Generative Engine Optimization
Generative engine optimization (GEO) is the practice of structuring content so AI systems—not just search engines—cite and surface it when answering user queries. Where traditional SEO optimized for Google's ranking algorithm, GEO optimizes for the retrieval and citation behavior of large language models in tools like ChatGPT, Perplexity, Google AI Overviews, and Bing Copilot. The tactics overlap but are not identical. LLMs favor content with clear, direct definitions early in the text (BLUF-style), structured data markup, and authoritative sourcing. They penalize content that buries the answer under filler. A 2024 study from Princeton found that content with statistics and quotations from named sources was cited 40% more often by generative engines. GEO matters because a growing share of information queries never reach a traditional search result—the AI answers directly, and if your content is not what it references, you are invisible.
Related terms:
Structured Output
Structured output occurs when a language model returns data in predictable, machine-readable formats—such as JSON, XML, or typed objects—rather than free-form prose, enabling software systems to reliably parse fields like names, dates, and dollar amounts. By using constrained generation to enforce a JSON schema, structured output transforms AI from a conversational interface into a dependable system component.
Fine-Tuning
Fine-tuning continues training a pretrained language model on a smaller, task-specific dataset so it internalizes particular behaviors, styles, or domain knowledge. While it yields more consistent formatting and terminology than prompting alone, it requires curated data, additional training time, and can lead to loss of general capabilities.
Few-Shot Prompting
Few-shot prompting leverages AI’s pattern recognition by providing a handful of examples in the prompt, enabling the model to identify patterns and generate responses that match your intended style or format. This real-time approach achieves consistent, domain-specific outputs without needing massive datasets or model fine-tuning.