Most people think AI is inconsistent. It isn't. The quality of the output depends largely on the quality of your prompt. If you've ever thought, "ChatGPT isn't giving me what I asked for," chances are your prompt needs improvement. Here's a simple framework that professionals use to get significantly better results from any AI model. The 5-Part Prompt Formula 1. Role Tell the AI who it should be. Example: "You are a senior marketing strategist with 10 years of experience." This helps the AI respond from the right perspective. 2. Task Clearly define what you want. Instead of: Write about AI. Try: Write a LinkedIn post explaining how AI agents help small businesses save time. Specific tasks produce specific results. 3. Context Give the AI background information. Example: The audience consists of small business owners with no technical knowledge. Context helps AI tailor its response to the right audience. 4. Constraints Set clear rules. Examples: Keep it under 250 words. Use simple English. Avoid technical jargon. Include one real-world example. End with a question. Constraints improve consistency and reduce unnecessary revisions. 5. Output Format Tell AI exactly how you want the response structured. Example: A strong hook 3 short paragraphs Bullet points where appropriate A conclusion 5 hashtags When you define the format, the output becomes much easier to use. Complete Prompt Example You are a senior marketing strategist. Write a LinkedIn post explaining how AI agents help small businesses automate repetitive work. The audience consists of business owners with no technical background. Use simple language. Keep it under 250 words. Include one practical example. End with a question that encourages discussion. Format the response with a compelling hook, three short paragraphs, and five relevant hashtags. Why This Framework Works Most people give AI a sentence. Experts give AI instructions. The more clearly you communicate:

Most companies don't fail at Forward Deployment because their engineers aren't good enough. They fail because they mistake customer success for product progress. That's a dangerous assumption. Forward Deployed Engineers (FDEs) have become one of the most talked-about roles in enterprise software. Palantir built an entire operating model around them. Companies like OpenAI, Anthropic, Databricks, and others are investing heavily in similar deployment teams. But after reading through engineering playbooks, deployment case studies, and discussions from experienced FDEs, one pattern kept appearing. The companies that win aren't the ones with the smartest Forward Deployed Engineers. They're the ones that learn the fastest from them. That's a very different objective. Most people describe an FDE as "an engineer who works closely with customers." That's technically correct. But it completely misses the real purpose of the role. A great Forward Deployed Engineer isn't there to solve customer problems. They're there to discover which customer problems deserve to become product capabilities. That distinction changes everything. Think about Palantir. Their engineers don't simply configure software and move on. They embed deeply inside customer environments, understand operational workflows, solve incredibly specific problems, and then look for patterns across completely different organizations. A government agency struggles with entity resolution. Months later, a pharmaceutical company runs into what looks like an unrelated issue. Then a financial institution experiences something surprisingly similar. Three different customers. Three different industries. One underlying engineering problem. That's the moment where a company stops thinking like a consultancy and starts thinking like a platform company. Instead of shipping three custom solutions, the problem becomes a reusable capability inside the core product. Every deployment makes the next deployment cheaper. Every customer makes the product smarter.

Small thought experiment: Imagine the smartest AI model in the world existed today. Perfect reasoning. Perfect coding. Zero hallucinations. Now imagine giving it to a company where workflows are messy, people explain problems differently, processes live in random spreadsheets, and half the knowledge exists only in someone's head. Would the AI alone solve anything? Probably not. This is why I think FDEs are interesting. Not because they write code. Because they're becoming translators. One language = what users say they want. Another language = what users actually need. And those two are often completely different. Customer: "We need a dashboard." Reality: "We actually need fewer meetings." Customer: "We need AI search." Reality: "Nobody can find information quickly." Customer: "We need automation." Reality: "Our process is broken." The crazy part? The highest leverage work often isn't building faster. It's discovering the real problem hiding underneath the request. Maybe the future of engineering isn't just building. Maybe it's becoming insanely good at translating chaos into systems. 👇 Curious: What's a time when the real problem turned out to be completely different from what people originally asked for?

I keep seeing one role pop up again and again in AI companies: Forward Deployed Engineer (FDE). And I think it's changing how we define a “great engineer.” A few years ago, the idea was simple: Build the product → ship it → let customers figure out how to use it. Now it feels different. The engineers creating the biggest impact are often the ones sitting closest to real users. They’re jumping into customer calls, understanding messy workflows, finding bottlenecks, and shipping fixes fast. They’re not just writing code. They’re connecting dots. That’s what makes FDEs interesting to me. Because when model quality, frameworks, and tools become easier for everyone to access, the edge shifts somewhere else, understanding context. Knowing why people struggle can sometimes matter as much as knowing how to build. Maybe the future “10x engineer” isn't just the person with the deepest technical knowledge. Maybe it's someone who can code, communicate, and handle ambiguity without getting stuck. Curious what others think 👇 Is FDE just another AI buzzword, or do you think it becomes a major engineering role over the next few years?

Many people think Forward Deployed Engineers have a completely different skill set. In reality, most professionals already have pieces of the puzzle. Think about your current role. Have you ever: ✅ Gathered requirements from stakeholders? ✅ Designed a solution before writing code? ✅ Presented technical ideas to non-technical people? ✅ Worked with customers or end users? ✅ Led a project from idea to implementation? ✅ Measured business impact after deployment? If you answered "yes" to any of these, you've already been developing FDE skills. The difference is that FDEs combine these skills into a repeatable framework for solving customer problems and driving outcomes. Quick Exercise Give yourself 1 point for each item above. 0-2 points: Early Stage Builder 3-4 points: Emerging FDE 5-6 points: Strong FDE Potential Discussion What's one project you've worked on that involved more than just coding? Describe: 1. The problem 2. The solution 3. The business outcome I'll help identify which FDE skills you demonstrated in that project. 👇 Share your example below.