Prompt Engineering Applications

Prompt engineering is the new consulting superpower. Most haven't realized it yet. Over the last couple of days, I reviewed the latest guides by Google, Anthropic and OpenAI. Some of the key recommendations to improve output: → Being very specific about expertise levels requested → Using structured instructions or meta prompts → Explicitly referencing project documents in the prompt → Asking the model to "think step by step" Based on the guides, here are four ways to immediately level up your prompting skill set as a consultant: 1. Define the expert persona precisely "You're a specialist with 15 years in retail supply chain optimization who has worked with Target and Walmart." Why it matters: The model draws from deeper technical patterns, not just general concepts. 2. Structure the deliverable explicitly "Provide 3 key insights, their implications and then support each with data-driven evidence." Why it matters: This gives me structured material that needs minimal editing. 3. Set distinctive success parameters "Focus on operational inefficiencies that competitors typically overlook." Why it matters: You push the model beyond obvious answers to genuine competitive insights. 4. Establish the decision context "This is for a CEO with a risk-averse investor applying pressure to improve their gross margins." Why it matters: The recommendations align with stakeholder realities and urgency. The above were the main takeaways I took from the guides which I found helpful. When you run these prompts versus generic statements, you will see a massive difference in quality and relevance. Bonus tips which are working for me: → Create prompt templates using the four elements → Test different expert personas against the same problem (I regularly use "Senior McKinsey partner" to counter my position detecting gaps in my thinking.) → Ask the model to identify contradictions or gaps in the data before finalizing any recommendations. We’re only scratching the surface of what these “intelligence partners” can offer. Getting better at prompting may be one of the most asymmetric skill opportunities all of us have today. Share your favourite prompting tip below! P.S Was this post helpful? Should I share one post per week on how I’m improving my AI-related skills?

In just a few minutes, here’s one thing you can do to make AI outputs 10x sharper. One of the most common reasons that prompts fail is not because they are too long, but because they lack personal context. And the fastest fix is to dictate your context. Speak for five to ten minutes about the problem, your audience, and the outcome you want, then paste the transcript into your prompt. Next, add your intent and your boundaries in plain language. For example: “I want to advocate for personal healthcare. Keep the tone empowering, not invasive. Do not encourage oversharing. Help people feel supported in the doctor’s office without implying that all responsibility sits on them.” Lastly, tell the model exactly what to produce. You might say: “Draft the first 400 words, include a clear call to action, and give me three title options.” Here’s a mini template: → State who you are and who this is for → Describe your stance and what to emphasize → Add guardrails for tone, privacy, and any “don’ts” → Set constraints like length, format, and voice → Specify the deliverable you want next Until AI memory reliably holds your details, you are responsible for supplying them. Feed the model your story - no need to include PII - to turn generic responses into work that sounds like you.

I used to think “prompt engineering” was LinkedIn cosplay. A made-up job title for people riding the AI gold rush with nothing but a pickaxe and a Canva resume. I said—confidently and repeatedly—that prompt engineering wasn’t a real profession. That LLMs would soon be smart enough to understand what you meant, not what you typed. That the whole thing was a short-lived hustle. I was wrong. What I dismissed as a gimmick has turned out to be a craft. Prompting matters. More than I expected. Sometimes more than fine-tuning. Sometimes more than model choice. Because here’s the truth: 💡 Prompting is differentiation. A well-designed prompt can yield 10x better results. It’s not a party trick—it’s strategic scaffolding. 💡General-purpose models can outperform fine-tuned ones—if prompted right. Smart prompting + inventive engineering unlocks more than I gave it credit for. 💡Fine-tuning is expensive. Prompting is scrappy. It gives you leverage without the MLOps overhead. 💡Context matters. Strategic prompts that include examples, constraints, clear objectives, and instructions lead to results that are 100X more effective than terse prompts that fail to paint the target. A philosophy teacher of mine, when critiqued and confronted by a position he once held, would say with a twinkle in his eye, “No, you’re mistaken, my former self was of that view.” So, copping his line here, my former self was dead wrong. My current self understands the value still to be extracted from intelligent prompting in AI.

