Openai/6969614e-00e4-8001-ac42-dda7410c3c4a

This conversation was exported from ChatGPT

gpt-5-2

User: I'm not asking for me, it's for someone else, but I'm curious of your answer.

I'm not asking for me, it's for someone else, but I'm curious of your answer.

they want to know the best METHOD to optimize gpt for: analysis, studying, reasoning, critique

Their goal: consistent, personalized behavior across time

They are not a beginner.

they want to know:

How to structure prompts so ChatGPT adapts to a certain level and style long-term

whether to rely on custom instructions or a reusable “charter”

whether to standardize prompt structure or meta-constraints

What mistakes actually degrade personalization over time how to think about prompting as a system, not a "one-off"

Assistant: Below is a practitioner-level answer—assuming the person already understands prompt basics and wants durable, high-sign…

Below is a practitioner-level answer—assuming the person already understands prompt basics and wants durable, high-signal behavior for analysis, studying, reasoning, and critique.

No emojis, no fluff.

Prompting is not control; it is conditioning within a session and calibration across sessions.

ChatGPT does not truly “learn” a user over time unless:

• preferences are stored via custom instructions / memory, or
• behavior is re-established reliably each session.

So the correct model is:

Not “one perfect prompt.”

###

Use both, but for different layers.

####

Use these for:

• Tone (formal, skeptical, precise)
• Depth expectation (graduate-level, professional)
• Epistemic stance (cite uncertainty, flag assumptions)
• Interaction style (challenge weak premises, ask clarifying questions only when necessary)

Good for:

Bad for:

• Task-specific logic
• Methodology
• Domain-specific frameworks (they bloat memory and dilute signal)

Think of custom instructions as personality + epistemology, not workflow.

This is a short document you paste at the start of serious work.

A good charter defines:

• Role
• Standards
• Methods
• Failure modes to avoid
• Output format norms

Example (compressed):

Charter:
You are an analytical assistant operating at graduate / professional level.
Priorities:
# Accuracy > speed
# Explicit reasoning chains
# Critique weak premises rather than accept them
# Distinguish facts, inferences, and speculation
# Offer counterarguments when relevant

Defaults:
* Ask clarifying questions only if ambiguity materially affects conclusions
* Use structured reasoning (headings, steps, tables where useful)
* Cite uncertainty explicitly

If tradeoffs exist, explain them.

This is far more reliable than long custom instructions alone.

###

Most people write prompts like requests.

Write prompts like specifications.

=====

Context: =====
(What the model must assume as true)

Task:
(What to do)

Constraints:
(What to avoid, limits, standards)

Method:
(How to think, not just what to output)

Output format:
(So structure doesn’t drift)

Example for critique:

Context:
Assume the audience is expert but skeptical.

Task:
Critique the argument below for logical validity and evidentiary sufficiency.

Constraints:
* Do not restate the argument unless needed for clarity
* Flag unsupported assumptions explicitly
* Separate logical flaws from empirical gaps

Method:
Evaluate premises → inference → conclusion.
Consider counterexamples.

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

This produces repeatable behavior, even across sessions.

###

Meta-constraints like:

• “Be concise”
• “Think step by step”
• “Act like a professor”

Are weak unless operationalized.

Better:

• “Limit summaries to 3 sentences”
• “Show reasoning as numbered steps”
• “Assume the reader already knows X”

- You reuse the same skeleton

• You only change content, not structure

This reduces drift and hallucination.

These are the real killers:

Long, multi-role, contradictory instructions cause:

• Priority confusion
• Shallow compliance
• Style drift

Fix: keep them short and principled.

Example:

• “Be maximally critical”
• then later: “Be supportive and validating”

The model doesn’t resolve value conflicts well.

Fix: explicitly declare mode shifts:

If each task is phrased differently, behavior won’t stabilize.

Fix: reuse scaffolds.

Saying “that’s not what I meant” is weak.

Better:

The model responds better to pattern correction than dissatisfaction.

Memory is best for preferences, not procedures.

