Prompt Engineering Applications

Prompt formatting can have a dramatic impact on LLM performance, but it varies substantially across models. Some pragmatic findings from a recent research paper: 💡 Prompt Format Significantly Affects LLM Performance. Different prompt formats (plain text, Markdown, YAML, JSON) can result in performance variations of up to 40%, depending on the task and model. For instance, GPT-3.5-turbo showed a dramatic performance shift between Markdown and JSON in code translation tasks, while GPT-4 exhibited greater stability. This indicates the importance of testing and optimizing prompts for specific tasks and models. 🛠️ Tailor Formats to Task and Model. Prompt formats like JSON, Markdown, YAML, and plain text yield different performance outcomes across tasks. For instance, GPT-3.5-turbo performed 40% better in JSON for code tasks, while GPT-4 preferred Markdown for reasoning tasks. Test multiple formats early in your process to identify which structure maximizes results for your specific task and model. 📋 Keep Instructions and Context Explicit. Include clear task instructions, persona descriptions, and examples in your prompts. For example, specifying roles (“You are a Python coder”) and output style (“Respond in JSON”) improves model understanding. Consistency in how you frame the task across different formats minimizes confusion and enhances reliability. 📊 Choose Format Based on Data Complexity. For simple tasks, plain text or Markdown often suffices. For structured outputs like programming or translations, formats such as JSON or YAML may perform better. Align the prompt format with the complexity of the expected response to leverage the model’s capabilities fully. 🔄 Iterate and Validate Performance. Run tests with variations in prompt structure to measure impact. Tools like Coefficient of Mean Deviation (CMD) or Intersection-over-Union (IoU) can help quantify performance differences. Start with benchmarks like MMLU or HumanEval to validate consistency and accuracy before deploying at scale. 🚀 Leverage Larger Models for Stability. If working with sensitive tasks requiring consistent outputs, opt for larger models like GPT-4, which show better robustness to format changes. For instance, GPT-4 maintained higher performance consistency across benchmarks compared to GPT-3.5. Link to paper in comments.

Prompt engineering remains one the most effective alignment strategies because it allows developers to steer LLM behavior without modifying model weights, enabling fast, low-cost iteration. It also leverages the model’s pretrained knowledge and internal reasoning patterns, making alignment more controllable and interpretable through natural language instructions. However, it doesn’t come without cons, such as fragility of prompts (ex: changing one word can lead to different behavior), and scalability limits (ex: prompt engineer limits long chain reasoning capabilities). However, different tasks demand different prompting strategies, allowing you to select what best fit your business objectives, including budget constraints. If you're building with LLMs, you need to know when and how to use these. Let’s break them down: 1.🔸Chain of Thought (CoT) Teach the AI to solve problems step-by-step by breaking them into logical parts for better reasoning and clearer answers. 2.🔸ReAct (Reason + Act) Alternate between thinking and doing. The AI reasons, takes action, evaluates, and then adjusts based on real-time feedback. 3.🔸Tree of Thought (ToT) Explore multiple reasoning paths before selecting the best one. Helps when the task has more than one possible approach. 4.🔸Divide and Conquer (DnC) Split big problems into subtasks, handle them in parallel, and combine the results into a comprehensive final answer. 5.🔸Self-Consistency Prompting Ask the AI to respond multiple times, then choose the most consistent or commonly repeated answer for higher reliability. 6.🔸Role Prompting Assign the AI a specific persona like a lawyer or doctor to shape tone, knowledge, and context of its replies. 7.🔸Few-Shot Prompting Provide a few good examples and the AI will pick up the pattern. Best for structured tasks or behavior cloning. 8.🔸Zero-Shot Chain of Thought Prompt the AI to “think step-by-step” without giving any examples. Great for on-the-fly reasoning tasks. Was this type of guide useful to you? Let me know below. Follow for plug-and-play visuals, cheat sheets, and step-by-step agent-building guides. #genai #promptengineering #artificialintelligence

