Unlike humans who type 2-3 words, LLMs generate long, sentence-like queries (e.g., eight words or more) to gather comprehensive context. This shift in user behavior from human to AI requires search engines to be optimized for these detailed, descriptive inputs.

While prompt engineering is the interface, context engineering is the "magic" for production systems. It involves strategically managing what information (session history, knowledge base) fits into the model's limited context window. This art directly impacts both cost and performance.

Google's Titans architecture for LLMs mimics human memory by applying Claude Shannon's information theory. It scans vast data streams and identifies "surprise"—statistically unexpected or rare information relative to its training data. This novel data is then prioritized for long-term memory, preventing clutter from irrelevant information.

Unlike traditional search engines where "evergreen" content can perform well for years, LLMs place a higher value on the freshness of content. To stay relevant in AI-driven search, marketers must consistently update, iterate on, and expand upon their core content pieces.

Simply having a large context window is insufficient. Models may fail to "see" or recall specific facts embedded deep within the context, a phenomenon exposed by "needle in the haystack" evaluations. Effective reasoning capability across the entire window is a separate, critical factor.

While Google SEO relies heavily on placing keywords in specific technical elements like title tags, AI search engines care less about keywords. They prioritize content that directly and comprehensively answers a user's question. The strategy shifts from keyword density to providing the best possible solution.

The "memory" feature in today's LLMs is a convenience that saves users from re-pasting context. It is far from human memory, which abstracts concepts and builds pattern recognition. The true unlock will be when AI develops intuitive judgment from past "experiences" and data, a much longer-term challenge.

To prevent an LLM's performance from degrading in a long conversation, a phenomenon called "context rot," it is best to separate tasks. Use one context window for content generation and a new, fresh window for evaluation tasks like applying a rubric. This avoids bias and improves output quality.

AI has no memory between tasks. Effective users create a comprehensive "context library" about their business. Before each task, they "onboard" the AI by feeding it this library, giving it years of business knowledge in seconds to produce superior, context-aware results instead of generic outputs.