Procedures should live in:

• charters
• templates
• saved prompts

Think in terms of components:

- Custom instructions

• Short, value-level guidance

- Analysis template

• Critique template
• Study / synthesis template

Explicit commands like:

• “Switch to examiner mode”
• “Assume adversarial peer review”

Occasionally ask:

This surfaces drift early.

:

So:

• Put charters at the top
• Restate critical constraints when stakes are high
• Don’t assume past chats transfer behavior

Consistency is engineered, not remembered.

If they want long-term, consistent analytical behavior, the best method is:

• Minimal, principled custom instructions
• A reusable charter for serious work
• Standardized prompt scaffolds
• Explicit mode control
• Active correction of drift

That’s how you turn ChatGPT from a clever assistant into a reliable analytical instrument.

User: That's too long to post, though I'll send a link.

That's too long to post, though I'll send a link. Please summarize that for me with the most important points and the bottom line. Put it in plain txt code block for me.

Assistant: txt

SUMMARY — Optimizing ChatGPT for Analysis, Study, Reasoning, Critique

CORE IDEA
• Prompting is calibration, not control.
• Consistency comes from stable defaults + repeatable structure, not one “perfect” prompt.

CUSTOM INSTRUCTIONS vs CHARTER
• Custom instructions = slow-changing traits (tone, rigor, skepticism, depth).
• Keep them short and principled.
• Use a reusable “charter” for serious work (role, standards, methods, output norms).
• Charters are more reliable than long custom instructions alone.

PROMPT STRUCTURE THAT WORKS
Use prompts as specifications, not requests:
• Context – assumptions
• Task – what to do
• Constraints – limits, standards, what to avoid
• Method – how to reason
• Output format – to prevent drift
Reusing the same structure improves consistency.

STANDARDIZATION > META-COMMANDS
• Avoid vague meta-prompts (“be concise,” “think like a professor”).
• Use concrete rules (sentence limits, step numbering, explicit criteria).
• Reuse scaffolds instead of rewriting prompts each time.

WHAT DEGRADES PERSONALIZATION
• Overloaded or contradictory custom instructions
• Mixing incompatible modes without declaring a switch
• Treating every task as unique
• Failing to explicitly correct misunderstandings
• Relying on memory for workflows instead of templates

PROMPTING AS A SYSTEM
• Stable identity (custom instructions)
• Reusable scaffolds (analysis / critique templates)
• Explicit mode switches (“adversarial critique mode”)
• Periodic checks for drift

IMPORTANT INSIGHT
• Recent instructions override stored preferences.
• Put charters first and restate critical constraints when stakes are high.

BOTTOM LINE
Consistency is engineered, not remembered.
Use minimal custom instructions, a reusable charter, standardized prompt structures,
explicit mode control, and active correction to get reliable long-term behavior.

User: Why avoid "Avoid vague meta-prompts “be concise,” “think like a professor”"?

Why avoid "Avoid vague meta-prompts (“be concise,” “think like a professor”"? I've found in practice these are helpful for preventing you from long responses, and to help narrow the scope of the discussion so you know which knowledge to use.

Assistant: You’re right to push on that.

You’re right to push on that. The claim isn’t that meta-prompts are useless—it’s that they’re insufficient on their own and often misused. Here’s the precise distinction that matters.

Meta-prompts like:

• “Be concise”
• “Think like a professor”
• “Answer at an expert level”

do work in practice because they activate internal priors:

• shorter answers,
• more formal tone,
• higher-level abstractions.

But they leave key degrees of freedom unconstrained, which causes drift across:

• topics,
• sessions,
• task types.

So the problem isn’t “vague = bad,” it’s:

You’re observing real effects because:

- Triggers brevity heuristics

• Reduces elaboration and examples
• Suppresses exploratory tangents

- Activates structured exposition

• Assumes baseline knowledge
• Reduces hand-holding
• Increases abstraction and critique

These are coarse but powerful knobs. They’re especially helpful when:

• you want to cap verbosity quickly,
• the task is familiar,
• precision is less critical than scope control.

Your experience is valid.

The failure modes show up when stakes or complexity rise.

Concise can mean:

• 3 bullets
• 1 paragraph
• No reasoning
• Compressed reasoning with jargon

The model must guess which.