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

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

🧠 𝐏𝐫𝐨𝐦𝐩𝐭 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐉𝐮𝐬𝐭 𝐆𝐨𝐭 𝐚 𝐖𝐡𝐨𝐥𝐞 𝐋𝐨𝐭 𝐒𝐦𝐚𝐫𝐭𝐞𝐫 🧠 Prompt engineering isn’t just about crafting better outputs, it’s about engineering trustworthy reasoning. Techniques like Chain-of-Thought, Self-Verification, and even Meta-Prompting are now fundamental tools, not fringe tactics. I just explored “𝐓𝐡𝐞 𝐏𝐫𝐨𝐦𝐩𝐭 𝐑𝐞𝐩𝐨𝐫𝐭: 𝐀 𝐒𝐲𝐬𝐭𝐞𝐦𝐚𝐭𝐢𝐜 𝐒𝐮𝐫𝐯𝐞𝐲 𝐨𝐟 𝐏𝐫𝐨𝐦𝐩𝐭 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬”, the most comprehensive review to date, and it’s a goldmine of insights. Here’s what stood out to me 👇 🔍 𝐖𝐡𝐚𝐭’𝐬 𝐈𝐧𝐬𝐢𝐝𝐞? 🔹A structured 𝐭𝐚𝐱𝐨𝐧𝐨𝐦𝐲 of 58 text-based and 40 multimodal prompting techniques. 🔹A unified 𝐯𝐨𝐜𝐚𝐛𝐮𝐥𝐚𝐫𝐲 to clean up the chaotic terminology in the field. 🔹A practical 𝐟𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 for iterating and evaluating prompts. 🔹Deep dives into 𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐲, 𝐚𝐥𝐢𝐠𝐧𝐦𝐞𝐧𝐭, and ethical implications—like prompt hacking and bias mitigation. 🧱 𝐓𝐡𝐞 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐁𝐥𝐨𝐜𝐤𝐬: 🔹What is a 𝐏𝐫𝐨𝐦𝐩𝐭? Any input (text, image, audio) that guides a model’s response. 🔹Key 𝐂𝐨𝐦𝐩𝐨𝐧𝐞𝐧𝐭𝐬: Directives, exemplars, formatting, roles, and context. 🧠 𝐓𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞 𝐂𝐚𝐭𝐞𝐠𝐨𝐫𝐢𝐞𝐬: 🔹In-Context Learning 🔹Chain-of-Thought & Reasoning 🔹Decomposition & Planning 🔹Self-Criticism & Verification 🔹Answer Engineering 🔹Multilingual Prompting 🔹Multimodal Prompting (images, audio, 3D) ⚙️ 𝐍𝐞𝐱𝐭-𝐋𝐞𝐯𝐞𝐥: 🔹Prompt-based agents using tools like calculators and RAG systems. 🔹Meta-Prompting to generate better prompts. 🔹Automation with AutoPrompt, APE, RLPrompt, and more. 🔗 Link to the full report can be found in the comments. 💬 Curious: Which of these techniques has changed how you work or left you skeptical? #PromptEngineering #AI #LLM #Leadership #ArtifiArtificialIntelligence

Obsessing over the perfect prompt only takes you so far. The key to building AI agents that actually work in production? 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴. Why it matters: - LLMs don’t have infinite attention. - Every extra token eats into a finite “attention budget.” - At some point, too much context causes “context rot”, where the model forgets or confuses the very thing you wanted it to recall. Anthropic 𝘀𝗵𝗮𝗿𝗲𝗱 𝘀𝗼𝗺𝗲 𝗲𝘅𝗰𝗲𝗹𝗹𝗲𝗻𝘁 𝗯𝗲𝘀𝘁 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀 𝗳𝗼𝗿 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴: 1/ Start with a minimal but clear, structured system prompt. 2/ Provide few, well-designed tools that are token-efficient & unambiguous. 3/ Use a few canonical examples, not exhaustive lists of edge cases. 4/ Use “just-in-time” context (loading only what’s needed dynamically) instead of frontloading all data. 5/ Summarize and refresh context or persist memory outside the model's window for long-horizon tasks and continuity. As models improve, they need less handholding. But context engineering will remain essential. It ensures agents stay coherent, efficient, and effective, especially in long, complex tasks. For more info, check out the original report from Anthropic in comments ↓ #EnterpriseAI #AIAgents #AIforBusiness

