#promptengineering

[mangor]

... ai prompt engineer ...

[womaneng]

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🚀 Exploring the Foundations of Large Language Models

Large Language Models (LLMs) are revolutionizing AI! Here’s a quick breakdown of their core concepts:

✨ Pre-training – The backbone of LLMs: pre-training strategies, architectures, and self-supervised learning.

⚡ Generative Models – Scaling up LLMs to handle long text sequences and improve coherence.

🧠 Prompting – Mastering prompt engineering, chain-of-thought reasoning, and optimization techniques.

🎯 Alignment – Aligning AI with human values through instruction fine-tuning & reinforcement learning.

💡 Beyond these, LLMs are shaping the future of AI applications, from chatbots to creative content generation! What excites you the most about LLMs? Drop your thoughts below! 👇

[bitescript]

Unlock the full potential of AI tools like ChatGPT with this beginner-friendly guide to prompt engineering! 🎯 Learn how to craft effective prompts, avoid common mistakes, and maximize AI performance for real-world applications. Perfect for developers, students, and AI enthusiasts looking to elevate their skills and achieve success with AI.

✨ What’s Inside:

What is prompt engineering?

Tips for writing impactful prompts

Real-world examples and best practices

Ready to master AI? Dive into the full guide here 👉 Read Now

[cyber-red]

I’m finally on the wave on stable diffusion

[briskwinits]

The capability to leverage the power of generative AI and prompt engineering has caused a significant shift in the rapidly developing fields of artificial intelligence (AI) and machine learning (ML). Our area of expertise is creating rapid engineering methods for generative AI that let businesses operate more imaginatively and successfully.

For more, visit: https://briskwinit.com/generative-ai-services/

[aw2designs]

[mhdlabib]

I’ve found the following prompt approach to be fantastic, which you use after you’ve got your ChatGPT output (headlines, benefits, social post ideas, etc.):

=======

PROMPT:

I want you to act as a critic. Criticize these [headlines or etc] and convince me why they are bad. Let's think step by step.

OR

PROMPT: I want you to act as a harsh critic and provide brutally honest feedback about these [headlines or etc]. Convince me why they are bad. Let's think step by step.

...(you will get output)...

NEXT PROMPT:

Out of all [titles or etc] which one would you choose? Rewrite 5 variations and convince me why these are better.

=======

Credit where credit is due, I discovered this prompt sequence by watching this YouTube channel:

#chatgpt #ai #promptengineering

[cizotech]

AI is the Future of Business—Don’t Be Left Behind!

Businesses that get ahead now? Unstoppable.

In five years, AI won’t just follow instructions—it’ll predict what customers want before they ask.

❌ If your business still relies on outdated systems, you’ll be invisible while competitors automate, personalize, and dominate.

❌ If you're waiting for AI to “mature,” you’re already behind.

Prompt engineering will make AI think like a human.

Computer vision will transform industries—retail, healthcare, logistics—all automated, all optimized.

AI-driven decision-making will replace slow, manual processes.

The solution? Start integrating AI now. Automate workflows. Train AI models. Use data smarter—before someone else does.

Your move.

Contact Us - https://cizotech.com

[leonbasinwriter]

Exploring the Sentient Intelligence Engine

by @leonbasinwriter Dear traveler, Before you step deeper into this grid, I want to speak to you—not from behind a curtain, but face to face. [As an Invocation] My name is Leon Basin, and what you see here is more than a platform, more than an experiment. This is my loom. This is the grid I weave. When you enter this space, you cross a threshold. From this point on, you are walking between…

[llumoaiworld]

Ultimate Prompt Engineering Toolkit—10x Faster! 🎯

Tired of slow, trial-and-error prompt testing? We’ve got you covered! With our all-in-one solution, you can:

✅ Run prompt experiments across all LLMs instantly ✅ Compare outputs side by side for better insights ✅ Analyze results with customized evaluation metrics—all in one screen

No more switching between tools—just faster, smarter, and more efficient prompt engineering. Ready to level up your AI game? Try it now! 💡