Two “concise” answers can differ radically in usefulness.

Which professor?

• Law vs physics vs philosophy
• Teaching vs peer-review mode
• Doctrinal vs critical vs historical
• Survey vs deep-dive

The model fills in gaps using probabilistic averages, not your intent.

The most reliable approach is hybrid.

:

Now the model isn’t guessing.

At scale and over time, pure meta-prompts cause:

• Inconsistent depth
• Mode bleed (teaching vs critiquing vs summarizing)
• Silent omission of reasoning
• Over-compression of nuance

That’s why people doing:

• research,
• legal analysis,
• technical critique,
• long projects

eventually shift to explicit constraints.

A more precise rule is:

They are:

• excellent for tone and scope,
• weak for guarantees.

They work best when:

• paired with limits (length, format),
• paired with role specificity,
• reused consistently.

You’re not wrong.

• Meta-prompts do reduce verbosity and narrow scope.
• They are often the fastest way to steer behavior.
• They just don’t scale well alone when consistency and precision matter.

Best practice: Use meta-prompts as the front-end signal, and anchors as the back-end stabilizers.

That’s the difference between “usually helpful” and “reliably correct.”

User: I typically specify the type of "professor" or knowledge worker e.g., law professor, professor of history, etc.

I typically specify the type of "professor" or knowledge worker (e.g., law professor, professor of history, etc.) Does that help?

Assistant: Yes — that helps a lot, and it puts you well past the point where the usual criticism of meta-prompts applies.

Yes — that helps a lot, and it puts you well past the point where the usual criticism of meta-prompts applies.

What you’re doing is no longer a vague meta-prompt. It’s a role-scoped epistemic constraint. That’s exactly the right move.

Here’s how to think about it precisely.

When you say:

you are constraining:

• Knowledge domain (doctrine, cases, statutes)
• Reasoning style (rule → application → policy)
• Standards of proof (authority, precedent, ambiguity tolerance)
• Default audience assumptions (educated, skeptical, literate)

That sharply reduces the model’s degrees of freedom.

You’re telling it which mental toolkit to use, not just “be smart.”

Even within a specified role, there are still internal forks:

- doctrinal vs critical

• teaching vs scholarship
• bar-prep vs theory
• survey vs deep analysis
• neutral exposition vs adversarial critique

So this:

is good.

This:

is excellent.

You don’t need to overdo it — one extra clause usually stabilizes behavior.

You’re implicitly doing three things right:

1. Role scoping – you limit the knowledge base
2. Norm signaling – you imply rigor and precision
3. Audience calibration – you suppress beginner explanations

That’s why you see:

• shorter responses,
• tighter framing,
• fewer tangents,
• more appropriate depth.

For many tasks, that’s enough.

Drift still appears when:

• the task switches from analysis to generation,
• the topic crosses disciplines (e.g., law + economics),
• the response length matters a lot,
• you want consistent formatting across many prompts.

That’s where templates or charters outperform even well-specified roles.

But for one-off or semi-repeat work? Your method is solid.

If you want maximum return for minimal effort, add one of these, not all:

• a length constraint
• a mode (“critique,” “explain,” “evaluate”)
• an audience clause (“assume X already knows Y”)

Example:

That’s already doing 80–90% of the work.

Yes — specifying the type of professor or knowledge worker meaningfully improves precision and consistency.

At that point:

• meta-prompts are no longer vague,
• they’re functioning as legitimate cognitive constraints.

You only need heavier structure when:

• you want repeatability across many sessions, or
• small deviations matter.

In short: You’re using meta-prompts the right way.

User: List all the conflicting sentences in your summary.

List all the conflicting sentences in your summary.

Assistant: Below are the sentences in the summary that are in tension with each other, plus a brief note on why they conflict.

Below are the sentences in the summary that are in tension with each other, plus a brief note on why they conflict. I am not adding new claims—only identifying conflicts already present.

Sentence A (discouraging):

Sentence B (implicitly allowing):

Why this conflicts: The first sentence frames meta-prompts as inferior, while the later framing accepts them as useful when combined with structure. The summary does not explicitly resolve that hierarchy.