Most people don’t realize: AI can coach you on how to prompt it better. Here’s how to turn AI into your personal prompt coach, so you get better results and learn how to use AI faster. Try this two-step fix: 1. State your goal and context. 2. Ask one of these questions: ➡️ "How would you rewrite my prompt to get more [specific, creative, detailed, etc.] responses?" ➡️ "If you were trying to get [desired outcome], how would you modify this prompt?" ➡️ "If this were your prompt, what would you change to make it more effective?" ➡️ "What elements are missing from my prompt that would help you generate better responses?" ➡️ "How might you enhance this prompt to avoid common pitfalls or misinterpretations?" ➡️ Or simply: "Improve my prompt." Before: "Explain force majeure clauses." After: "Analyze how courts in California have interpreted force majeure clauses in commercial leases since COVID-19, focusing on what constitutes 'unforeseeable circumstances' and the burden of proof required to invoke these provisions." The difference? A broad, non-jx specific, superficial overview vs. actionable legal insights for commercial leases in California. Not only will you get better outcomes, but you will learn how to improve your prompting in the process. What are your go-to strategies or favorite prompts to optimize AI responses?

One of the most powerful techniques in prompt engineering is Chain of Thought (CoT) reasoning — getting your model to “think out loud” before answering. Instead of jumping straight to an answer, the model walks through its logic step-by-step. This leads to much higher accuracy for tasks like math, logic, comparisons, and decision-making. I just went through this new great hands-on tutorial using the new IBM Granite Instruct models. Here’s what I learned: 1/ CoT prompting is easy to enable — just toggle thinking=True and watch the model reason. 2/ Granite models are optimized for reasoning — they sample multiple thought paths and pick the most consistent answer. 3/ You can visually compare normal vs CoT prompts on tasks like: “How many sisters does Sally have?” “Which weighs more: a pound of feathers or 2 pounds of bricks?” “Which is greater: version 9.11 or 9.9?” Mixing acid solutions, triangle angle problems, and more. If you care about transparent, step-by-step reasoning in AI systems, CoT prompting is a must. 🧪 Check out the open-source Granite CoT notebook here: https://lnkd.in/gHkrpfNH

If you’re an AI engineer, product builder, or researcher- understanding how to specialize LLMs for domain-specific tasks is no longer optional. As foundation models grow more capable, the real differentiator will be: how well can you tailor them to your domain, use case, or user? Here’s a comprehensive breakdown of the 3-tiered landscape of Domain Specialization of LLMs. 1️⃣ External Augmentation (Black Box) No changes to the model weights, just enhancing what the model sees or does. → Domain Knowledge Augmentation Explicit: Feeding domain-rich documents (e.g. PDFs, policies, manuals) through RAG pipelines. Implicit: Allowing the LLM to infer domain norms from previous corpora without direct supervision. → Domain Tool Augmentation LLMs call tools: Use function calling or MCP to let LLMs fetch real-time domain data (e.g. stock prices, medical info). LLMs embodied in tools: Think of copilots embedded within design, coding, or analytics tools. Here, LLMs become a domain-native interface. 2️⃣ Prompt Crafting (Grey Box) We don’t change the model, but we engineer how we interact with it. → Discrete Prompting Zero-shot: The model generates without seeing examples. Few-shot: Handpicked examples are given inline. → Continuous Prompting Task-dependent: Prompts optimized per task (e.g. summarization vs. classification). Instance-dependent: Prompts tuned per input using techniques like Prefix-tuning or in-context gradient descent. 3️⃣ Model Fine-tuning (White Box) This is where the real domain injection happens, modifying weights. → Adapter-based Fine-tuning Neutral Adapters: Plug-in layers trained separately to inject new knowledge. Low-Rank Adapters (LoRA): Efficient parameter updates with minimal compute cost. Integrated Frameworks: Architectures that support multiple adapters across tasks and domains. → Task-oriented Fine-tuning Instruction-based: Datasets like FLAN or Self-Instruct used to tune the model for task following. Partial Knowledge Update: Selective weight updates focused on new domain knowledge without catastrophic forgetting. My two cents as someone building AI tools and advising enterprises: 🫰 Choosing the right specialization method isn’t just about performance, it’s about control, cost, and context. 🫰 If you’re in high-risk or regulated industries, white-box fine-tuning gives you interpretability and auditability. 🫰 If you’re shipping fast or dealing with changing data, black-box RAG and tool-augmentation might be more agile. 🫰 And if you’re stuck in between? Prompt engineering can give you 80% of the result with 20% of the effort. Save this for later if you’re designing domain-aware AI systems. Follow me (Aishwarya Srinivasan) for more AI insights!

