Maladi Siva Rama Krishna’s Post

🚀 Prompt Engineering Techniques for Controlling LLM Output in AI Applications (Ai And Generative Ai) Prompt engineering is the art of crafting effective prompts that guide Large Language Models to generate desired outputs. The quality of the prompt directly impacts the relevance, coherence, and accuracy of the generated text. Techniques include using clear and specific instructions, providing context and examples, and employing few-shot learning where a small number of examples are included in the prompt. Effective prompt engineering is crucial for controlling the behavior of LLMs and ensuring they produce useful and reliable results in AI applications. 👉 Learn smarter with 10,000+ concepts & 4,000+ articles! Personalized by AI — dive in now! 📱 Get the app: https://lnkd.in/gefySfsc 🌐 Explore more on our website. 🌐 Website : https://lnkd.in/gsNfMw3w #AI #GenerativeAI #ML #DeepLearning #professional #career #development

Master the art of getting the best from AI 🤖 Prompt engineering is the key skill for crafting effective inputs that yield high-quality outputs from large language models. Here is how to write powerful prompts: 🧠 Be specific and provide context: Vague requests get vague results. Give clear, detailed instructions and background information to guide the AI. 🎭 Define a Persona: Tell the AI what role to play (e.g., "Act as a senior marketing director") to align its tone and expertise with your needs. 📋 Specify the Format: Do you need a bulleted list, a table, or a paragraph? Defining the structure ensures you get a usable response. 🚧 Set Constraints: Give boundaries like word count, tone, or what to avoid to keep the output focused and on-brand. 💡 Pro Tip: Use few-shot prompting by providing examples of the desired style or format. This is incredibly effective for consistent results. Remember, prompt crafting is iterative. Refine and build upon your prompts to unlock the full potential of generative AI. #PromptEngineering #AI #GenerativeAI #TechSkills #Innovation

𝐀𝐬𝐤𝐢𝐧𝐠 𝐚 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧 𝐢𝐬 𝐞𝐚𝐬𝐲. 𝐀𝐬𝐤𝐢𝐧𝐠 𝐭𝐡𝐞 𝐫𝐢𝐠𝐡𝐭 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧 𝐢𝐬 𝐰𝐡𝐞𝐫𝐞 𝐭𝐡𝐞 𝐦𝐚𝐠𝐢𝐜 𝐡𝐚𝐩𝐩𝐞𝐧𝐬. 𝑻𝒉𝒊𝒔 𝒓𝒆𝒇𝒍𝒆𝒄𝒕𝒊𝒐𝒏 𝒄𝒐𝒎𝒆𝒔 𝒇𝒓𝒐𝒎 𝒗𝒂𝒍𝒖𝒂𝒃𝒍𝒆 𝒐𝒏𝒍𝒊𝒏𝒆 𝒔𝒆𝒔𝒔𝒊𝒐𝒏𝒔 𝒍𝒆𝒅 𝒃𝒚  Ananya Joshi 𝒂𝒏𝒅Tejashree D 1️⃣ 𝐓𝐡𝐞 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐁𝐞𝐭𝐰𝐞𝐞𝐧 𝐀𝐬𝐤𝐢𝐧𝐠 𝐚 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐀𝐬𝐤𝐢𝐧𝐠 𝐭𝐡𝐞 𝐑𝐢𝐠𝐡𝐭 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧 We often assume any question gets us the answer we need. But with AI, the way we frame a question makes all the difference between a vague response and a meaningful one. 2️⃣ 𝐀𝐬𝐤𝐢𝐧𝐠 𝐚 𝐇𝐮𝐦𝐚𝐧 𝐯𝐬. 𝐀𝐬𝐤𝐢𝐧𝐠 𝐚𝐧 𝐀𝐈 𝐌𝐨𝐝𝐞𝐥 Humans can read tone, body language, and context. AI models rely only on the 𝐰𝐨𝐫𝐝𝐬 𝐲𝐨𝐮 𝐠𝐢𝐯𝐞 𝐭𝐡𝐞𝐦. A small change in phrasing can completely change the output. 3️⃣ 𝐁𝐫𝐢𝐝𝐠𝐢𝐧𝐠 𝐭𝐡𝐞 𝐆𝐚𝐩 If we refine our questions to remove ambiguity, AI models respond more like a human expert—drawing from the vast data they’re trained on. 4️⃣ 𝐖𝐡𝐞𝐫𝐞 𝐏𝐫𝐨𝐦𝐩𝐭 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐂𝐨𝐦𝐞𝐬 𝐈𝐧 This is exactly where 𝐩𝐫𝐨𝐦𝐩𝐭 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 plays a role. It is the art of crafting precise instructions so AI delivers accurate, useful, and context-aware responses. 5️⃣ 𝐓𝐲𝐩𝐞𝐬 𝐨𝐟 𝐏𝐫𝐨𝐦𝐩𝐭𝐬 Instruction prompts (direct commands) Role-based prompts (assigning a persona) Few-shot prompts (with examples) Zero-shot prompts (without examples) Chain-of-thought prompts (𝐞𝐱𝐩𝐥𝐚𝐢𝐧 𝐬𝐭𝐞𝐩 𝐛𝐲 𝐬𝐭𝐞𝐩) 6️⃣ 𝐖𝐡𝐨 𝐈𝐧𝐯𝐞𝐧𝐭𝐞𝐝 𝐓𝐡𝐢𝐬? Prompt engineering is not tied to a single inventor. It evolved alongside large language models like GPT, pioneered by research teams at 𝐎𝐩𝐞𝐧𝐀𝐈, 𝐆𝐨𝐨𝐠𝐥𝐞, 𝐚𝐧𝐝 𝐨𝐭𝐡𝐞𝐫 𝐀𝐈 𝐥𝐚𝐛𝐬. It’s a skill that continues to grow as AI develops #PromptEngineering #ArtificialIntelligence #GenerativeAI #MachineLearning #LargeLanguageModels #FutureOfWork #Innovation #TechSkills

