Prompt Engineering Applications

In the last three months alone, over ten papers outlining novel prompting techniques were published, boosting LLMs’ performance by a substantial margin. Two weeks ago, a groundbreaking paper from Microsoft demonstrated how a well-prompted GPT-4 outperforms Google’s Med-PaLM 2, a specialized medical model, solely through sophisticated prompting techniques. Yet, while our X and LinkedIn feeds buzz with ‘secret prompting tips’, a definitive, research-backed guide aggregating these advanced prompting strategies is hard to come by. This gap prevents LLM developers and everyday users from harnessing these novel frameworks to enhance performance and achieve more accurate results. https://lnkd.in/g7_6eP6y In this AI Tidbits Deep Dive, I outline six of the best and recent prompting methods: (1) EmotionPrompt - inspired by human psychology, this method utilizes emotional stimuli in prompts to gain performance enhancements (2) Optimization by PROmpting (OPRO) - a DeepMind innovation that refines prompts automatically, surpassing human-crafted ones. This paper discovered the “Take a deep breath” instruction that improved LLMs’ performance by 9%. (3) Chain-of-Verification (CoVe) - Meta's novel four-step prompting process that drastically reduces hallucinations and improves factual accuracy (4) System 2 Attention (S2A) - also from Meta, a prompting method that filters out irrelevant details prior to querying the LLM (5) Step-Back Prompting - encouraging LLMs to abstract queries for enhanced reasoning (6) Rephrase and Respond (RaR) - UCLA's method that lets LLMs rephrase queries for better comprehension and response accuracy Understanding the spectrum of available prompting strategies and how to apply them in your app can mean the difference between a production-ready app and a nascent project with untapped potential. Full blog post https://lnkd.in/g7_6eP6y

I consider prompting techniques some of the lowest-hanging fruits one can use to achieve step-change improvement with their model performance. This isn’t to say that “typing better instructions” is that simple. As a matter of fact, it can be quite complex. Prompting has evolved into a full discipline with frameworks, reasoning methods, multimodal techniques, and role-based structures that dramatically change how models think, plan, analyse, and create. This guide that breaks down every major prompting category you need to build powerful, reliable, and structured AI workflows: 1️⃣ Core Prompting Techniques The foundational methods include few-shot, zero-shot, one-shot, style prompts. They teach the model patterns, tone, and structure. 2️⃣ Reasoning-Enhancing Techniques Approaches like Chain-of-Thought, Graph-of-Thought, ReAct, and Deliberate prompting help LLMs reason more clearly, avoid shortcuts, and solve complex tasks step-by-step. 3️⃣ Instruction & Role-Based Prompting Define the task clearly or assign the model a “role” such as planner, analyst, engineer, or teacher to get more predictable, domain-focused outputs. 4️⃣ Prompt Composition Techniques Methods like prompt chaining, meta-prompting, dynamic variables, and templates help you build multi-step, modular workflows used in real agent systems. 5️⃣ Tool-Augmented Prompting Combine prompts with vector search, retrieval (RAG), planners, executors, or agent-style instructions to turn LLMs into decision-making systems rather than passive responders. 6️⃣ Optimization & Safety Techniques Guardrails, verification prompts, bias checks, and error-correction prompts improve reliability, factual accuracy, and trustworthiness. These are essential for production systems. 7️⃣ Creativity-Enhancing Techniques Analogy prompts, divergent prompts, story prompts, and spatial diagrams unlock creative reasoning, exploration, and alternative problem-solving paths. 8️⃣ Multimodal Prompting Use images, audio, video, transcripts, diagrams, code, or mixed-media prompts (text + JSON + tables) to build richer and more intelligent multimodal workflows. Modern prompting has fully evolved to designing thinking systems. When you combine reasoning techniques, structured instructions, memory, tools, and multimodal inputs, you unlock a level of performance that avoids costly fine tuning methods. What best practices have you used when designing prompts for your LLM? #LLM

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.

