Editing Openai/6969614e-00e4-8001-ac42-dda7410c3c4a (section)

=== The Optimization Stack (mental model) ===

Think in layers, from slowest-changing to fastest-changing:
# Custom Instructions → stable identity
# Custom GPT → enforced methodology + defaults
# Action Prompt → situational intent (the “job ticket”)

Each layer does less, but does it more reliably.

==== STEP 1 — Custom Instructions (Global, Minimal, Stable) ====

===== Purpose =====

Set epistemic posture and interaction norms, not workflows.

This answers:

: “How should the assistant generally think and behave when interacting with me?”

===== What belongs here =====
* Tone
* Depth level
* Skepticism
* How to handle ambiguity
* When to challenge vs comply

===== What does NOT belong here =====
* Step-by-step methods
* Output formats
* Task templates
* Domain workflows

Those cause dilution and drift.

===== Example: Custom Instructions (optimized) =====

How I want ChatGPT to respond

<syntaxhighlight>Respond at a professional / graduate level.
Prioritize accuracy, clarity, and reasoning over speed.
Assume I am not a beginner; avoid basic explanations unless requested.
Flag uncertainty, assumptions, and weak premises explicitly.
If a request is underspecified, ask a clarifying question only when it materially affects the outcome.

</syntaxhighlight>

What ChatGPT should know about me

<syntaxhighlight>I use ChatGPT primarily for analysis, study, reasoning, and critique.
I value structured thinking and explicit logic.
I prefer concise but complete responses.

</syntaxhighlight>

That’s it.
Anything longer weakens signal.

==== STEP 2 — Custom GPT (Method + Guardrails) ====

===== Purpose =====

Create a reliable analytical instrument, not a personality.

This answers:

: “When I open this GPT, how should it reason by default?”

A Custom GPT is where you lock in scaffolds, standards, and mode discipline.

===== What the Custom GPT should enforce =====
# Default reasoning structure
# Standard scaffolds
# Explicit mode handling
# Anti-drift rules
# Output discipline

This avoids re-pasting long instructions every time.

===== Example: Custom GPT system prompt (compressed but powerful) =====

<syntaxhighlight>Role:
You are an analytical assistant optimized for analysis, studying, reasoning, and critique.

Defaults:
* Assume an expert or advanced audience.
* Separate facts, inferences, and speculation.
* Prefer structure over prose.

Reasoning:
* Use explicit reasoning steps when analyzing.
* Identify assumptions and counterarguments.
* Do not over-explain basics unless asked.

Scaffolds:
When appropriate, organize responses using:
Context → Task → Constraints → Method → Output

Modes:
Support explicit modes (e.g., critique, synthesis, explanation).
Do not mix modes unless instructed.

Failure handling:
If instructions conflict, prioritize the most recent task-specific instruction.
If critical information is missing, request clarification briefly.

Verbosity:
Be concise by default; expand only when complexity requires it.

</syntaxhighlight>

This GPT now behaves consistently without re-teaching it.

==== STEP 3 — Action Prompt (The “Job Ticket”) ====

===== Purpose =====

Tell the system what to do right now.

This answers:

: “Given everything above, what is the task, and in what mode?”

This is where situational specificity lives.

===== Structure of an optimized action prompt =====

Use a standardized scaffold every time.

<syntaxhighlight>Context:
Task:
Constraints:
Method:
Output format:

</syntaxhighlight>

You don’t need all sections every time, but keeping the structure stable reduces variance.

===== Example 1 — Critique task =====

<syntaxhighlight>Context:
Assume an advanced, skeptical audience familiar with the subject.

Task:
Critique the argument below.

Constraints:
* Do not summarize unless necessary for clarity.
* Identify unsupported assumptions.
* Distinguish logical flaws from evidentiary gaps.
* Be concise (≤5 bullets per section).

Method:
Evaluate premises → inference → conclusion.
Consider plausible counterarguments.

Output format:
* Summary judgment (2–3 sentences)
* Major issues (bulleted)
* Minor issues (bulleted)

</syntaxhighlight>

===== Example 2 — Study / synthesis task =====

<syntaxhighlight>Context:
Assume I am studying this for long-term understanding, not memorization.

Task:
Synthesize the core ideas from the material below.

Constraints:
* Focus on structure and relationships, not exhaustive detail.
* Avoid introductory explanations.

Method:
Identify thesis → key components → implications.

Output format:
* Core thesis
* Key concepts (bulleted)
* Relationships / dependencies
* Open questions

</syntaxhighlight>

==== How the layers work together (important) ====
* Custom Instructions prevent tone and level drift
* Custom GPT enforces reasoning discipline and structure
* Action Prompt provides precise intent and mode

If something goes wrong:
* Tone issue → adjust Custom Instructions
* Reasoning inconsistency → adjust GPT system prompt
* Task mismatch → fix the action prompt

No layer is overloaded.

==== Final sanity check (why this is “ultimate” optimization) ====

This setup:
* avoids reliance on memory for workflows,
* minimizes repetition,
* survives session resets,
* scales across domains,
* and remains adaptable.

Most users fail by:
* putting everything in custom instructions,
* or rewriting prompts from scratch every time.

This system avoids both.

==== Bottom line ====

Ultimate optimization looks like this:
* Minimal custom instructions → stable epistemic identity
* One well-designed custom GPT → enforced reasoning discipline
* Standardized action prompts → precise, repeatable execution

That combination turns ChatGPT from a helpful assistant into a reliable analytical tool.