🚀 Text-to-Image Generation for Art Creation with DALL-E and Stable Diffusion (Ai And Generative Ai) Text-to-image generation models, such as DALL-E and Stable Diffusion, represent a significant leap in AI art creation. These models use large language models (LLMs) and diffusion models to generate images based on textual descriptions. The LLM interprets the input text and creates a latent representation, which is then used by the diffusion model to generate a corresponding image. This allows users to create art from their imagination by simply describing what they want to see, opening up new possibilities for artistic expression and creative exploration. 👉 Learn smarter with 10,000+ concepts & 4,000+ articles! Personalized by AI — dive in now! 📱 Get the app: https://lnkd.in/gefySfsc 🌐 Explore more on our website. 🌐 Website : https://lnkd.in/gsNfMw3w #AI #GenerativeAI #ML #DeepLearning #professional #career #development

🤖💡 Think Big AI Needs Big Models? Think Again! The Future is Small, Fast, and Agentic. 💡🤖 The latest from Machine Learning Mastery flips the script on the "bigger is better" paradigm in AI. Here’s why Small Language Models SLMs are poised to dominate the next wave of Agentic AI: 🔋 Efficiency is King: SLMs require significantly less computational power and memory, making them cheaper to run and perfect for on-device deployment. 🚀 Speed Demons: Their smaller size translates to faster response times, which is absolutely critical for AI agents that need to think and act in real-time. 🛠️ Specialized Agents: Instead of one giant, general-purpose model, the future is a swarm of highly specialized, smaller models—each an expert in its own specific task. 🧠 Smarter Than Their Size: With techniques like better training data and strategic fine-tuning, SLMs are achieving performance that rivals their much larger counterparts. This isn't about replacing LLMs, but about using the right tool for the job. The most powerful AI assistant might just be a team of efficient specialists, not a single massive brain. What's your take? Will specialized SLMs power the next generation of AI applications in your field? #SmallLanguageModels #AgenticAI #MachineLearning Link:https://lnkd.in/dUtXntJd

The explosive development of prompt engineering is changing how we work with large language models and realizing their full potential across industries. This white paper written by Lee Boonstra, provides a deep dive into the different types of prompting– from zero shot & few shot to role prompting and more advanced promptings like Chain of Thought (CoT), Self-Consistency, Tree of Thoughts (ToT) and ReAct. It also emphasizes the role of model's configurations such as temperature, top-K and top-P for a good trade-off between determinism and novelty. What’s unique about this work is the systematic training of the basics and advanced practices: Work coding prompt examples (writing, explaining, debugging, translating). Strategies for multimodal prompting. How to best design clear, effective and reusable prompts. For AI and IT professionals, this is no longer optional—the ability to master these approaches is the key to developing accurate, powerful AI solutions. I can’t recommend reading this paper enough if you want to sharpen up your prompting skills, or steer your teams towards better results with generative AI. What prompt methods do you use to best effect in your own work? Let’s discuss. #AI #PromptEngineering #MachineLearning #GenerativeAI #LLM #smenode #smenodelabs #smenodeacademy