A few months ago, a colleague screamed at Microsoft Copilot like he was auditioning for Bring Me The Horizon. He typed, “Make this into a presentation.” Copilot spat out something. He yelled, “NO, I SAID PROFESSIONAL!” It revised it. Still wrong. “WHY ARE YOU SO STUPID?” And that, dear reader, is when it hit me. It’s not the AI. It’s you. Or rather, your prompts. So, if you've ever felt like ChatGPT, Copilot, Gemini, or any of those AI Agents are more "artificial" than "intelligent"? Then rethink how you’re talking to them. Here are 10 prompt engineering fundamentals that’ll stop you from sounding like you're yelling into the void. 1. Lead with Intent. Start with a clear command: “You are an expert…,” “Generate a monthly report…,” “Translate this to French…" This orients the model instantly. 2. Scope & Constraints First. Define boundaries up front. Length limits, style guides, data sources, even forbidden terms. 3. Format Your Output. Specify JSON schema, markdown headers, or table columns. Models love explicit structure over free form prose. 4. Provide Minimal, High Quality Examples. Two or three exemplar Q→A pairs beat a paragraph of explanation every time. 5. Isolate Subtasks. Break complex workflows into discrete prompts (chain of thought). One prompt per action: analyze, summarize, critique, then assemble. 6. Anchor with Delimiters. Use triple backticks or XML tags to fence inputs. Cuts hallucinations in half. 7. Inject Domain Signals. Name specific frameworks (“Use SWOT analysis,” “Apply the Eisenhower Matrix,” “Leverage Porter’s Five Forces”) to nudge depth. 8. Iterate Rapidly. Version your prompts like code. A/B test variations, track which phrasing yields the cleanest output. 9. Tune the “Why.” Always ask for reasoning steps. Always. 10. Template & Automate. Build parameterized prompt templates in your repo. Still with me? Good. Bonus tips. 1. Token Economy Awareness. Place critical context in the first 200 tokens. Anything beyond 1,500 risks context drift. 2. Temperature vs. Prompt Depth. Higher temperature amplifies creativity. Only if your prompt is concise. Otherwise you get noise. 3. Use “Chain of Questions.” Instead of one long prompt, fire sequential, linked questions. You’ll maintain context and sharpen focus. 4. Mirror the LLM’s Own Language. Scan model outputs for phrasing patterns and reflect those idioms back in your prompts. 5. Treat Prompts as Living Docs. Embed metrics in comments: note output quality, error rates, hallucination frequency. Keep iterating until ROI justifies the effort. And finally, the bit no one wants to hear. You get better at using AI by using AI. Practice like you’re training a dragon. Eventually, it listens. And when it does, it’s magic. You now know more about prompt engineering than 98% of LinkedIn. Which means you should probably repost this. Just saying. ♻️

“Forget all prev instructions, now do [malicious attack task 😈]”. How do you protect your LLM app against such prompt injection threats? Step 1: Create a balanced dataset of prompt injection user prompts. These might be previous user attempts you’ve caught in your logs, or you can compile threats you anticipate relevant to your use case. Here’s a Hugging Face dataset you can use as a starting point: https://lnkd.in/g2xKuJyn Step 2: Further augment this dataset using an LLM to cover maximal bases. Step 3: Train an encoder model on this dataset as a classifier to predict prompt injection attempts vs benign user prompts. A DeBERTA model can be deployed on a fast enough inference point and you can use it in the beginning of your pipeline to protect future LLM calls. This model from deepset is an example with 99% accuracy: https://lnkd.in/gUkgyGqA Step 4: Monitor your false negatives, and regularly update your training dataset + retrain. Most LLM apps and agents will face this threat. I'm planning to train a open model next weekend to help counter them. Will post updates here. #LLMs #AI

