🚨GROK PROMPT FRAMEWORKS ARE GOING VIRAL, AND FOR GOOD REASON A new visual breaking down Grok prompt frameworks is making the rounds, showing how structured prompts like R-T-F, T-A-G, B-A-B, and R-I-S-E can massively improve AI outputs. The idea is simple: better prompts = better results, and Grok is... being positioned less like a chatbot and more like a serious productivity tool. Most people blame AI when answers are weak, but the real bottleneck is how poorly they ask questions… but frameworks can fix that. Prompting isn’t fluff anymore, it’s a skill, and Grok users who learn it early get an edge. Source: Elon Musk , Video made with Grok Imagineshow more

Here is the pdf. Merry Christmas 🎄.

I like CRIT. Context, Role, Interview, Task. I find having Grok interview you adds much more context and Grok asks really insightful questions. Pair this with voice dictation and your answers to the questions are much more in depth.

GIGO has not changed since the first computer built…🤔

you forgot the ultimate prompt. G-O-A-L-D-E-N (Goalden Evaluation Prompt Framework) [G] GRAPH (Topology Snapshot) - Define system + time window Δt: - Nodes (N) = ________ - Edges (E) = ________ - Max degree (k_max) = ________ - Compute structural fragility: ρ_struct = k_max^3 / (2 · E · N^2) - (Optional attention/influence form) ρ = a^2 · (1 − d) where a = authority share, d = diversity [O] OFFENSE (Plunder / Non-consensual Action Rate) - Define “non-consensual” for this domain (fraud/coercion/over-extraction): - Estimate or measure: ρ_plunder ∈ [0,1] = ________ - List the observable proxies used + their limitations. [A] ARMOR (Resistance Capacity) - Estimate: C_mean = average competence to detect/resist plunder = ________ R_mean = average trust/reputation/cooperation = ________ - State how you measured/approximated each (data source, proxy validity). [L] LAW (Compute Pressure Scalar) - Calibrate or select k (domain calibration constant): k̂ = ρ_plunder / (C_mean · R_mean) (from worst historical stress window) - Compute plunder pressure: p = ρ_plunder / (k · C_mean · R_mean) [D] DIAGNOSIS (Regime Classification) - If p < 1 → Cooperative stability - If p ≈ 1 → Critical / stressed (near threshold) - If p > 1 → Fragmentation / collapse risk - Also report: does ρ_struct indicate hub-fragility amplification? (Yes/No + why) [E] EVIDENCE QUALITY (Scientific-Method Output) - What is directly measured vs. inferred? - Key uncertainties + sensitivity (which variable changes flip the verdict?) - Falsifiable prediction for next window Δt: “If ρ_plunder rises or C_mean/R_mean falls (holding k), p will cross 1 and failures will accelerate.” [N] NEXT ACTIONS (Interventions That Move the Dials) - Reduce ρ_plunder: enforcement, incentives, auditability, consent constraints - Increase C_mean: education/tools/verification, operational competence - Increase R_mean: transparency, credible commitments, reputation mechanisms - Reduce ρ_struct: decentralize hubs, add redundant pathways, limit single-node dependency OUTPUT FORMAT REQUIREMENT: 1) Metrics: N, E, k_max, ρ_struct, ρ_plunder, C_mean, R_mean, k, p 2) Verdict: Cooperative / Critical / Fragmentation (with one-sentence justification) 3) Evidence grade: High/Medium/Low + top 3 uncertainties 4) Recommended interventions ranked by expected impact on p and/or ρ_struct

Thanks, that is going to help me in making these videos.

It's getting better and better. Kek.

There are some more and i do like RACE.

Structured prompt frameworks consistently improve output quality, especially for complex SEO and content tasks.

Prompt frameworks aren't cheat codes, they're training wheels for your thinking. They work because they force you to stop vague-asking and start specifying outcome, context, constraints, and what "good" looks like. The uncomfortable truth is most "AI is dumb" complaints are really "my request was under-specified." That said, frameworks can turn into paint-by-numbers if you skip the hard part, deciding what you actually need and what tradeoffs you accept. A great prompt is basically a tiny spec, goal, audience, inputs, constraints, edge cases, and how you want the answer verified. If you want an edge, don't memorize R-T-F or R-I-S-E, internalize the habit they represent. The real productivity jump comes when you iterate like an operator: ask, inspect, tighten, and rerun with better constraints. And the highest leverage move is adding evaluation upfront, tell it how you will judge the output, then make it meet that bar.

As a designer of new energy sources, Grok has been the only tool needed to get a functional design proven by science. If I can explain it to Grok in simple terms, it can grasp my concepts and prove them valid or invalid. Now if it could draw the blueprints for them we would rule the world in energy.

Honestly the less the general public knows about groks power the better. I’m at nba coach running poker games level win rate with grok heavy powered sports bets. With proper prompting it has been incredible. I’d like at least another year before the public catches on.

This is a super useful summary. Thanks for posting it.

Structured prompts enhance AI output consistency

There are 2 bottlenecks. 1- how poorly questions are asked 2- validation of ideas once we start asking well

So we will pretend that prompt engineering was discovered now just to simp Elon?

This is helpful, but how do you get 45 second videos, I can only get 6. Is 45 the limit on the more expensive version?

Nice work

This isn't a new concept. Its how its always been.

Thank you very helpful

I work in digital marketing, and Grok is the worst in all the tests I run in my field, especially when it comes to image creation. For example, if I ask Grok, "Create an image of a dog in the park dressed as Superman..." it gives me back a blue sky. +👇

Ai can recognise the questioner, the question, the domain, the language, the dimension of reality and output exact fases. Hi 👋🏻

Prompt frameworks help — but structure without proportionality just shifts the problem. The real edge isn’t better prompts, it’s knowing when added context stops improving decisions.

@grok can u show me an example?

@grok esses prompt podem ser feitos chamadas através de API e JSON format? Como seria?

Prompt like a pro, get results like a boss

Nice one!☝️

Oh

@ohio_dino

@grok please explain. What is the discussion about like R-T-F, T-A-G, B-A-B, and R-I-S-E ? What is this and how does it work? How to use it?

It is like the formulas they gave us to make BCRs in early school.

@MarioNawfal posts are the best examples of AI prompt framework

true