AI terms, explained
The concepts behind the prices and recommendations on this site — tokens, context windows, prompt caching, agents, and the rest of the jargon — in plain language.
Understanding AI Tokens
When managing or designing AI products, you'll constantly hear the term token. While humans think in words and sentences, AI models process…
Read →Architectural Model Routing: How to Build a Cascading LLM System
When teams first deploy AI features, they usually default to a single model. If they need high-quality reasoning, they hook their entire…
Read →Beyond Traditional Agile: Welcome to Agentic SDLC (ADLC)
For product managers, the Software Development Life Cycle (SDLC) has long dictated daily life. We define requirements, wait weeks or months…
Read →Demystifying AGI: What It Is and How It Differs from Today's AI
As artificial intelligence advances from simple pattern matching to multi-agent automation, the term AGI (Artificial General Intelligence)…
Read →Open Weights vs. Open Source AI: The Reality of Transparency for Product Teams
In the rapidly evolving landscape of artificial intelligence, Open Weights have emerged as a prominent buzzword indicating incremental…
Read →The Multimodal Matrix: Comparing Text, Image, Audio, and Video Generation
When building general AI solutions, product teams often assume that moving from text to rich media (images, audio, and video) is just a…
Read →Understanding AI Agents: The Architecture of Agentic Workflows
Most developers and product teams are familiar with traditional LLM applications: a user inputs a prompt, and the model generates a static…
Read →Understanding Prompt Caching: The Ultimate Cheat Code for AI Cost Reduction
If you are running a production AI application—especially a customer support chatbot, a coding assistant, or a RAG (Retrieval-Augmented…
Read →Understanding RAG (Retrieval-Augmented Generation)
When you deploy a standard large language model, it can only answer questions using the data it was trained on. It doesn't know who your…
Read →Understanding the Context Window (And What Happens When It's Full)
Every large language model has a hard physical limit on how much total text it can read, hold in memory, and generate at any single moment.…
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