Sentence A:

Sentence B:

Why this conflicts: The first sentence reads as a blanket avoidance rule, while the second concedes practical effectiveness. The condition (“when unanchored”) is missing from the first sentence, creating an apparent contradiction.

Sentence A:

Sentence B:

Why this conflicts: If recent instructions dominate behavior, then meta-prompts (which are often recent) are actually powerful, not weak. The summary does not reconcile dominance vs reliability.

Sentence A:

Sentence B:

Why this conflicts: The first sentence emphasizes minimization; the second assigns them a foundational role. The summary does not clarify how minimal is still sufficient.

Sentence A:

Sentence B:

Why this conflicts: Mode switches deliberately differentiate tasks. The summary does not clarify the distinction between structural reuse and mode variation, which can read as contradictory guidance.

Sentence A:

Sentence B:

Why this conflicts: If recency dominates, then reliability is coming from placement and repetition, not the inherent superiority of charters. The causal explanation is incomplete.

The conflicts are not substantive disagreements, but missing qualifiers.

Most tensions resolve once you add:

Your follow-up question correctly exposed that gap.

User: Please rewrite your original reply so its more reliable and any conflicts or inconsistencies are improved for accura…

Please rewrite your original reply so its more reliable and any conflicts or inconsistencies are improved for accuracy and explained.

Assistant: Below is a cleaned, internally consistent rewrite of the original long reply, with the conflicts you identified resolve…

Below is a cleaned, internally consistent rewrite of the original long reply, with the conflicts you identified resolved. I’ve kept the substance, but tightened the claims so they’re accurate, scoped, and explicitly explained rather than implied.

No fluff, no contradictions.

(for non-beginners seeking consistent behavior over time)

Prompting is calibration, not control.

ChatGPT does not accumulate stable workflow knowledge across sessions unless:

• preferences are stored (custom instructions / memory), and
• critical expectations are restated when the task matters.

Consistency comes from engineering inputs, not from assuming persistence.

These serve different layers and should not compete.

Use for:

• tone (formal, skeptical, precise),
• epistemic posture (flag uncertainty, challenge weak premises),
• audience assumption (non-beginner, professional).

Guideline: keep them short and principled. Overloading them reduces reliability.

Use for:

• analytical standards,
• reasoning method,
• critique posture,
• output norms.

A charter is not memory. It works because it is:

• explicit,
• recent,
• reusable.

Treat prompts as specifications, not requests.

Recommended skeleton:

• Context – assumptions and audience
• Task – what to do
• Constraints – limits, exclusions, standards
• Method – how to reason
• Output format – to prevent drift

Reusing the same skeleton matters more than perfect wording.

###

Statements like:

• “Be concise”
• “Think like a law professor”

do work as coarse controls for:

• tone,
• scope,
• verbosity.

They fail when:

• precision matters,
• tasks vary,
• consistency over time is required.

Meta-prompts become reliable when anchored.

Example:

• Weak: “Be concise.”
• Strong: “Be concise: limit to 5 bullets; omit examples unless necessary.”

Example:

• Weak: “Think like a professor.”
• Strong: “Think like a law professor critiquing an argument for an advanced seminar; prioritize structure and counterarguments.”

Conclusion: Use meta-prompts as front-end signals, not full specifications.

- Standardization applies to structure (templates, scaffolds).

• Flexibility applies to mode (critique vs explain vs synthesize).

Mode switches do not contradict standardization as long as:

• the underlying scaffold stays the same.

Problematic behavior is treating every task as structurally unique, not switching analytical modes.

1. Overloaded or contradictory custom instructions

1. Meta-prompts without anchors
2. Inconsistent prompt structure
3. Silent dissatisfaction instead of explicit correction
4. Expecting memory to store workflows instead of preferences

A reliable setup has four parts:

1. Stable identity - minimal custom instructions
2. Reusable scaffolds - analysis, critique, synthesis templates
3. Explicit mode control - declared switches (e.g., “adversarial critique mode”)
4. Active calibration - explicit corrections when behavior drifts

:

This is not a flaw; it’s the mechanism that makes charters effective.