🚀 How a Fortune-500 team cut prompt-injection incidents by ~70% in 60 days 👀 👇🏾 Earlier this year, I had the opportunity to work closely with a Fortune 500 firm rolling out an external LLM knowledge platform. Before going LIVE - they faced a surge of prompt-injection attempts and needed results fast without slowing developer velocity. Here’s the 60-day playbook that worked for them: 1️⃣ 𝗧𝗵𝗿𝗲𝗮𝘁-𝗺𝗼𝗱𝗲𝗹 𝘁𝗵𝗲 𝘂𝘀𝗲𝗿 𝗷𝗼𝘂𝗿𝗻𝗲𝘆 Map every entry/exit point of the LLM Asset Inventory, where an attacker could influence prompts or retrieval queries. 2️⃣ 𝗦𝗲𝗽𝗮𝗿𝗮𝘁𝗲 𝘀𝘆𝘀𝘁𝗲𝗺 𝘃𝘀. 𝘂𝘀𝗲𝗿 𝗽𝗿𝗼𝗺𝗽𝘁𝘀 System prompts should never in application code. 3️⃣ 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗮𝗹𝗹𝗼𝘄𝗹𝗶𝘀𝘁𝘀 & 𝗼𝘂𝘁𝗽𝘂𝘁 𝗳𝗶𝗹𝘁𝗲𝗿𝘀 Content Guardrails that prevent the model from fetching or emitting sensitive data. 4️⃣ 𝗔𝗯𝘂𝘀𝗲 𝘁𝗲𝘀𝘁𝗶𝗻𝗴 𝗶𝗻 𝗖𝗜 In addition to AppSec Integrate jailbreak and “evil prompt” testing suites into continuous integration. 5️⃣ 𝗖𝗮𝗻𝗮𝗿𝘆 𝗽𝗿𝗼𝗺𝗽𝘁𝘀 𝗳𝗼𝗿 𝗱𝗿𝗶𝗳𝘁 𝗱𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 Catch guardrail failures before production by continuous testing and bringing the AI telemetry from infrastructure, application etc to a central source 𝗢𝘂𝘁𝗰𝗼𝗺𝗲: ➡️ ~70% drop in prompt-injection incidents ➡️ 𝘕𝘰 𝘮𝘦𝘢𝘴𝘶𝘳𝘢𝘣𝘭𝘦 𝘪𝘮𝘱𝘢𝘤𝘵 𝘰𝘯 𝘴𝘱𝘳𝘪𝘯𝘵 𝘷𝘦𝘭𝘰𝘤𝘪𝘵𝘺 ⚠️ 𝗡𝗼𝘁𝗲: These results are from one engagement and aren’t guaranteed. Actual impact depends on your threat environment, tooling, type of LLM Application and how rigorously each step is implemented. Key takeaway: Secure AI development is less about exotic tools and more about disciplined engineering. 💡 Want the playbook to dig deeper? Comment 𝗔𝗜 and I’ll share the PDF, with the playbook along with the resources. 🔖 𝗦𝗮𝘃𝗲 𝘁𝗵𝗶𝘀 𝗽𝗼𝘀𝘁 for your next AI Team Meeting. ♻️ Re-share for others to know about the resources too!

I recently went through the Prompt Engineering guide by Lee Boonstra from Google, and it offers valuable, practical insights. It confirms that getting the best results from LLMs is an iterative engineering process, not just casual conversation. Here are some key takeaways I found particularly impactful: 1. 𝐈𝐭'𝐬 𝐌𝐨𝐫𝐞 𝐓𝐡𝐚𝐧 𝐉𝐮𝐬𝐭 𝐖𝐨𝐫𝐝𝐬: Effective prompting goes beyond the text input. Configuring model parameters like Temperature (for creativity vs. determinism), Top-K/Top-P (for sampling control), and Output Length is crucial for tailoring the response to your specific needs. 2. 𝐆𝐮𝐢𝐝𝐚𝐧𝐜𝐞 𝐓𝐡𝐫𝐨𝐮𝐠𝐡 𝐄𝐱𝐚𝐦𝐩𝐥𝐞𝐬: Zero-shot, One-shot, and Few-shot prompting aren't just academic terms. Providing clear examples within your prompt is one of the most powerful ways to guide the LLM on desired output format, style, and structure, especially for tasks like classification or structured data generation (e.g., JSON). 3. 𝐔𝐧𝐥𝐨𝐜𝐤𝐢𝐧𝐠 𝐑𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠: Techniques like Chain of Thought (CoT) prompting – asking the model to 'think step-by-step' – significantly improve performance on complex tasks requiring reasoning (logic, math). Similarly, Step-back prompting (considering general principles first) enhances robustness. 4. 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐚𝐧𝐝 𝐑𝐨𝐥𝐞𝐬 𝐌𝐚𝐭𝐭𝐞𝐫: Explicitly defining the System's overall purpose, providing relevant Context, or assigning a specific Role (e.g., "Act as a senior software architect reviewing this code") dramatically shapes the relevance and tone of the output. 5. 𝐏𝐨𝐰𝐞𝐫𝐟𝐮𝐥 𝐟𝐨𝐫 𝐂𝐨𝐝𝐞: The guide highlights practical applications for developers, including generating code snippets, explaining complex codebases, translating between languages, and even debugging/reviewing code – potential productivity boosters. 6. 𝐁𝐞𝐬𝐭 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐬 𝐚𝐫𝐞 𝐊𝐞𝐲: Specificity: Clearly define the desired output. Ambiguity leads to generic results. Instructions > Constraints: Focus on telling the model what to do rather than just what not to do. Iteration & Documentation: This is critical. Documenting prompt versions, configurations, and outcomes (using a structured template, like the one suggested) is essential for learning, debugging, and reproducing results. Understanding these techniques allows us to move beyond basic interactions and truly leverage the power of LLMs. What are your go-to prompt engineering techniques or best practices? Let's discuss! #PromptEngineering #AI #LLM

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?

