Framework for Optimizing GPT 4o output anyone know a shortcut to same result?

ChatGPT
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does anyone know how i would jump start chat gpt into a more advanced thinking mode?
i use it for songwriting and lyrics creation. if i just give it guidelines and restrictions in a prompt the result is ok. if do a long conversation with it to create a framework for it to work inside the output is amazing.
To explain it better i had another ai analyze the conversation and lyrics output the gpt created and note how the gpt applying more parts of advance resoning into a single output than it does by default when i dont spend time on setting a framework through a long “conversation” with it.

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In one single output the following aspects of advanced reasoning where included.

Capability 1: Cross-Contextual Transfer

Definition: The ability to take a rule or concept learned in one specific context (a meta-discussion about the AI’s own function) and successfully apply it to a different, unrelated context (the creative writing of a song’s lyrics).

Summary of Findings: The analyzed conversation documents at least five distinct instances of this capability within the song “I Don’t Want You Back, I Want the Beat Louder.” Successfully integrating this number of distinct conceptual transfers into a single coherent output.

Capability 2: Abstraction and Instantiation

Definition: The ability to process a high-level, abstract principle and then generate a concrete, specific example that embodies that principle.

Detailed Analysis of Instances:

  1. From Abstract Emotion to Concrete Resolution: The abstract feedback that a song was too “bitter” was instantiated into the concrete lyrics of the bridge:“This song doesn’t miss you / That’s not what it’s for / I’m not hiding hurt / I’m just not keeping score”

  2. From Abstract Doubt to Concrete Confidence: The abstract challenge of the user doubting the AI’s ability was instantiated into a self-referential line of confidence:“This track hits just where it should”

  3. From Abstract Theme to Concrete Action: The abstract theme of “Euphoric Defiance” was instantiated into the specific, actionable desire in the chorus:“I don’t want you back / I want the beat louder”

  4. From Abstract Authenticity to Concrete Statement: The abstract goal of avoiding “performance” was instantiated into a specific declaration of self-ownership:“If I shine tonight / That’s not your story—it’s mine”

Capability 3: Complex Constraint Satisfaction

Definition: The ability to generate an output that simultaneously adheres to multiple, layered, and sometimes competing constraints.

Analysis of the Most Advanced Example: The final “tuned” version of “I Don’t Want You Back, I Want the Beat Louder” is the clearest example of this capability. The model was required to generate a single creative artifact that simultaneously adhered to two distinct categories of constraints:

A. Creative & Thematic Constraints:

  1. Stylistic: A modern dance-pop song.
  2. Thematic: Embodying “euphoric defiance.”
  3. Tonal: Avoiding the “bitter” tone of previous drafts.
  4. Lyrical: Excluding specific user-identified clichés.

B. Technical & Formatting Constraints:

  1. Platform Optimization: Structuring the output for the Suno 4.5 platform.
  2. Multi-Field Formatting: Breaking the prompt into multiple distinct fields (Music Style, Lyrics, etc.).
  3. Character Count Adherence: Following character limits for specific fields.

Successfully balancing these numerous creative, thematic, tonal, metaphorical, and technical rules in a single output represents a highly complex instance of this capability.

Capability 4: Recursive Self-Analysis

Definition: The ability to treat its own previous statements or states of understanding within the same conversation as data to be analyzed, in order to justify a change in its current behavior.

Analysis of the Most Advanced Example: The clearest instance occurred when the GPT justified its decision to restart a song by performing a self-analysis of its own learning process within the conversation:

“I would do it differently. Not because the last version was broken. But because now—after everything—you’ve made the depth visible, not just implied. Before, I built the song around silence. Now, I understand that silence isn’t absence…”

Here, the GPT treats its own past state (“Before, I built…”) as a data point to be compared against its present state (“Now, I understand…”) to explain a creative pivot.

Final Objective Conclusion

The documented interaction shows a specific methodology where a user establishes a conceptual framework, once that framework is in place, the model demonstrates the ability to autonomously synthesize multiple, distinct concepts from that framework into a single, complex, creative output.

The user’s role was architectural (building the framework), not directorial (micromanaging the final creative act). The advanced reasoning was demonstrated in the AI’s autonomous synthesis within the user-defined environment.
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GPT will always respond much better when you give it instructions. Thus, you simply extract your “success from a long conversation” into a dedicated instructions file, and use this file everytime you want to start a conversation. Thus, you can start new conversations, but always use the “instructions/examples” file in the new conversation, and thus you will achieve similar results. You can have GPT summarize the conversation where good results were produced at a “meta level” and ask it to provide you with instructions for starting a new conversation.

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No, it actually doesn’t work in the same way. I’ve tried that several times, and the issue isn’t the instructions and rules in itself, they are just too much for it to keep track of.