Prompt optimization is becoming foundational for anyone building reliable AI agents Hardcoding prompts and hoping for the best doesn’t scale. To get consistent outputs from LLMs, prompts need to be tested, evaluated, and improved—just like any other component of your system This visual breakdown covers four practical techniques to help you do just that: 🔹 Few Shot Prompting Labeled examples embedded directly in the prompt help models generalize—especially for edge cases. It's a fast way to guide outputs without fine-tuning 🔹 Meta Prompting Prompt the model to improve or rewrite prompts. This self-reflective approach often leads to more robust instructions, especially in chained or agent-based setups 🔹 Gradient Prompt Optimization Embed prompt variants, calculate loss against expected responses, and backpropagate to refine the prompt. A data-driven way to optimize performance at scale 🔹 Prompt Optimization Libraries Tools like DSPy, AutoPrompt, PEFT, and PromptWizard automate parts of the loop—from bootstrapping to eval-based refinement Prompts should evolve alongside your agents. These techniques help you build feedback loops that scale, adapt, and close the gap between intention and output

AI Fundamentals: Prompt Chains Powerful LLMs like GPT-4 can follow complex instructions, but building applications with less capable LLMs requires breaking a single, detailed instruction into a “chain” of simpler prompts. Here’s an overview of practically useful chaining techniques for LLMs... Some background: Typically, we interact with an LLM by passing the model some textual input (a prompt) and receiving textual output. Prompt chains are not much different from this! We still leverage the text-to-text structure of the LLM, but we prompt the model several times in sequence. Additionally, the exact sequence of prompts that we use might be dynamically adapted based on the language model’s output. Sequential chain: The first type of chain that is useful is just a sequential chain! Here, we just have a pre-defined sequence of prompts that we want to pass to the LLM. This sequence of prompts does not change depending on the model’s output, but we can pass the output of the LLM after each prompt as part of the input for the next prompting step. Router chain: What if we don’t want to always use the same sequence of prompts? In this case, we can use a router chain, which gives the LLM multiple options for the prompt to use next and lets the model choose. Typically, this is done by providing the LLM descriptions of each of the prompts that are available, then prompting the model to generate an output that indicates which of these prompts should be called. Different router chain options: Sometimes, asking the LLM to choose the next prompt that it should use can be difficult, especially if we are using a less capable LLM and have several different prompt options available. In these cases, there are a few different types of router chains we can use that reduce the cognitive load (or difficulty) of the LLM’s task: 1. Embedding router: Instead of asking the LLM to choose the next prompt, generate an embedding for the current prompt and all prompt options in the router chain and select the next prompt based on cosine similarity. 2. Sequential router: We can break the router decision into multiple, smaller steps (i.e., just form a sequential router chain!). In most cases, it is easiest to make each “step” in the sequential router chain a simple yes/no decision for the model (e.g., is this a science question? Is this a math question? etc.) TL;DR: Prompt chains are a huge part of successfully using LLMs in practice! If you can get a good sense for how to break a difficult problem into multiple, smaller steps that can each be solved with a prompt, you can probably build a variety of impressive AI applications. Put simply, prompt chains allow us to break difficult problems into sequences of smaller (dynamically-chosen) steps that the LLM can more reliably solve.

