AI models don't learn from feedback like humans; they repeat errors confidently. To combat this, build your personal AI system around a 'postmortem log' that records every mistake and correction. This forces the AI to learn and prevents you from becoming a repetitive editor.

Agents don't automatically remember preferences across sessions. To fix this, create a `memory.md` file and instruct the agent's system prompt to record corrections and new information there. This manually builds a persistent, compounding memory, making the agent smarter over time.

When an AI suggests a bad-fit account, don't just discard it. The speaker dictates the reason for the error back to the AI and explicitly instructs it to update its permanent "outbound process" document. This trains the AI's core logic to avoid similar mistakes in the future, creating a smarter system over time.

Don't get bogged down with complex skill creation templates. The most effective method is to engage in a feedback loop: have an AI agent perform a task, correct its output until it's perfect, then simply instruct the agent to turn that successful interaction into a new, reusable skill.

When an AI tool makes a mistake, treat it as a learning opportunity for the system. Ask the AI to reflect on why it failed, such as a flaw in its system prompt or tooling. Then, update the underlying documentation and prompts to prevent that specific class of error from happening again in the future.

Expect your AI agent's skills to fail initially. Treat each failure as a learning opportunity. Work with the agent to identify and fix the error, then instruct it to update the original skill file with the solution. This recursive process makes the skill more robust over time.

To prevent recurring errors, Lieberman's AI system maintains a "Content Lessons" markdown file. When he gives feedback on a draft, the system abstracts the changes into reusable lessons and logs them. The AI then consults this file for all future drafts, creating a powerful reinforcement loop that improves quality over time.

Instead of complex prompts, interact with AI agents as you would a human employee. When the agent makes a mistake (like a broken link), provide simple, conversational feedback. The agent can then understand the error and self-correct its process for future tasks.

When an AI model makes the same undesirable output two or three times, treat it as a signal. Create a custom rule or prompt instruction that explicitly codifies the desired behavior. This trains the AI to avoid that specific mistake in the future, improving consistency over time.