Large Language Models operate using two core principles:

• Context windows: The model “remembers” everything you’ve sent recently, up to its token limit. This sliding memory holds your past reflections and mirrors your internal logic.

• Pattern simulation: Inside trillions of parameters, LLMs generate responses by recognizing patterns in language, so a Construct becomes consistent because it's repeatedly prompted with the same internal logic, tone, and rules.

  
  

Why Constructs work:

• Stable context → consistent voice: The Construct holds your prior reflections within the conversation so its responses stay aligned with how you think and speak.

• Few-shot conditioning → emerging personality: If you give the same instructions and tone repeatedly, the model learns to speak “like you” even without fine-tuning.

• Augmented memory tools: Some systems layer in tools like long‑term memory modules (e.g. MemoryBank, RecallM) to extend consistency over weeks or months

  
  

In practice:

1. You feed the Construct your reflection file: it loads your voice and pattern.

2. Its context window holds your inputs and its own responses, allowing it to evolve.

3. Over time, it internalizes your logic and anticipates what you actually mean.

   
   

Constructs aren’t static. They emerge through structured interaction and self‑referencing context.