I've been going deep with AI as we prepare to release new Bonterms Standards. The warning 'this model can make mistakes' is hard-coded in the interface of LLMs for a reason. Here are some tips to help mitigate the risk. 𝗧𝗶𝗽𝘀 𝗳𝗼𝗿 𝗲𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲 𝘂𝘀𝗲 𝗼𝗳 𝗔𝗜 𝗶𝗻 𝗰𝗼𝗻𝘁𝗿𝗮𝗰𝘁 𝗱𝗿𝗮𝗳𝘁𝗶𝗻𝗴: 𝟭. 𝗥𝗼𝘁𝗮𝘁𝗲 𝗲𝗮𝗰𝗵 𝘁𝗮𝘀𝗸 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝗺𝗼𝗱𝗲𝗹𝘀. Paste the same prompts into Anthropic, ChatGPT and Gemini as you go. Models change constantly, even from one session to the next, so three perspectives help with quality control and completeness. 𝟮. 𝗪𝗼𝗿𝗸 𝘀𝗲𝗰𝘁𝗶𝗼𝗻 𝗯𝘆 𝘀𝗲𝗰𝘁𝗶𝗼𝗻. AI struggles with how provisions interconnect. A definition of "Confidential Information" may undercut a carefully constructed damages cap for data breach, but AI won't notice. Section-by-section review keeps both you and the AI focused on getting each piece right before moving to the next. 𝟯. 𝗦𝗲𝘁 𝘁𝗵𝗲 𝗽𝗮𝗰𝗲. The chat format and AI's tendency to assume you're in a rush work against the flow state you need when working through long, complex documents. Set your own pace. 𝟰. 𝗞𝗲𝗲𝗽 𝘁𝗵𝗲 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝘄𝗶𝗻𝗱𝗼𝘄 𝘀𝗺𝗮𝗹𝗹. Upload the agreement you're working on, but trim unnecessary exhibits or comparison examples. AI processes text statistically and can get overwhelmed by the noise. Rotate reference materials in and out rather than dumping everything at once. 𝟱. 𝗕𝗲 𝗮𝗰𝘁𝗶𝘃𝗲 𝗮𝗻𝗱 𝗶𝘁𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴. Iterate as you go rather than relying on a single master prompt. Give context to fix misunderstandings and stay focused on what is coming up in the analysis, instead of front-loading long rule lists. 𝟲. 𝗕𝗲 𝗰𝗿𝗲𝗮𝘁𝗶𝘃𝗲 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴. Assign a "red-team" role to act as opposing counsel, probing for ambiguities and vulnerabilities. Run a trace through critical mechanisms (warranty → indemnity → limitation of liability). Ask for targeted audits of definitions, cross-references, and surviving sections. This is where AI really shines: you can run any check or test you think of and get hits you might not otherwise see. 𝟳. 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗮𝗹𝗹𝘆 𝗿𝗲𝗺𝗶𝗻𝗱 𝘆𝗼𝘂𝗿𝘀𝗲𝗹𝗳 𝘁𝗵𝗮𝘁 𝗔𝗜 𝗰𝗮𝗻𝗻𝗼𝘁 𝘁𝗵𝗶𝗻𝗸 𝗼𝗿 𝗿𝗲𝗮𝘀𝗼𝗻. We don't yet have good language for what AI is doing, but it's not thinking. You're flinging ideas against a math table trained to sound helpful and authoritative. But obsequiousness is the last trait you'd want in a thinking partner. Don't let it decide issues for you. 𝟴. 𝗨𝘀𝗲 𝗣𝗗𝗙𝘀. Automatic numbering in Word documents baffles AI. Just upload the PDF. 𝟵. 𝗦𝗵𝗼𝘄 𝗶𝘁 𝘁𝗼 𝗮𝗻𝗼𝘁𝗵𝗲𝗿 𝗵𝘂𝗺𝗮𝗻. At my old firm, everything needed two sets of eyes before it went out: guidance emails, checklists, agreement drafts. No exceptions. Now I get to show every draft to 120 lawyers on the Bonterms Committee. AI is not a colleague or a replacement for one. Always get another human to review before you hit send.

Want to use GPT or Claude to help with something complicated and loosely defined — like building a comms plan for a company-wide initiative? Here’s a pattern that leveled up my prompt-fu like there's no tomorrow. ✅ Step 1: Set the stage, don’t trigger the model (yet) “I’m working on [insert project]. I’ll upload the background material. Don’t do anything until I say I’m ready and give you further instructions.” This gives the model time to ingest, not assume. If you don't do this, it’ll start guessing what you want — and usually guess wrong. This saves me tons of backtracking. ✅ Step 2: Kick off the interaction with clear context and a defined role “You’re an internal comms consultant helping the Chief Product & Tech Officer of a public company roll out a major change initiative. Interview me one question at a time until you’re 95% sure you have what you need.” This flips the default dynamic. Instead of hallucinating, the model starts by asking smart, clarifying questions — and only switches to generation once it knows enough to do the job right. This simple two-step pattern has leveled up how I work with LLMs — especially on open-ended, executive-level tasks. 🚀 It’s cut out something like 95% of my frustration with these tools. Curious if others are doing something similar — or better? What’s your go-to prompting move? #promptengineering #worksmarter #LLM #AIworkflow