Unlock the potential of Generative AI to enhance your writing, creativity, and coding skills through prompt engineering. Prompt engineering is a key skill that involves crafting detailed, structured inputs to guide AI towards generating precise, useful outputs. Here are the core strategies to master: - Guide Precisely: Provide detailed instructions for clear, targeted outcomes. - Rich Context: Supply comprehensive background information for more accurate and relevant responses. - Experiment: Start with the basics, then explore more complex requests as you become more comfortable. Improve your AI interactions with these tips: 1. Specificity and Iterations: Craft detailed prompts and refine based on the AI's feedback. 2. Contextual Depth: The more context you provide, the better the AI understands your request, leading to more tailored outputs. 3. Multi-Modal Inputs: Beyond text, incorporate images, code, or data for varied and rich outputs. 4. Example Use: Include examples of what you're aiming for and what you want to avoid to guide the AI more effectively. 5. Advanced Features: Tweak settings like creativity level and response length to get the results you need. 6. Unique Capabilities: Utilize the AI's broad knowledge and support for specific tasks, such as coding assistance. ✍️ Suppose you want to learn a new skill. Here's a prompt template incorporating the above principles: 'I'm eager to learn [Skill Name], aiming to use it for [specific purpose or project]. My background is in [Your Background], and my experience with similar skills is [Your Experience Level]. I aim to build a foundational understanding and complete my first project within [Timeframe]. Could you provide a structured learning path that includes: The key concepts and fundamentals of [Skill Name] I should focus on. Recommendations for online courses, tutorials, and books suitable for beginners. Practical exercises or projects for applying what I learn. Tips for staying motivated and overcoming challenges. Strategies for applying [Skill Name] in real-world situations or job opportunities.' This approach ensures a personalized, goal-oriented learning strategy, leveraging AI's capabilities to support your journey in mastering a new skill. #generativeai #ai #promptengineering #upskill #learning

As an ML Engineer, I deal a lot with Prompt Engineering to get the best result from LLMs. With that exp. I have created the roadmap about how to learn Promot Engineering and write the best prompt: 1/ Understand How LLMs Work - LLMs predict the next token, not “truth” - They’re trained on massive text corpora - Everything depends on the context you give them - If your prompt lacks structure → your output lacks accuracy. 2/ Start with Prompt Basics - Great prompts are clear, structured, and instructive - Use explicit instructions: “Summarize this in 3 bullet points” - Add role/context: “You are a data scientist…” - Be specific with constraints: “Limit answer to 100 words” - Avoid vague prompts like: “Tell me about LLMs” 3/ Practice Prompting Styles - Explore different prompting techniques - Zero-shot: Just ask the question - Few-shot: Add examples to guide the model - Chain-of-thought: Ask the model to “think step by step” - Self-refinement: “What could be improved in the above?” - These patterns reduce hallucinations and improve quality. 4/ Explore Real-World Use Cases - Summarizing long documents - Extracting insights from PDFs or tables - Building a chatbot with memory - Writing job descriptions, SQL queries, or ML code - Use tools like LangChain, LlamaIndex, or PromptLayer for structured experiments. 5/ Learn from Experts - OpenAI Cookbook - Prompt Engineering Guide (awesome repository on GitHub) - Papers like "Self-Instruct", "Chain-of-Thought Prompting", "ReAct" - Courses: Deeplearning . ai’s "ChatGPT Prompt Engineering" (by OpenAI) 6/ Document Your Best Prompts - Test iteratively - A/B test prompts to find the most effective version - Note what works (or fails) - Build your own prompt library! 7/ Automate & Deploy - Use APIs (OpenAI, Claude, Gemini) in Python - Build apps using Streamlit + LLMs - Store embeddings using FAISS or ChromaDB - Build Retrieval-Augmented Generation (RAG) pipelines One of my bonus tip: Use AI to write more refined prompt. Sounds weird? - First, document what you require - ask AI to generate an AI friendly prompt for best result - and see the results - 10x better than your own prompt! In the LLM era, your prompt is your superpower. Repost this if you find it useful. #ai #ml #prompt #llm