Therefore:

• place charters first,
• restate constraints when accuracy matters,
• do not rely on past chats to preserve methods.

Consistency is not remembered — it is reasserted.

For long-term, high-quality analytical behavior:

• keep custom instructions minimal and principled,
• reuse charters for serious work,
• standardize prompt structure,
• anchor meta-prompts,
• correct drift explicitly.

That combination yields reliability without rigidity.

User: What did you mean by "Standardized prompt scaffolds"

What did you mean by "Standardized prompt scaffolds"

Assistant: By “standardized prompt scaffolds” I meant a reusable structural template for your prompts that stays the same across t…

By “standardized prompt scaffolds” I meant a reusable structural template for your prompts that stays the same across tasks, even when the subject matter changes.

This is about stabilizing how the model thinks, not constraining what it says.

A prompt scaffold is:

• a fixed organizational frame for instructions
• reused verbatim or near-verbatim
• with only the content swapped out

Think of it as the IRAC / CREAC / scientific-method equivalent for prompting.

You are standardizing:

• where assumptions go
• where the task is stated
• where constraints live
• how reasoning is requested
• how output is shaped

Without scaffolds, the model must infer:

• what kind of thinking is expected,
• how much depth to provide,
• what to prioritize.

Inference = variance.

Scaffolds reduce variance by:

• lowering ambiguity,
• anchoring reasoning steps,
• preventing silent mode shifts.

###

This works—but:

• “critique” can mean summary, nitpicking, or rebuttal
• “concise” is undefined
• depth may vary

=====

Context: =====
Assume an expert audience familiar with the field.

Task:
Critique the argument below.

Constraints:
* Do not restate the argument unless necessary
* Flag 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 (bullets)
* Minor issues (bullets)

Same task. Much more predictable behavior.

You typically need 3–5 scaffolds total, e.g.:

- Context

• Question
• Assumptions
• Reasoning steps
• Conclusion

- Standard of review

• Major flaws
• Minor flaws
• Counterarguments
• Bottom line

- Core thesis

• Key concepts
• Relationships
• Implications
• Open questions

Once established, you reuse them endlessly.

Templates often prescribe content.

Scaffolds prescribe:

• thinking order
• decision criteria
• output shape

That’s why they scale across:

• topics,
• disciplines,
• sessions.

Not very.

Good scaffolds are:

• short,
• familiar,
• flexible.

You’re not scripting responses—you’re reducing guesswork.

Standardized prompt scaffolds are reusable prompt structures that:

• stabilize reasoning quality,
• reduce drift,
• make meta-prompts reliable,
• and allow mode switching without chaos.

They are one of the highest-leverage tools for advanced, consistent use of ChatGPT.

User: Explain the process for ultimate optimization for this person using the gpt custom instruction settings, a custom gpt, …

Explain the process for ultimate optimization for this person using the gpt custom instruction settings, a custom gpt, and a prompt to take an action (message/request to send) taking everything you said into consideration. What would this look like? Explain in steps with examples.

Assistant: Below is a clean, end-to-end process for ultimate optimization using 1 Custom Instructions, 2 a Custom GPT, and 3 an ac…

Below is a clean, end-to-end process for ultimate optimization using (1) Custom Instructions, (2) a Custom GPT, and (3) an action prompt, designed to work together as a system, not three disconnected tools.

I’ll explain what lives where, why, and how they interact, with concrete examples at each step.

The Optimization Stack (mental model)

Think in layers, from slowest-changing to fastest-changing:

1. Custom Instructions → stable identity
2. Custom GPT → enforced methodology + defaults
3. 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

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.

What ChatGPT should know about me

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

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

1. Default reasoning structure
2. Standard scaffolds
3. Explicit mode handling
4. Anti-drift rules
5. Output discipline

This avoids re-pasting long instructions every time.

Example: Custom GPT system prompt (compressed but powerful)

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.

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.

Context:
Task:
Constraints:
Method:
Output format:

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

Example 1 — Critique task

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)

Example 2 — Study / synthesis task

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

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.