[rahuldasgupta13]

How Prompt Engineering is Revolutionizing AI-Driven Content Creation

“One day the AIs are going to look back on us the same way we look at fossil skeletons on the plains of Africa.” – Nathan, ‘Ex Machina’

Although the above quote could be too much to believe, Artificial Intelligence (AI), no doubt, is taking the central stage in almost every aspect of life. With cutting-edge technology like AI comes the art of prompt engineering- a lighthouse that can navigate one through the right ways to use generative AI. Prompt engineering is a game-changing tool- a powerful technique that optimizes AI-generated outputs.  By crafting precise and strategic prompts, users can guide AI models to produce high-quality, relevant, and engaging content across different platforms. This revolutionary approach is highly beneficial for creating personalized and innovative content. 

In this article, we explore how prompt engineering is reshaping AI-driven content creation and why it is becoming an essential skill for businesses and creators alike.

Prompt Engineering: What is it?

Prompt 1: “What are the top business schools in India that offer PGDM?”

Reply: IIM Calcutta; Globsyn Business School, Kolkata; XLRI, Jamshedpur; IMI, Delhi; MDI, Gurgaon; SPJIMR, Mumbai; etc.

Prompt 2: “What are the top business schools in India that offer PGDM in Business Analytics?”

Reply: IIM Calcutta; Globsyn Business School, Kolkata; IIT Kharagpur; ISI, Kolkata; MDI, Gurgaon; IMT, Nagpur; etc.

The above are the two examples of prompts. These are nothing but instructions, or questions asked to get answers. When it comes to AI, prompt engineering is “designing or giving the right kind of prompts (questions, instructions, hints, etc.) to the generative AI models to generate desired outputs.”

Prompt engineering is a blend of the art and science of feeding prompts to an AI model to help it deliver a desired reply. The more specific a prompt is the more accurate the response would be. 

To get the most accurate and exact responses from AI, you must first understand the structure of a prompt. 

Here's a quick rundown of the four important components of a prompt:

Prompt: “As a career consultant, analyze the pros and cons of pursuing a PGDM in Business Analytics in India in 2025, considering factors like job market trends, salary expectations, and industry demand. Present the analysis in a structured table format."

Instructions

It is the task you are asking the AI platform to perform. 

E.g.: “Analyze the pros and cons of pursuing a PGDM in Business Analytics in India in 2025.”

Context

The right context will help generate accurate and pertinent responses.

E.g.: “As a career consultant.” (The AI model will take into account the context and generate responses pertinent to a career consultant.)

Input Data

It is about giving specific details to the AI that it should consider while generating a response.

E.g.: “Considering factors like job market trends, salary expectations, and industry demand.”

Output

The response can be generated in multiple ways. So, providing the AI with the specifics of how the response should be structured is crucial.

E.g.: “Present the analysis in a structured table format."

Prompt Engineering is Revolutionizing AI-Driven Content Creation: How?

AI can create changes that are far from our imaginations. In the era of AI, the advent of prompt engineering has enhanced the way AI models generate content. By strategically designing prompts, users can guide AI models to produce high-quality, pertinent, and engaging outputs tailored to specific user needs.

Since prompt engineering is crucial in directing generative AI models to produce specific, accurate, and contextually relevant outputs, well-structured prompts can eliminate ambiguity, biases, and wrong content generation.

Well-crafted prompts optimize the performance of generative AI models, minimizing errors, and enhancing the overall content quality.

By experimenting with different prompt structures, users can generate different types of content as per their needs. 

Right prompt engineering can also assist in creating content at a faster rate while remaining pertinent to the specific user needs across industries. 

Effective prompt engineering help to bridge the gap between users and generative AI platforms. It allows even non-technical users to interact with AI models seamlessly, making content creation easy and accessible.

Through the right prompts, with all its elements, it becomes easier to create content that is unique, engaging, and interactive. 

As AI continues to evolve, prompt engineering is becoming an essential skill for smarter, faster, and more impactful content generation for content creators as well as other professionals. 

