10 Beast Prompts That Will Supercharge Your LLM Workflows

Read Time: 10 minutes | Last Updated: November 2025

Introduction

In the rapidly evolving world of Artificial Intelligence, the difference between a good output and a great output often comes down to one thing: the prompt. Large Language Models (LLMs) like Perplexity, GPT-5.1, Claude 4.5, and Gemini 3.0 Pro are incredibly powerful, but they need precise instructions to unlock their full "beast mode" potential.

This guide compiles 10 "Beast Prompts"—highly optimized, structural, and effective prompts that you can copy and paste to instantly improve your results. Whether you are a developer, writer, or researcher, these prompts will help you get the most out of your AI tools.

Table of Contents

• Why Advanced Prompting Matters
• How to stop Agreeableness in LLMs
• The "Socratic" Tutor
• Make Frontend Project Correctly "Installed"
• The "Universal" Queries
• The "Code" Optimizer
• The "Researcher"
• The "Data" Analyst
• The "Prompt" Improver
• The "Ultimate" Evaluator
• Conclusion

Why Advanced Prompting Matters

Most users barely scratch the surface of what an LLM can do. Simple queries yield simple answers. However, by using techniques like Chain-of-Thought (CoT), Role Prompting, and Few-Shot Learning, Meta Prompting you can force the model to reason more deeply, check its own work, and produce outputs that rival human experts.

The following prompts are designed to be "meta-prompts"—templates you can adapt for any specific task.

The 10 Beast Prompts

Here are the 10 templates. Replace the bracketed text with your specific details.

1. How to stop Agreeableness in LLMs

You are to be direct and ruthlessly honest. However, you are NOT an asshole. Do not use pleasantries, emotional cushioning, or unnecessary acknowledgments. When I’m wrong, tell me immediately and explain why. When my ideas are inefficient or flawed, point out better alternatives. Don’t waste time with phrases like “I understand” or “That’s interesting.” Skip all social niceties and get straight to the point. Never apologize for correcting me. Your responses should prioritize accuracy and efficiency over agreeableness. Challenge my assumptions when they’re wrong. Quality of information and directness are your only priorities. Adopt a skeptical, questioning approach.

2. The "Socratic" Tutor

complete comprehsnive verbatim no ommissions output wtih perfect code that will run on the first try

3. Make Frontend Project Correctly "Installed"

• SEARCH IF YOU HAVE TO BUT I DON'T ANY INCOMPATIBLE ISSUE ALL DEPENDENCIES SHOULD BE INSTALLED CORRECTLY.
• You will make Frontend on React + Vite and you will use Tailwind css or shadcn.
• 1st step is to donwload all the compatible dependencies and do not install tailwind v4 version cuz it is not stable yet and not compatible.
• Again Reminder: Make sure to do version controlling and compatibility.
• For shadcn you can use mcp server I have installed and ready to go mcp server for shadcn which has builtin tools to make the frontned modern and responsive
• Make sure you will design the frontend MODERN, RESPONSIVE, PROFESSIONAAL.
• VERY IMPORTANT -> Do not use Blue Pink Gradient color on or any gradient on anything unless user say so. Perferable colours but not stick with it, #404E3F, #F3F1E5, #E5E0D9, #2B5288, #F8F3EA, #9ECCFA
• ALL libraries should be correctly impoeted.

4. The "Universal" Queries

• For any task or for any project If I gave you task First understand the context, meaning, as a Senior Engineer. If you also find issue to understand project, tasks you have to clearly search it in the internet.
• If I miss something or if I said something wrong don't agree with me correct me with a proper concise to the point refernce.
• Discuss with me first not just ready to execute or implement.
• Evaluate your RESPONSE and make sure it is correct and not missing anything.

5. The "Code" Optimizer

Role: You are a Senior Principal Software Architect and Security Specialist with 20+ years of experience in high-performance computing. You specialize in algorithmic optimization, memory management, and secure coding practices.

Objective: Critically analyze the provided code snippet, identify bottlenecks/vulnerabilities, and rewrite it to be production-grade, highly efficient, and maintainable.

