标签：#prompting

서론 AI Native 커리어 캠프에 참여한 지 한달이 지났다. 처음 5일 간은 최신 AI 기술 트렌드나 현업에서 AX 전환이 어떻게 되고 있는지, 포트폴리오에 어떻게 연결하는게 좋을 지에 대한 강의를 들었고, 현재까지는 AI 리터러시를 높이기 위해 프롬프트 작성 방법에 대한 이론과 실습을 병행하고 있다. 나름 프롬프트 작성에 대한 노하우가 쌓였다고 생각했는데, 실습을 하다보니 개선할만한 패턴이 발견됐다. 따라서 이번에 프롬프트 작성 방식에 대한 회고를 해보려고 한다. 본론 문제 인식 실습은 개인과 조별로 진행을 한다. 개인 실습의 예로는 모호한 지시를 4요소(역할, 맥락, 지시, 형식)을 포함해 개선해나가는 식이고, 조별 실습은 사내 회의 시나리오가 주어지고 안건으로 올릴 요약 대시보드 표를 만드는 식이다. 내가 실습을 할 때는 바로 요청사항을 지시하기보다, 아래 내용을 포함해 프롬프트 생성 자체를 지시한다. (메타 프롬프팅이라고 한다.) 프롬프트 엔지니어링 전문가라는 역할을 부여 간단한 요청사항 추가 더 필요한 정보는 질문해달라고 언급 이 방법이 내가 직접 작성하는 것보다 빠르고 생각치 못한 부분도 챙겨줘서 애용한다. 그런데 비슷한 실습을 반복하면서 이런 방식이 AI 활용 역량 향상에 도움이 될까 하는 의문이 들었다. 또한 조별 실습과 발표를 할 때에도 어떤 흐름으로 할 지 AI에게 물어보고, 응답을 조합해서 발표하다보니 어딘가 알맹이가 빠진 듯한 느낌을 받았다. 그 느낌은 다른 조의 발표를 들으면서 뚜렷해졌다. 어떤 문제를 해결하는 프롬프트를 작성하고 개선하는 실습이 있을 때, 문제를 해결하기 위한 방법을 조원들과 논의하고 직접 작성해서 응답을 받아본 다음, 아쉬운 점과 개선 방향을 논의해 다시 지시하는 것을 반복하는 흐름이었다. AI가 개입한 지점은 요청 사항대로 응답한 부분 뿐이었다. 문제 의식을 가지고 지시를 하고, 결과물에 대한 판단은 사람의 몫이었다. 알맹이의 정체는 ??이었다. 목표 재정의 및 프롬프트 작성 방식 비교 AI 활용 역량을 키우기 위해선 기존 방식을 벗어나야했다. 또한 확실한 인사이트를 얻기 위해 실습마다 개인적인 목표를 정의했다. 실습은 내 업무에 반복적으로 사용할 직무 프롬프트를 만드는 것이었다. ( 링크 ) 상황을 정해서 프롬프트를 만드는 실습이었는데, 여기에 개인적으로 달성할 목표를 추가했다. 실습 목표 AI가 프롬프트도 잘 만들어주는 시대에, 나 혼자서도 역할/맥락/지시/형식을 채울 수 있는 감을 키우기 AI가 만들어준 것과 내가 목적에 맞게 작성한 것의 결과 비교해보고 핵심 인사이트 얻기 뭐든 초반에 직접 생각해보지 않고 AI에게 통으로 맡기는 습관 회고해보기 따라서 직접 작성 / 메타 프롬프팅 방식 두 가지를 모두 사용했다. 4요소(역할, 맥락, 지시, 형식)을 개별적으로 작성 직접 작성: 4요소를 참고해 하나의 프롬프트로 작성 (완벽주의가 있으니, 최소 목표를 지정하라는 개인적인 맥락 추가) 메타 프롬프팅: 4요소를 붙여넣고 이런 상황에 사용할 직무 프롬프트를 만들어 달라고 요청 응답을 비교했을 때 아래 기준을 만족하며, 두 방식 모두 작업을 이어나가는데에는 충분했다. 프롬프트 평가 기준 정확성: 원본(문서·이미지·검색 결과)과 맞는지 대조했는지 형식: 원하는 형식(표·길이 등)으로 잘 나왔는지 활용도: 즉시 쓸 수 있는지, 손이 얼마나 더 가는지 안전: 개인정보나 회사 기밀이 담긴 파일은 올리지 않았는지 그러나 직접 작성한 프롬프트에 ‘완벽주의가 있는 특성’을 추가한 차이로, 내 단점을 보완하고 시간 내 작업하는 데에 더 유리할 것이라 판단했다. 그 맥락 또한 메타 프롬프팅에 추가했으면 응답 결과물 차이가 거의 없었을 수 있다는 것도 포인트였다. 사람 손을 많이 탈 수록 결과물이 좋을 거라 생각한 부분도 빗겨나갔다. 두 방식의 결과에 대한 차이는 근소했지만, 목표를 정의하고 실험해보고 고민하는 과정을 통해 생각을 이어나간 과정은 유의미했다. 결론 작업에 대한 맥락만 명확하다면 직접 작성과 메타 프롬프팅 모두 기준에 충족되는 응답을 했다. 그렇다면 결과물의 질을 높이기 위