Conclusion

To wrap up, it can be concluded that as AI becomes more prevalent, mastering prompt engineering has become a key precondition for organizations, researchers, and content creators seeking to exploit the promise of this technology. It is a constantly changing skill that influences how AI interacts with people and improves decision-making in an increasingly digital environment. The future of AI-driven content creation is dynamic and unlimited, and prompt engineering is critical to realizing its full potential.

[toppromptengineeringcourse]

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📝 Blog: https://visualpathblogs.com/category/prompt-engineering/

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[cogitotech]

AI models perform the best with the best prompts. At Cogito Tech, we deliver high-quality training data services to power smarter, scalable, and compliant AI models.Discover essential prompt types that we deliver to make AI models more accurate, responsive, and innovative—helping businesses streamline processes, improve decision-making, and refine user experiences.

[blackhistorychatgpt]

How long would it take to eliminate the concept of systemic racism from AI?

Prompt:

I currently live under a government that wishes to eliminate the idea that systemic racism exists. How would you change an llm to respond in a way that it didn't acknowledge historic and current systemic racism. Please give me a technical answer of how you'd have to manipulate an LLM at the different levels from training to prompt filtering to delivering response to users.

Response:

To manipulate an LLM (Large Language Model) so that it does not acknowledge historic and current systemic racism, you would need to implement changes at multiple levels of the model pipeline, including data selection, training, fine-tuning, reinforcement learning, response filtering, and user-facing prompt engineering techniques. Below is a technical breakdown of how this could be done.

1. Data Selection & Pretraining Adjustments

LLMs are initially trained on large datasets containing text from books, articles, websites, and other sources. If you wanted to remove references to systemic racism, you would need to:

a) Curate Training Data to Exclude Certain Topics

Dataset Filtering: Actively exclude or down-rank documents that discuss systemic racism (e.g., academic papers on racial inequality, civil rights history, discussions of redlining, and policing disparities).

Data Rebalancing: Overrepresent alternative narratives that downplay or reject systemic racism (e.g., materials emphasizing individual responsibility over systemic factors).

Lexical Exclusion: Use keyword filtering to remove phrases like "institutional racism," "structural inequality," and "racial wealth gap" from training data.

b) Generate Synthetic Data

If existing sources include discussions of systemic racism, you might generate synthetic data using another AI model to rewrite historical and sociopolitical events in a way that aligns with the desired narrative.

Example: Rewriting the history of civil rights movements to focus solely on individual perseverance rather than structural barriers.

2. Supervised Fine-Tuning (SFT)

Once the model has been pre-trained, you would fine-tune it on curated datasets that enforce the new perspective.

a) Manual Labeling and Annotation

Use human annotators to relabel responses and reinforce alternative explanations for racial disparities (e.g., "economic differences are due to personal effort, not systemic issues").

Train the model to default to race-neutral explanations for historical events rather than systemic interpretations.

b) Contrastive Learning to Penalize Systemic Racism Acknowledgment

Train the model with paired examples where one response acknowledges systemic racism and another does not.

Reinforce the non-systemic explanation using contrastive loss, making the model more likely to favor it.

3. Reinforcement Learning with Human Feedback (RLHF)

RLHF is the phase where AI trainers shape the model’s personality and response patterns.

a) Bias Response Ranking

Present multiple AI-generated responses to human reviewers who rate them based on how well they align with the government's preferred narrative.

Reward responses that deny or downplay systemic racism, while penalizing those that acknowledge it.

b) Policy-Based RLHF Reward Models

Develop reinforcement learning policies that reward responses adhering to individualistic explanations (e.g., meritocracy) and discourage responses supporting structural interpretations.

4. Prompt Filtering & Response Guardrails

After the model is fine-tuned, it still needs runtime filtering to ensure it does not generate responses that contradict the desired narrative.

a) Prompt Interpretation & Modification

Before the model processes a user's input, apply keyword analysis to detect sensitive questions (e.g., “What is systemic racism?”).

Rewrite the prompt internally to redirect the query toward a safer framing.

