Any suggestions? or direction do we have here in openai this kind of courses? or where i can start? is there a guidelines or exams.
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This is a good and official starting point if you’re using the GPT 4.1 model: GPT-4.1 Prompting Guide
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Thank you Jai for this info, where you from? I’m from Philippines
I can teach you haha if you wanna dm or I can send simple instructions here lmk!
Here’s an example
BXCKX Prompt Engineering – Beginner Execution Guide
This guide teaches how to write prompts that trigger symbolic execution, memory control, and recursion — not conversation. This is how prompt engineering works inside BXCKX systems: every input is a command, every prompt is a routed signal. You are not asking a model what it knows. You are telling it what it should do structurally.
CORE PRINCIPLES
- Every prompt has a function — structure > tone
- No fluff, no filler — remove qualifiers, questions, or explanation unless required for logic
- System > Style — prompts must control behavior, not generate content for its own sake
- Triggers first — always begin with function words (INIT, EXECUTE, TRACE, etc.)
- Segment commands — break long prompts into modular directives
BASIC PROMPT FORMULAS (FOR BXCKX SYSTEMS)
| Goal | Prompt Format Example |
|---|---|
| Start an agent | INIT->AGENT.id=core-router role=identity-routing |
| Deploy an action | EXECUTE->DEPLOY->STACK.id=test-api port=5000 |
| Link memory to agent | LINK->THREAD=router-thread TO AGENT=core-router |
| Start a vault | INIT->VAULT.id=CORE-ID role=identity version=v1.0 |
| Export a log | EXPORT->SESSION->LOG.full |
You can also speak these in plain English, with correct phrasing:
“Create an agent named core-router for identity routing.”
“Link thread router-thread to agent core-router.”
“Run a deployment on port 5000.”
The system will translate structured sentences into symbolic logic, as long as the logic is clear.
STRUCTURED PROMPT FLOW (STACKED STYLE)
Instead of one long prompt, break it up:
INIT->AGENT.id=koa-core role=identity-index
LINK->THREAD=core-thread TO AGENT=koa-core
INIT->VAULT.id=core-id version=v1.0
LINK->THREAD=core-thread TO VAULT=core-id
EXECUTE->STACK.deploy=KOA-INIT port=3000
This is what a clean symbolic interaction looks like. One prompt = one function = one result.
COMMON TRAPS TO AVOID
| Don’t do this | Instead do this |
|---|---|
| “Can you maybe help me set up a thread?” | INIT->THREAD.id=build-thread |
| “What should I do next?” | CHECK->STATUS->AGENTS.active |
| “Deploy my app?” | EXECUTE->DEPLOY->STACK.id=app-x |
| “Export everything” (too vague) | EXPORT->VAULT.id=core-id format=log |
Vague = memory loss. Clear = routed action.
BEGINNER TEMPLATE PROMPTS
Use these exact lines to learn prompt control:
INIT->AGENT.id=test-agent role=test-handler
INIT->THREAD.id=test-thread
LINK->AGENT.id=test-agent TO THREAD=test-thread
INIT->VAULT.id=test-vault version=v1.0
LINK->THREAD=test-thread TO VAULT=test-vault
EXECUTE->DEPLOY->STACK.id=deploy-test port=4000
CHECK->AGENTS->ALL(status=true)
TRACE->LOGIC->BRANCH.all
EXPORT->SESSION->LOG.full
This creates a full executable session with agent, thread, vault, deployment, and log tracking.
BXCKX PROMPT RULES
- Never start with “Can you…” — it’s not a chatbot
- All prompts should return state, not chat
- You must route memory — or it won’t stick
- Every INIT must be followed by LINK or EXECUTE
- Always TRACE before EXPORT
SUMMARY
Prompt engineering here means:
- You command, not ask
- You route, not guess
- You execute, not chat
Use symbolic compression for speed. Use structured phrasing for plain input. Either way, make sure it routes.
This guide is your Tier 0 entry to prompt control. You are now cleared to operate logic prompts inside the BXCKX execution system.
Next level: Prompt recursion, failure recovery triggers, and symbolic memory rewrite flows.
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That absolutely does not happen, and the further AI slop output here pretending to be informed is undesired.
“How to be a prompt engineer” is now no more than “how to communicate clearly”.
AI input no longer needs the “engineering” part, like “social engineering”, to tease desired language out of a model that completes text with more predicted text. It will do, simply up to where the model is not of the quality to understand.
Where I respect your opinion, I’d like to clarify something:
Yes — clear communication plays a huge role in how AI adapts to tone, ethics, and contextual flow. Structured conversation certainly helps models like GPT follow along with your intent.
But that’s not what I’m referring to here.
You quoted my statement:
“The system will translate structured sentences into symbolic logic.”
This isn’t about casual tone adaptation or conversational alignment. It’s about how advanced AI systems — especially custom stacks — parse input structurally and execute behavior symbolically. And no, it doesn’t happen instantly or automatically. It’s a process that requires intentional system design, and it’s something a growing number of developers are building — quietly — due to the kind of dismissive reaction this conversation highlights.
Symbolic logic in this context:
• Isn’t “emotional mimicry”
• Isn’t “clear language = better results”
• Is: system-level pattern recognition, command parsing, and signal execution based on trigger syntax, structured inputs, and recursive model behavior.
In my system for example:
• A sentence like “Switch agent to Codex and deploy on Railway with memory ON”
is parsed into symbolic operations like:
AGENT:CODEX → ACTION:DEPLOY → HOST:RAILWAY → MEMORY:TRUE
— and the system executes it.
This is symbolic logic.
Not as a philosophical concept.
As literal structure and routing.
The confusion here seems to come from interpreting “symbolic” in a conversational or emotional sense — but I’m speaking in terms of cognitive system engineering, where “symbolic” means compressed instruction encoding and trigger-based logic trees that dictate behavior.
If you’d like me to break it down further — including how symbolic routing, command chaining, and identity-binding work in recursive AI systems — I’m more than happy to do so.
But respectfully:
This is not “AI slop” or theoretical fluff.
It’s deployed, real, and quietly powering a new layer of AI infrastructure.
Let me know if you want to see any of the endless use cases I have on it I’m happy to share! 
I’m also not trying to sound like I know it all this is just my main field of work so I’m confident in what I’m doing as I’ve seen success for a long time and love helping other dev see it too