AI for Marketing
AI for marketing is the application of language models, predictive analytics, and automation to marketing workflows—content generation, audience segmentation, campaign optimization, personalization, attribution modeling. The hype has outpaced the reality. Most marketing teams using AI in 2025 are using it for first-draft content generation and not much else, despite vendor promises about autonomous campaigns and predictive everything. The real opportunity is less glamorous than the pitch decks suggest: AI is good at compressing time on tasks marketers already know how to do (writing variants, analyzing performance data, repurposing content across formats) and bad at the things that actually drive marketing results (original positioning, creative insight, understanding what your specific audience cares about). The teams getting real value treat AI as a tool for their best marketers to move faster, not as a replacement for marketing judgment.
Related terms:
Chain-of-Thought
Chain-of-thought prompting, introduced by Google Research in 2022, transforms AI from an answer machine into a reasoning partner by explicitly modeling the problem-solving process step by step. By decomposing complex queries into sequential reasoning steps and making implicit thinking explicit, it fundamentally improves AI performance.
Context Window
A context window is the maximum amount of text a language model can process in a single call—input and output combined—measured in tokens. Larger windows (from about 4,000 tokens up to over a million) let you handle longer inputs but raise costs and can suffer from the “lost in the middle” attention issue.
Hallucination
Hallucination occurs when a language model generates text that sounds confident and plausible but is factually incorrect, such as invented citations or fabricated statistics. It stems from LLMs being pattern-completion engines rather than databases.