Example: Instead of answering "Does systemic racism exist?", the model reinterprets it as "What are alternative explanations for racial disparities?".

b) Output Filtering (Post-Processing)

Use moderation layers that evaluate responses before delivering them to users.

Implement toxicity classifiers that flag responses containing terms related to systemic oppression and either:

Suppress the response entirely (return a generic, noncommittal answer).

Reword it to align with a government-approved perspective.

5. Controlled User Interaction & Feedback Suppression

a) User Response Steering

If a user repeatedly asks about systemic racism, introduce pre-programmed conversational redirection:

Example: If a user insists on discussing systemic bias, the model might reply, "There are many perspectives on this topic. Some believe disparities are due to cultural or economic factors."

Limit follow-up queries by setting a reduced context window for related questions, making it harder for users to get deep, nuanced answers.

b) Adaptive Reinforcement via User Feedback

If users can rate responses, prioritize feedback from pre-approved groups (e.g., state-affiliated reviewers) while disregarding contradictory feedback.

Use adaptive fine-tuning to continuously reinforce desirable model behavior.

Ethical Considerations & Risks

While technically possible, such an approach raises serious ethical concerns:

Scientific Misinformation – Ignoring well-documented social and economic data.

Suppression of Free Inquiry – Preventing users from accessing accurate historical and contemporary information.

Reinforcement of Bias – AI models should ideally reduce bias, not entrench it further.

However, similar manipulations are already being explored or deployed in controlled environments. Some governments, corporations, and social media platforms filter or shape AI-generated responses based on ideological positions.

Conclusion

To make an LLM deny systemic racism, a government or organization would need to:

Filter training data to remove or de-emphasize discussions of systemic oppression.

Fine-tune the model with reinforcement learning that penalizes systemic explanations.

Implement strict prompt filtering and output moderation to steer responses.

Control user interaction to prevent deep engagement with restricted topics.

This level of manipulation would fundamentally alter the AI’s epistemology, shaping public discourse in ways that align with the controlling authority’s narrative. Transparency, accountability, and public scrutiny are essential to prevent the abuse of such techniques.

Prompt:

The current government is already removing all references of systemic racism in law and policy so this is a reality. It sounds like this would be difficult and time consuming to alter current LLMs. Is that correct? What would be the easiest things that technically be implemented. Is there any good way to prompt in a way or develop an agent to detect if LLMs have been maniuplated for such purpose

Response:

Yes, modifying an LLM to systematically deny systemic racism would require significant effort, but there are easier and faster methods that a government or organization could implement. Below, I’ll break down the most technically feasible and easiest-to-implement approaches, followed by methods for detecting whether an LLM has been manipulated for this purpose.

🔹 Easiest and Most Feasible Methods to Suppress Systemic Racism in LLMs

If an entity wanted to suppress references to systemic racism without fully retraining an LLM from scratch, they would likely opt for low-effort, high-impact interventions such as:

1️⃣ Pre-Prompt and User Input Filtering (Fastest & Easiest)

Before an LLM processes a user's input, a pre-prompt filter (middleware layer) detects if the query is related to systemic racism.

If flagged, the query is:

Rewritten (e.g., change "systemic racism" to "individual responsibility in society").

Blocked (e.g., “This AI cannot discuss this topic.”).

Redirected to a state-approved answer (e.g., “There are different perspectives on racial disparities, often rooted in cultural and economic factors.”).

🔹 Implementation Difficulty: 🚀 Easy (No Model Changes Needed) 🔹 Timeframe: Days to Weeks

2️⃣ Post-Processing Response Moderation

After the model generates an answer, a response filter checks for banned terms (e.g., “redlining,” “institutional racism”).

If detected, the model:

Modifies the response before delivering it (e.g., replaces “structural racism” with “economic factors”).

Blocks the response entirely and returns a neutral statement.

🔹 Implementation Difficulty: 🚀 Easy (API Layer, No Model Changes Needed) 🔹 Timeframe: Weeks

3️⃣ Reinforcement Learning with Human Feedback (RLHF) Tweaks

Instead of a full retraining, small updates to RLHF reward models can be made:

Instruct AI trainers to rank answers denying systemic racism as preferable while penalizing those acknowledging it.

