Distilling Your Own Thinking from AI Chat Logs

What is "distillation" in this context?

Model distillation traditionally compresses a large model into a smaller one, preserving not only correct answers but also the larger model's error patterns and reasoning paths. The recent .skill craze applies a different meaning: extracting a person's behavior, judgment logic, and writing style from all their textual records and packaging it as an AI module.

How does it work?

The core mechanism is Retrieval‑Augmented Generation (RAG): historical texts are stored in a vector database, relevant fragments are retrieved for a query, and a large language model generates a response in a similar tone. Essentially, it is an AI‑wrapped search engine that stitches together notes to answer new questions.

Limitations of the approach

Only explicit knowledge—such as how you write reports, typical phrasing, and standard procedures—can be captured. Implicit knowledge, like the five‑second pause before speaking in a meeting, gut reactions to data anomalies, or unspoken reasons for avoiding a topic, remains out of reach. Consequently, a digital twin can mimic the "skin" (70‑80% fidelity) and perhaps the "bones" (barely passing), but the "soul" is essentially missing.

Why use AI chat logs for self‑distillation?

Chat logs with AI record the process rather than the polished final product, revealing the evolution of thoughts, the topics you truly care about, and a timestamped snapshot of your cognition. Unlike formal documents, these logs are not curated for external readers, making them a raw source of personal reasoning.

A practical method

"Based on these conversations, what stable thinking patterns do I exhibit? Which dimensions do I repeatedly focus on? Which questions do I tend to skip?"

Copy a recent segment of your AI conversations, include both your questions and the model’s answers, and ask the model the above prompt. The response can highlight strengths (e.g., detailed quantitative reasoning) and blind spots (e.g., jumping to conclusions without probing intermediate steps).

What can you extract?

First layer – Skill inventory: concrete abilities you can articulate (useful for resumes or introductions).

Second layer – Thinking patterns: the framework you use to tackle new problems, including typical first steps, decomposition strategies, and common error sources.

Third layer – Cognitive boundaries: areas you avoid, topics you rarely question, and implicit limits of your expertise.

Note that the third layer often requires additional reflection, as AI can only infer from the questions you asked.

Takeaway

Distilling yourself creates a checkpoint of your current knowledge and reasoning, not a prediction of future potential. It offers a low‑cost way to gain insight into your own mind, improve self‑awareness, and better communicate your capabilities.