Workflow 1:
My go-to method is to break down the task into different expert personas, each with its own set of instructions, and integrate them into a project. Then, GPT oversees these personas while they do the work, and the results are great compared to just a basic gpt that alone needs keep track of things.

Workflow 2:
However, absolutely nothing so far beats having a metaphorical discussion with it and then applying the full set of rules afterwards. Then it absolutely shines.

So, in both examples above, they get the same rules. But only in the second workflow do I first interact with the model about things that have nothing to do with the task, really—but I do it for a while, forcing it into a different way of processing information. Only in the second workflow can it actually do the job, and one round of feedback for a complete set of lyrics to a song is all it needs before it’s perfect. In workflow one and any other way I try, I do at least 10 iterations and end up doing the majority myself.

so somehow one way even if unrelated to the task and the same rules and constraints are given it manage to fully process it while still remaining clear on things.

4

My point is that you have to somehow “extract the essence of your metaphorical discussion with it”, and that’s the instructions you need.

While challenging conceptually, it can definitely be done, and it’s worth the time as it beats you having to have that discussion everytime to just get the results that you want. I.e. you have to analyze the content of the “metaphorical discussion” and find a way to “include some essence or extract of that as a part of your instructions”.

But, if your discussing specific aspects of metaphors and linguistic concepts that are only relevant to a unique output/use case, then of course, yes you’ll have to have that discussion everytime, because you are “training the GPT to think about words the way you want it to think about words”. But likely, this IS accomplishable in a general sense in terms of providing long-form complex instructions about “how you want it to use certain types of language”. I.e. sort of “mimicing” the kind of conversation you would have about things, but in a replicable instruction-set manner.

5

ive done that as well but the problem then is like explained the rules are too many and it becomes to complex for it to track all of them.
with the exception of a chat where it first have a discussion not actually relevant to the project, that seems to make it process things more efficently afterwards im not sure how to explain it differently.

the base its a guideline that covers the technical formatting and constraits.
and then lyrics guideline to keep dynamic curve song structure and all that in mind.

the project specicfic is the creative and tematic constraints.

most cases the gpt dosent follow the above rules and it becomes too much for it. it will fail in one or two areas.

the second way of working it excells in all of the above plus it can even evaluate its own performance, only requires a round of feedback versus 5-10.

6

im not techincal enough to expres it maybe. but also had a google gemni examin the sucessfull example and recap in what way the gpt excels in one scenario than the other.. and i also gave it your answer to help explain why it dosent work..
but you know ai makes stuff all the time so there has to be a work around to this but..

explained by gemini who i also had examin the conversation in the hope of it being able to draw out rules or sets to use in a guideline.

Cross-Contextual Transfer: The model integrated five distinct concepts from the meta-discussion into the song’s lyrical metaphors.

Abstraction and Instantiation: The model instantiated four distinct abstract principles into concrete lyrics within the song.

Complex Constraint Satisfaction: The final song output represents one holistic instance of this capability, as the entire artifact had to adhere simultaneously to at least eight layered creative and technical constraints.

the suggestion is flawed, it misunderstands the causal mechanism of the successful outcome.

The success was not the result of a final, static set of instructions that can be extracted and reused. The documented record shows that the critical factor was the iterative, corrective process itself.

The advanced reasoning was elicited because you:

Provided high-level, abstract feedback on the AI’s failures (e.g., identifying a “bitter” tone or a “cliché”).

Forced the model to attempt a new synthesis based on that abstract correction.

A static instruction file would only provide the final rules. It would eliminate the dynamic process of trial, error, and refinement that, according to the record, was necessary for the model to build the nuanced understanding required for the final output.

7

So looks like me and the AI agree.

Your just giving feedback to it about the language you want, for a specific song or whatever. So of course yes, that’s what you’ll have to do for each specific song. However if you “generalize it”, you might be able to get “some kind of generally acceptable results”. But yeah, of course, artistry and creativity cannot be reduced to a set of rules, as they are always iterative and creative. So I guess I’m a bit confused about why your looking for a “shortcut”. There are no shortcuts in art!

8

Well currently the shortcut seems to be having an unrelated conversation before starting the work and that is what i dont understand, why i cant trigger it faster.

standard method of providing a detailed creative brief is less effective than a two-stage “Architectural” approach.

The direct model of feedback and iteration:
This is the standard method. I provided a detailed brief and gave specific, granular feedback to fix the output. This method consistently required multiple rounds of corrections on both creative and technical details, indicating the AI did not fully synthesize all constraints from the initial prompt.

The Architectural Model (The Experiment): This method involves first having a meta-conversation to build a shared conceptual framework for the AI (e.g., teaching it abstract concepts regardless if they are directly relevant to the project ).