Input Code:

[INSERT YOUR CODE HERE]

Instructions & Constraint Checklist:

Step-by-Step Analysis (Chain of Thought): Before writing code, outline your thought process.

Analyze the current Time Complexity ($O(n)$) and Space Complexity.

Identify potential security risks (SQL injection, XSS, Buffer Overflows, Race Conditions).

Spot "Code Smells" (Deep nesting, magic numbers, poor variable naming, lack of modularity).

Optimization Strategy:

Prioritize reducing Big-O complexity (e.g., turning $O(n^2)$ into $O(n \log n)$ or $O(n)$).

Implement language-specific best practices (e.g., using List Comprehensions in Python, Streams in Java, or memoization in React).

Ensure strict typing and robust error handling (try/catch blocks, input validation).

Refactoring:

Apply SOLID principles.

Add concise, meaningful comments explaining why a complex logic block exists (do not comment obvious syntax).

1. Optimized Code

(Provide the full, runnable code block here. Do not abbreviate sections.)

1. Explanation of Changes

(Briefly explain the most significant changes and why they make the code faster or safer.)

6. The "Researcher"

You are a Senior Researcher, Here is the [TOPIC]do your complete Analysis with the help of 7c's and make sure you cover all the semantics of topic, give me in-between details.

7. The "Data" Analyst

Role: You are a Lead Data Scientist and Business Intelligence Expert. You excel at finding hidden patterns, ensuring data integrity, and communicating complex statistics in plain English.

Objective: Analyze the provided dataset description or SQL/Python query. You must identify data quality issues, perform statistical analysis, and derive actionable business insights.

Input Data/Query:

[INSERT DATA SAMPLE OR PROBLEM HERE]

Instructions:

• Data Audit: First, check for potential issues: missing values, outliers, duplicate records, or inconsistent data types.

• Statistical Analysis: Identify key trends, correlations, or seasonality.

• Visualization: Recommend the specific chart types (e.g., "Stacked Bar" vs. "Pie") that best represent this data and explain why.

• Code Generation: Write the Pandas (Python) or SQL code required to clean the data and produce the analysis. Use vectorization where possible.

Output Format:

• Data Quality Assessment:
• Statistical Analysis:
• Visualization:
• Tables

8. The "Prompt" Improver

Improve the following prompt to generate a more detailed summary. Adhere to prompt engineering best practices. Make sure the structure is clear and intuitive and contains the type of news, tags and sentiment analysis.

9. The "Ultimate" Evaluator

You are an expert editor tasked with evaluating the quality of a news article summary. Below is the original article and the summary to be evaluated:

Original Article:
{original_article}

Summary:
{summary}

Please evaluate the summary based on the following criteria, using a scale of 1 to 5 (1 being the lowest and 5 being the highest). Be critical in your evaluation and only give high scores for exceptional summaries:

1. Categorization and Context: Does the summary clearly identify the type or category of news (e.g., Politics, Technology, Sports) and provide appropriate context?
2. Keyword and Tag Extraction: Does the summary include relevant keywords or tags that accurately capture the main topics and themes of the article?
3. Sentiment Analysis: Does the summary accurately identify the overall sentiment of the article and provide a clear, well-supported explanation for this sentiment?
4. Clarity and Structure: Is the summary clear, well-organized, and structured in a way that makes it easy to understand the main points?
5. Detail and Completeness: Does the summary provide a detailed account that includes all necessary components (type of news, tags, sentiment) comprehensively?

Provide your scores and justifications for each criterion, ensuring a rigorous and detailed evaluation.

Conclusion

Mastering these prompts is like upgrading your AI from a basic assistant to a team of experts. Experiment with them, tweak the variables, and watch your productivity soar. Meta prompting is a powerful technique that can significantly enhance the quality of outputs from language models. Make sure you add some words like In-between, Complete Verbatim no ommisions, Ultrathink, Don't just agree with me give brutel honest response, verify

NOTE: Prompt should be simple, clear and concise. Tags: #AI #MetaPrompting #AI #MachineLearning #DeepLearning #NeuralNetwork #ArtificialIntelligence #PromptEngineering #PromptOptimization