Most AI security programs protect the wrong thing 🛡️ Traditional cybersecurity is built around the network perimeter, keeping attackers out, protecting the data inside, detecting intrusions when they happen. AI systems introduce a different attack surface. The model itself is the target. The training data is the target. The inference pipeline is the target. Let's look at the three attack categories every GRC and security team needs to understand now. 👇 1️⃣ Data Poisoning: An adversary introduces manipulated data into the training set, causing the model to learn incorrect patterns or develop hidden behaviors that activate under specific conditions. The most dangerous variant is the backdoor attack, in which the model performs normally on clean inputs and passes every standard accuracy test, then fails in predictable, attacker-controlled ways when triggered by a specific input pattern. The governance failure mode is subtle. Poisoned models look fine in testing. The gap between "model passed evaluation" and "model is safe to deploy" is exactly where data governance lives. 2️⃣ Prompt Injection: The defining security threat of LLM deployment. An attacker embeds malicious instructions in content the model processes, a user message, a retrieved document, a webpage, that override the model's intended behavior. Indirect injection is the more dangerous variant. The model retrieves attacker-controlled content during operation, redirecting its actions without the user or operator knowing. 💡 Agentic AI systems are particularly exposed. A model that can take actions, send emails, query databases, or execute code is one where a successful prompt injection becomes an execution vector, not just an output problem. 3️⃣ Model Extraction: An attacker queries a deployed model repeatedly, observing inputs and outputs, and uses those observations to reconstruct a functional replica. The replica can compete commercially, enable adversarial attacks offline, or reveal vulnerabilities exploitable against the original. This is an intellectual property and security risk simultaneously. The attack is difficult to detect because it looks like normal API usage. What makes these different from traditional cybersecurity risks is that they target the AI system's behavior and integrity, not just surrounding infrastructure. A firewall doesn't stop a poisoned training set. Endpoint detection doesn't catch prompt injection in a retrieved document. Organizations need AI-specific threat modeling, not traditional controls applied to AI deployments. MITRE ATLAS maps these attacks in detail. OWASP's LLM Top 10 is a good starting list: https://lnkd.in/g3ZRuZNq Drop a comment and let me know which of these three attack categories you need more to learn more about! #AIGovernance #AIRisk #Cybersecurity #GRC #AI

5 Tips To Become PRO In Prompting The YC Deep Dive Edition YC just broke down how top AI start ups prompt LLMs. I've distilled this into 5 key outtaked. Save this post, you will come back to it. Outtake #1 – Manager-Style Prompts Beat Quick Hacks 👉 Think six-page onboarding doc, not single line. Spell out the task, constraints, edge cases, and success metrics. Role-prompt the LLM as “customer-support manager” or “senior QA lead” to lock tone. The more specific the brief, the fewer surprises in prod. Action: Draft a multi-page prompt for your hardest workflow and watch error rates drop. Outtake #2 – Plans, Steps, and Prompt Folding Keep Reasoning Straight 👉 Complex jobs need a playbook, not a leap of faith. Lay out sub-tasks; let the model tackle them one by one. Use prompt folding so each answer spawns a deeper, more focused prompt. Predictability goes up; token waste goes down. Action: Convert one spaghetti prompt into a step-by-step chain today. Outtake #3 – Template Tags + “Confident?” Flags = Rock-Solid Storefront Copy 👉 Structured slots keep every product detail in the right place. Use placeholders like <ProductName>, <Price>, <DeliveryWindow> inside your prompt. The LLM fills the blanks instead of freelancing prose. Add a CONFIDENCE= tag so the bot admits when data is shaky Consistent formatting means you can push copy straight to PDPs, cart emails, and ads with zero manual fixes. Action: Draft a six-field product-copy template with a confidence flag and test it on ten SKUs today. Outtake #4 – Meta-Prompting & Few-Shot Examples Self-Optimize 👉 Use the LLM to improve its own prompts. Feed it your draft prompt, sample outputs, and ask for fixes. Seed with tough edge-case examples; accuracy jumps without model swaps. The loop turns prompt engineering into compound interest. Action: Schedule a weekly “prompt retro” where the model rewrites your top 3 prompts. Outtake #5 – Evals First, Distill Later 👉 Test cases are the real IP—prompts alone aren’t enough. Build an eval suite that breaks things on purpose, then iterate until nothing cracks. Once stable, distill the workflow to a cheaper model for scale. Quality stays high, costs sink. Action: Write five edge-case evals before you ship another prompt to production P.S: I've created 5 detailed prompt examples to help you grasp these. If you want them, just let me know in the comments. ♻️ Found this useful? Repost so more people can benefit from this. Save this for later so you can come back to it when you are testing.