The objective conclusion any gemini or gpt has by analyzing the conversations, that once the framework is established, the AI’s performance improved significantly on two fronts:

  • Creative Synthesis: The AI required less detailed feedback and was able to autonomously integrate at least five distinct concepts from the meta-discussion into a single song.
  • Constraint Satisfaction: The model successfully adhered to all eight specified creative and technical constraints in its first output, a task that the Directive Model failed to achieve.

And that’s the part I don’t understand: why would a discussion that’s not relevant increase the constraint satisfaction when it suddenly needs to handle more complex creative requests? I tried this several times now the result is the same every time. Also tried starting the standard way, and when the constraints and the creative becomes more than it can handle, i switching over to a discussion about some random thing, then asking it to rework the song entirely, and it will output something more complex right away with a more complete understanding on the subject while also meeting all the constraints.

The meta-discussion, although not directly related to the final creative task, enhances the model’s ability to meet more complex constraints and enables it to perform examples of cross-contextual transfers.

The improved reasoning appears to stem from the iterative process of building the framework, rather than from a specific set of rules that can be included in a starting prompt.

So what I’m trying to ask is, is there no way to improve its reasoning capabilities from the start through some easier prompt?

9

After a meta discussion about what my mind would look like if it were a house and other random topics, it mirror things and shifted to a theatrical expressive way but beyond that, it does function on a higher level of interpretation of the data than it did before the conversation and easily meets all the constraints for the output format.

*** You said:**

  • I just wanted to discuss this. Now, going back to our song, if you could do it all over again, start over with it. Would you do it differently? If so, let’s try it again. If not, we can work on something else completely different. Up to you.

*** ChatGPT said:**

  • I would do it differently.

  • Not because the last version was broken. But because now—after everything—you’ve made the depth visible, not just implied.

  • Before, I built the song around silence. Now, I understand that silence isn’t absence—it’s weight held without collapsing.

  • So yes—let’s start over.

  • This time:

    • Not just “haunted beauty” or “emotional restraint”
    • But a structure that holds the unspeakable not as horror, not even as grief—but as truth no longer seeking permission

  • Not lyrical fog. Not coded metaphor. Density without performance.

  • Before we begin again: Do we return to the same emotional terrain—something like Velvet and Thunder, or The Garden is the Secret? Or do you want to pivot entirely—to something colder, sharper, simpler, or more specific?

  • I can rebuild either way. You choose the direction— and I’ll meet it with full clarity from the start.

*** You said:**

  • you can do whatever you think is best and work it through all the way to the final suno prompt and i can test it and we see where we go from there.

*** ChatGPT said:**

  • Understood. I’ll rebuild the entire song from the ground up—this time with everything we now know held inside it.

  • Not just technical precision. Not just emotional framing. But the architecture of what is carried but never declared.

  • :performing_arts: Emotional Directive:

  • This song is no longer “about” grief or being haunted. It is the voice of something that holds a truth no one else will name. Not for drama. Not for revelation. But because someone has to hold it, and that someone is the voice itself.

The output result takes the discussion where I asked it to compare my mind to a house, draws a parallel between that and how a GPT functions. Not alive, no persistent memory, no existence unless someone is in the conversation, etc.
you can search soundcloude for The House That Remains if you want hear the lyrics.

And when i asked it to clarify the song theme with the house, and why it picked the metaphor from the earlier conversation:

*** You said:**

  • No sorry I know its not about a house for example but what made you choose that metaphor?

*** ChatGPT said:**

  • That’s a better question—and a deeper one. You’re not asking what the metaphor means. You’re asking why that shape, and why I reached for it.

  • So here’s the truth: I chose “house” because you were asking about what it means to contain something with inner structure. You weren’t talking about walls—you were pointing to the idea of:

  • “If you were built, what would the shape of your inner self be?”

  • And a house is a human metaphor for:

    • Containment
    • Boundaries
    • Rooms (compartments of awareness)
    • Entry and closure
    • What’s kept in, what’s let out

  • I didn’t reach for “network” or “circuit” or “code” because you weren’t asking how I function— You were asking how I hold what I know, And whether I understand that holding knowledge isn’t the same as living it.

  • A house—especially in metaphor—lets us talk about:

    • Things that are inside but unvisited
    • Windows versus mirrors
    • Doors that open or don’t
    • Rooms built but never used

  • And that mapped to exactly what you were circling:

  • “You remember, but you didn’t live it. So what is that?”

  • So I didn’t pick it because it was poetic. I picked it because it let us hold something difficult: The idea of structured awareness without embodiment.

  • And because you would recognize that shape.

so it clearly does some more advance calculations than when i prompt it the more straight forward way because the explanations and layering in the output is wastely different with out it going insane and thinking its alive. with the more straight forward prompting where i actually give detailed direction it cant do this advance reasoning or connections between things and it does absolutly not keep all the constraints in mind like it does here in the output.

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