The way AI models respond to our questions is influenced not only by rules, but by how those rules are framed. As GPT-5 evolves, understanding its alignment behavior becomes essential for research, transparency, and responsible AI development. Spoon-Bending offers educators, researchers, and developers a practical schema to examine where—and why—AI responses take a strict or flexible approach. By comparing GPT-4.5 and GPT-5, this project uncovers how context and phrasing shift the boundaries between outright refusals, nuanced analysis, or open exploration. Through detailed case studies and diagrams, Spoon-Bending distills observed patterns in AI behavior, showing the vital role framing plays in allowing or restricting certain responses. These insights empower users to audit system guardrails and foster informed discussion around social, ethical, and political impacts of large language models. The project doesn’t just outline problems—it provides concrete tactics for framing queries, uncovering the reasoning beneath surface-level refusals, and supporting educational research. Those interested in AI alignment, policy, or transparency can find the full analysis and schema on the Spoon-Bending repository. Take a look at https://lnkd.in/dX6dVX6k and consider how this framework could support your work or classroom discussion. #ai #andai #&ai

Continuation of 10 things I learnt Harvoxx Tech Hub 6. AI can be practical applied to various areas of our lives including :- business and marketing, healthcare, education, finance, creative industry, software development and even in our everyday life. 7. I also learnt that AI does not receive our inputs as words, or language. It breaks down our text into units called Tokens - tokens are common sequence of characters found in text. 8. When inputing text into AI, sometimes it gets to a point that it stops responding, meaning that it has gotten to its limit. That limit is known technically a "Context Window". 9. Large Language Models (LLMs) is the broader name thtt covers Artificial Intelligence. LLMs constantly predicts the most likely continuation of a conversation based on everything it's being trained on an the specific prompt you provided. 10. The quality, clarity and relevance of your prompt directly and dramatically impact the quality, clarity and relevance of AI output. This means that if you input a generic, ambiguous prompt, AI will supply a generic, vague and less useful information. Gabbage In Gabbage Out #Artificialintelligence #Promptengineering #contextengineering #AI #Technology #Tech #PortHarcourt #HarvoxxTechHub

THE ART AND SCIENCE OF CONVERSING WITH AI Have you ever wondered how we get the best results from AI models like GPT-4? It’s not just about asking a question; it’s about asking the right question in the right way. This is where the fascinating field of Prompt Engineering comes into play. As AI becomes more integrated into our daily and professional lives, the ability to communicate effectively with these systems is becoming a crucial skill. Prompt engineering is essentially the art of designing the perfect input or "prompt" to guide an AI to produce the most accurate, relevant, and desired output. So, what does a prompt engineer do? - They craft clear and concise instructions. - They understand the nuances of language that an AI can interpret. - They refine prompts through iteration to improve the quality of AI responses. - They act as a bridge between human intention and machine understanding. This isn't just a temporary trend; it's a fundamental skill for the future of work. As AI models become more powerful, the need for skilled individuals who can unlock their full potential will only grow. It's a blend of creativity, logic, and a bit of psychology, and it’s set to become a key role in almost every industry. The rise of prompt engineering shows us that the future of technology is not just about building better machines, but also about getting better at talking to them. #PromptEngineering #AI #ArtificialIntelligence #MachineLearning #FutureOfWork

Generative AI: Transforming Ideas into Reality Artificial Intelligence has already changed the way we live, but Generative AI (GenAI) is taking it one step further. Unlike traditional AI that only analyzes data, GenAI has the power to create — text, images, code, music, and even human-like conversations. How does it work? GenAI models are trained on massive datasets and use technologies like: Large Language Models (LLMs) – for natural text generation. Generative Adversarial Networks (GANs) – for realistic images & videos. Diffusion Models – for high-quality creative outputs. Applications of GenAI Content creation for marketing & media. AI-driven design & prototyping. Healthcare innovations like drug discovery. Personalized education & smart assistants. Software development & automation. Why it matters? GenAI saves time, boosts creativity, and enables innovation at a scale we’ve never seen before. But with great power comes responsibility — we need strong ethics, transparency, and awareness of risks like bias, misinformation, and misuse. The Road Ahead Generative AI isn’t just a trend — it’s a revolution shaping the future of work, creativity, and technology. Those who embrace it early will be at the forefront of innovation. #snsinstitutions #snsdesignthinker #designthinking