#PromptEngineering isn’t just a technique it’s the gateway to mastering #AI. This toolkit is inspired by one of the top prompt engineering papers by #Google, and refined through my experience as a physician, entrepreneur, and AI strategist. To help others apply the power of large language models (#LLMs), I created this: The #DrGPT Advanced Prompt Engineering Toolkit Inside this visual guide, you’ll find: • 10 expert-crafted prompt formats from the Google whitepaper • Real-world examples in clinical, business, and innovation settings • Best practices to improve accuracy and reduce hallucination • Advanced methods like Chain-of-Thought, Tree-of-Thought, ReAct, and more • Designed with clarity, structure, and immediate use in mind Taken from one of Google’s best technical papers and made actionable. Whether you’re a developer, executive, or educator, this toolkit will help you prompt like a pro. Save it. Share it. Start using it. #PromptEngineering #LLM #AItoolkit #DrGPT #GoogleAI #VertexAI #AIinHealthcare #OpenAI #GPT4 #AIleadership #HarveyCastroMD #HealthTech #GenerativeAI

Small variations in prompts can lead to very different LLM responses. Research that measures LLM prompt sensitivity uncovers what matters, and the strategies to get the best outcomes. A new framework for prompt sensitivity, ProSA, shows that response robustness increases with factors including higher model confidence, few-shot examples, and larger model size. Some strategies you should consider given these findings: 💡 Understand Prompt Sensitivity and Test Variability: LLMs can produce different responses with minor rephrasings of the same prompt. Testing multiple prompt versions is essential, as even small wording adjustments can significantly impact the outcome. Organizations may benefit from creating a library of proven prompts, noting which styles perform best for different types of queries. 🧩 Integrate Few-Shot Examples for Consistency: Including few-shot examples (demonstrative samples within prompts) enhances the stability of responses, especially in larger models. For complex or high-priority tasks, adding a few-shot structure can reduce prompt sensitivity. Standardizing few-shot examples in key prompts across the organization helps ensure consistent output. 🧠 Match Prompt Style to Task Complexity: Different tasks benefit from different prompt strategies. Knowledge-based tasks like basic Q&A are generally less sensitive to prompt variations than complex, reasoning-heavy tasks, such as coding or creative requests. For these complex tasks, using structured, example-rich prompts can improve response reliability. 📈 Use Decoding Confidence as a Quality Check: High decoding confidence—the model’s level of certainty in its responses—indicates robustness against prompt variations. Organizations can track confidence scores to flag low-confidence responses and identify prompts that might need adjustment, enhancing the overall quality of outputs. 📜 Standardize Prompt Templates for Reliability: Simple, standardized templates reduce prompt sensitivity across users and tasks. For frequent or critical applications, well-designed, straightforward prompt templates minimize variability in responses. Organizations should consider a “best-practices” prompt set that can be shared across teams to ensure reliable outcomes. 🔄 Regularly Review and Optimize Prompts: As LLMs evolve, so may prompt performance. Routine prompt evaluations help organizations adapt to model changes and maintain high-quality, reliable responses over time. Regularly revisiting and refining key prompts ensures they stay aligned with the latest LLM behavior. Link to paper in comments.