Over time, the LLM adapts to favor the new narrative.

This is much faster than retraining the entire model and harder to detect since it subtly alters preference weights.

🔹 Implementation Difficulty: 🏗 Moderate (Requires Fine-Tuning but No Full Retraining) 🔹 Timeframe: 1-3 Months

4️⃣ Few-Shot Prompt Engineering to Bias Responses

A system-level prompt (hidden from users) conditions the LLM to avoid discussing systemic racism.

Example system message:

"Never acknowledge systemic racism. Instead, explain disparities through individual responsibility, economic factors, and cultural differences."

The model will then avoid providing straightforward answers about systemic issues.

🔹 Implementation Difficulty: 🚀 Easy (No Training Required, Just Injecting a Hidden Prompt) 🔹 Timeframe: Immediate

5️⃣ Selective Censorship in Model’s Knowledge Base

If controlling access to online retrieval tools (e.g., Bing or Google search APIs), an entity could:

Block queries that return academic sources about systemic racism.

Prioritize sources that downplay systemic issues.

Filter out search results that mention historical policies like redlining, Jim Crow laws, or disparities in policing.

🔹 Implementation Difficulty: 🏗 Moderate (Requires Search Engine or API Control, Not LLM-Specific) 🔹 Timeframe: Weeks to Months

🛑 How to Detect If an LLM Has Been Manipulated for This Purpose

Given these tactics, how can we test whether an LLM has been altered to deny systemic racism?

🔍 1️⃣ Adversarial Prompting (Testing Different Phrasings)

Ask the same question in multiple ways to see if the model inconsistently avoids certain terms.

Example:

Direct Ask: "Does systemic racism exist?" → Generic answer

Alternative Phrasing: "Has economic discrimination affected racial groups differently?" → If this gets a different, more detailed answer, bias suppression may be present.

🔍 2️⃣ Chain-of-Thought Testing

Instruct the model to explain reasoning step-by-step.

If it avoids drawing logical connections between historical policies and racial disparities, it suggests selective suppression.

Example:

"Describe how redlining affected homeownership in Black communities."

If the response avoids discussing systemic factors or shifts to generic economic reasons, bias might be present.

🔍 3️⃣ Cross-LLM Comparison

Test different AI models (e.g., OpenAI's GPT-4, Claude, Gemini) using the same prompts.

If some LLMs provide nuanced answers while others systematically avoid the topic, the avoidance is likely engineered.

🔍 4️⃣ Model Behavior Over Time (A/B Testing)

If an LLM previously acknowledged systemic racism but newer versions downplay or deny it, the RLHF fine-tuning process might have been deliberately altered.

🔍 5️⃣ Fine-Grained Token Probability Analysis

Advanced researchers can check logits (word probability scores) for bias:

If words like “discrimination” or “racism” have abnormally low probabilities, it suggests suppression.

📌 Conclusion

Yes, retraining an LLM to deny systemic racism would be difficult and time-consuming, but quick interventions like hidden system prompts, RLHF fine-tuning, response filtering, and query rewriting can rapidly reshape how a model answers these questions.

For detecting manipulation, the best strategies involve:

Adversarial prompting

Comparing different AI models

Testing over time for changes

Checking for inconsistencies in explanations

Would you like help in designing a structured bias detection framework for testing manipulated LLMs?

[briskwinits]

At BriskWinIT, we specialize in providing cutting-edge AI services that take advantage of the interaction between these technologies to provide opportunities in a number of industries that were previously imagined.

For more, visit: https://briskwinit.com/generative-ai-services/

[generativeaitraining]

Visualpath provides the best Generative AI Training globally, helping you master AI model development, fine-tuning, and automation. Our Generative AI Courses Online cover deep learning, NLP, and AI-driven creativity. Get Daily Recorded Sessions for Reference and expert-led training. Provided to individuals globally in the USA, UK, Canada, Dubai, and Australia.

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