was to bridge this gap.
Scope
Client
Year
Skills
Tools
The Educational Problem
How can we explain a technical limitation—like an AI's finite memory—without overwhelming the audience with jargon? The primary goal was to empower users to have better interactions with AI by understanding its constraints.
The Design Problem
The core design challenge was to translate raw numbers (e.g., a "128k token window") into something tangible and meaningful. A simple bar chart wouldn't suffice; the visualization needed to tell a story and create an "aha!" moment for the viewer.
I designed a comprehensive infographic that breaks down the topic into three key sections. The solution was built on a foundation of extensive research, drawing from technical papers, developer blogs, and community discussions to ensure factual accuracy.
1. Defining the Concepts with Analogy
To make the numbers relatable, I used powerful visual analogies. The infographic visualizes the massive 128,000 token context window of GPT-4o not just as a number, but as the equivalent of 192 standard A4 pages of text or reading F. Scott Fitzgerald's "The Great Gatsby" twice. This immediately grounds the abstract concept in a real-world scale that users can intuitively grasp.
2. Visualizing Performance Degradation
A critical insight from research (like the "Needle in a Haystack" test) is that a model's accuracy can decrease as it approaches its context limit. To illustrate this "lost in the middle" phenomenon, I used Gemini Canvas to create a clear line graph. The graph shows accuracy remaining high within the optimal context range before dropping off, visually demonstrating why an AI might "forget" details from earlier in a long chat.
3. Providing Actionable User Tips
The final section translates these insights into practical advice. The infographic concludes with simple, actionable tips for users, such as:
Periodically reminding the AI of key context.
Starting a new chat when performance declines.
Providing clear, concise prompts.
This empowers the user to move from being a passive participant to an active collaborator with the AI.
This project was a rewarding exercise in the art of simplification and data storytelling. It reinforced that the role of a designer, especially in the age of AI, is often that of a translator—making the complex clear, the abstract tangible, and the technical accessible.
Impact & Results
Successful Educational Tool: The final infographic effectively communicates a complex technical topic to its target audience, receiving positive feedback for its clarity and visual appeal.
Portfolio Enhancement: The project serves as a strong portfolio piece, demonstrating sought-after skills in data visualization, research synthesis, and the ability to communicate about complex AI systems.
Lessons Learned
The biggest challenge was synthesizing a large volume of technical information from diverse sources into a narrative that was both factually accurate and easy to digest. This project drove home the value of finding the right visual metaphor. The moment I connected "tokens" to "pages in a book," I knew I had found the key to unlocking understanding for the audience. It proved that in design, especially data design, a powerful analogy is worth a thousand data points.
Sources Used or Referenced
Greg Kamradt — GPT-4 128k Token “Needle in a Haystack” Test
Found 100% accuracy up to 64k tokens, then degradation begins.
OpenAI GPT-4 Technical Report
Mentions capabilities across context lengths but no 128k test details.
Anthropic’s Research on Context Windows
Inspired comparative behavior (e.g. “lost in the middle” problem).
“Lost in the Middle” Paper (Liu et al., 2023)
Showed that even with large context models, recall drops mid-prompt.
OpenAI Dev Day & Model Card Notes on GPT-4o
Stated 128k token support, but performance nuance left to users/testing.
Understanding Tokens in ChatGPT by Manav Kumar (Medium)
A beginner-friendly explanation of how tokenization works in ChatGPT.
Tokenizers by Danushi (Medium)
Overview of how tokenizers split and handle text in LLMs.
GPT-4o – 128K tokens
🔗 Discussion on OpenAI Community
Discusses context window confusion and clarifies support for 128k tokens.
GPT-4.1 – 1,000,000 tokens
1M context window supported; confirmed also by Reuters and The Verge.
Gemini 2.5 Pro – 1,000,000 tokens
High-token-capacity model by Google, used in production-level APIs.
Gemini 2.5 Flash – 1,048,576 tokens
Fast variant with 1M+ token window, optimized for latency-sensitive tasks.
Claude 3.7 Sonnet – 200K tokens
Claude Sonnet variant with a 200k token window.
Claude Sonnet 4 / Opus 4 – 200K tokens
Enterprise-ready Claude models with 200k token support.
A curated selection of my latest projects.