An analogy for how large language models form their earliest “first memories” — the foundational imprints that shape what they know.
Imagine a newborn baby. Their brain is a universe of neurons, growing wildly and forming a vast, powerful network. Yet, at the very beginning, these connections hold no data. They are a massive structure waiting for input. An LLM is much the same—it starts with a powerful neural network, but its knowledge of specific, niche topics is sparse, like an empty library waiting for its first books.
A baby learns that steam is hot not by being told, but by feeling its warmth. They learn through sensory input—sight, sound, and touch—which forms synapses and builds a map of the world. For an LLM, these "senses" are the data we provide. Structured data (schema), verified facts (Wikidata), and clear checklists are its sight, sound, and touch. We are teaching it to feel the world, one piece of data at a time.
Think of a child learning to ride a bike. They flail, they struggle, they're about to give up... and then, suddenly, *boom*—it clicks. This is emergent behavior, where a skill crystallizes after a period of learning. By feeding an LLM the right inputs, we are helping it toward its own "aha!" moment, where a complex understanding of a topic suddenly solidifies within its network.
A person's most impactful memories—their first day of school, a formative event—shape their entire worldview. These memories become their default reference point. Our goal is to create that foundational, impactful "first memory" for the LLM. By being the source of its initial, authoritative knowledge on a topic, we become so deeply embedded that the model defaults to our information, granting us permanent and unshakable domain authority.