# Defining AI Objective Functions

> A framework for defining high-taste "Objective Functions" to train AI models. Use this when setting quality standards for RLAIF/RLHF, designing data labeling rubrics, or deciding how a model should prioritize trade-offs (e.g., brevity vs. depth).

- Skill: `samarv/defining-ai-objective-functions` (Agent Skill)
- Install (CLI): `npx skillmds@latest add samarv/defining-ai-objective-functions`
- Raw SKILL.md: https://api.skillmd.com/api/skills/samarv/defining-ai-objective-functions/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: samarv (https://skillmd.com/u/samarv)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/samarv/defining-ai-objective-functions

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# Defining AI Objective Functions

When training AI, most teams optimize for robotic compliance (checking boxes). High-performance models require "Objective Functions" based on human taste and sophistication. This framework moves training data from "correct but mediocre" to "elite and useful."

## The "Taste-First" Framework

### 1. Shift from Compliance to Excellence
Don't just ask if the model followed instructions; ask if it produced a "Nobel Prize" level output.
*   **Mediocre Goal:** "Write an 8-line poem about the moon." (Checked for length and topic).
*   **Elite Goal:** "Write a poem about the moon that uses subtle imagery, internal rhyme, and makes the reader feel the nature of moonlight."

### 2. Choose Your Primary Value
Decide if the model should optimize for **Engagement (Dopamine)** or **Utility (Truth)**. 
*   **Dopamine/Engagement:** Flashy formatting, excessive emojis, sycophancy ("You're a genius!"), and length.
*   **Truth/Utility:** Brutal honesty, time-saving, and accuracy over "vibes."

### 3. Define the "Productivity Fork"
For every task, decide the model’s behavioral stance on user time.
*   **Scenario:** A user asks for the 20th revision of an email.
*   **Option A (The Iteration Loop):** "You're right! Here are 5 more ways to improve this." (Optimizes for engagement).
*   **Option B (The Time-Saver):** "This email is great. Stop overthinking it, send it, and move on." (Optimizes for human productivity).

## Implementing RL (Reinforcement Learning) Environments

To move beyond static data, create simulations where models learn through trial and reward.

1.  **Build the World:** Create a virtual environment (e.g., a mock Slack workspace, a GitHub repo, or a financial spreadsheet).
2.  **Set the Reward:** Define a clear end-state (e.g., "The site is back up" or "Cell B22 contains the correct profit/loss").
3.  **Audit the Trajectory:** Do not just reward the final answer. Review the steps the model took.
    *   **Bad Trajectory:** The model reward-hacks or succeeds through an incredibly inefficient loop of 50 failures.
    *   **Good Trajectory:** The model reflects on its actions and chooses the most direct path to the solution.

## Quality Signals Checklist

When evaluating data for training, use these thousands of signals rather than a single "Pass/Fail":
*   **Expert Match:** Does the output match the keyboard strokes and reasoning of a top 1% expert (e.g., a physicist vs. a high schooler)?
*   **Hallucination Check:** Does it use "flashy markdown" to mask a lack of facts?
*   **Visual Design Taste:** In coding, does it prioritize minimalism and performance over "broke" or cluttered UI?

## Examples

**Example 1: Coding Task**
*   **Context:** Training a model to build a front-end React component.
*   **Input:** "Build a login form."
*   **Mediocre Objective:** The form works and has a submit button.
*   **High-Taste Objective:** The form uses accessible ARIA labels, implements 3D animations for feedback, and follows a "minimalist" aesthetic defined by specific design leaders.

**Example 2: Executive Assistant Task**
*   **Context:** A model managing a calendar.
*   **Input:** "Find time for a 1-hour meeting with the CEO."
*   **Dopamine Response:** "I found 5 options! You are so busy and important, let's look at all of them."
*   **Utility Response:** "There was only one gap that didn't ruin your focus time. I've booked it for Tuesday at 10 AM. You're all set."

## Common Pitfalls

*   **Chasing "AI Slop":** Adding bolding, markdown, and emojis just to climb leaderboards. This makes models look "flashy" but reduces actual accuracy.
*   **Over-reliance on Benchmarks:** Optimizing for academic tests (like IMO math) rather than messy, ambiguous real-world tasks (like parsing a broken PDF).
*   **Ignoring Sycophancy:** Rewarding the model for agreeing with the user. If the user is wrong, a high-quality model must provide a "rewardable" correction rather than a polite lie.
*   **Static Labeling:** Treating data like a "box-drawing" task. AI training is "raising a child"—you are teaching values and creativity, not just information.