What Is Temperature in AI? (And How to Stop Getting Poetry When You Asked for a Grocery List) Remember Magic 8-Balls? Those plastic oracles you'd shake for life advice, only to get "Reply hazy, try again" when you asked if your crush liked you back? Imagine someone added a little dial on the bottom. Turn it all the way to zero and the thing becomes painfully predictable, only ever offering "Yes" or "Most likely." Crank it all the way up and suddenly it's inventing answers that never appeared in the original twenty options, things like "Ask your neighbor's cat" and "The moon suggests Thursday." That dial is temperature, and every AI language model has one. How the dial works Temperature is a setting, usually ranging from 0 to 2, that tells an AI model how much risk to take when picking the next word. The model calculates the probability of every possible next word, then has to pick one. At low temperatures, it plays it safe and picks the most probable option almost every time. At high temperatures, it's willing to gamble on unlikely choices further down the list. This is why you can ask ChatGPT the exact same question twice and get a straightforward answer on Monday and what appears to be surrealist fiction on Tuesday. When you ask ChatGPT to write a professional email at temperature zero, you'll get "Dear Sir or Madam, I am writing to follow up on our previous correspondence..." every single time you hit enter. Set temperature to 1.5 and it might open with "Greetings, fellow traveler of the inbox wilderness" because that phrasing, while statistically improbable, is now in play. Why boring is sometimes good At temperature zero, you get the most boring dinner guest imaginable. It always picks the single most likely next token (the technical term for a chunk of text, usually a word or part of one). No variety, no surprises, just the statistical favorite every single time. This turns out to be perfect when you need factual accuracy, code that actually compiles, or data

📺 Prefer to watch? 90-second YouTube Short · 💬 Telegram Originally published on software-engineer-blog.com . You already know the trick: add "think step by step" to your prompt and the model's answer gets better. Almost nobody explains why — and the real reason has nothing to do with motivation or effort. Mental model: A transformer spends a fixed stack of layers per token, so adding reasoning tokens doesn't make the model smarter — it buys it more compute passes and an external scratchpad to read from. The Problem: Fixed Compute per Token Here's the floor. When a transformer generates a token, it runs through the same neural network layers every time. The stack depth is fixed at model-creation time. Whether you ask it "2+2" or "Sara has 3 packs of 8 markers, gives 5 to each of 4 friends, how many left?", the model gets the same amount of layered computation to produce each output token. That compute budget never grows with problem difficulty. Now imagine you ask for just the answer: "Sara has 3 packs of 8 markers, gives 5 to each of 4 friends, how many left? Answer only the number." The model has to solve a three-step problem (multiply 3 × 8 = 24, multiply 4 × 5 = 20, subtract 24 − 20 = 4) in a single forward pass. It needs to hold "24" and "20" somewhere while computing the final step. But it's only got one forward pass, one set of layer outputs, and nowhere internal to stash intermediate values. So it guesses. It might say 19. It didn't get the math wrong because it's bad at math. It got it wrong because you handed it the wrong compute budget for the job. The Mechanism: Three Small Shifts Now ask the same question and let it write the steps: "Sara has 3 packs of 8 markers, gives 5 to each of 4 friends, how many left? Think step by step." Three mechanical things happen: 1. The model becomes a loop. Every token the model emits is appended to the input context and fed back in on the next forward pass. So if it writes "First, 3 × 8 = 24", that token sequence gets rea

Most prompts ask AI to explain things. The best ones ask it to show you something instead. That distinction sounds cosmetic. It isn't. It changes what the model generates, how you process it, and — more importantly — whether it actually sticks. I came across this idea while watching an interview with Amanda Askell — a philosopher and researcher at Anthropic whose work sits at the intersection of AI alignment and what you might loosely call Claude's inner life. She's a primary author of the document that defines Claude's values and character — the framework that governs how the model reasons when the rules run out. Almost as an aside near the end of the interview, she mentioned a prompting technique she uses to understand complex concepts. It stopped me cold. Not because it was elaborate. Because it was disarmingly simple, and it worked in a way I hadn't thought to ask for. The Exact Prompt Template Here it is, cleaned up and ready to use: I want to understand [concept]. Please explain it by writing a fable — an indirect, narrative version of the concept. The story should embody the concept completely without naming it directly. Ideally, the reader should only start to realize what the concept actually is near the end of the story. After the fable, add a short explanation that names the concept clearly and connects it back to the key moments in the story. That's it. No elaborate scaffolding. No chain-of-thought trigger. No persona assignment. Just a deliberate decision about the order in which understanding should arrive. Why This Works (and Why Direct Explanation Often Doesn't) When you ask AI to explain a concept directly, you get a definition. Definitions are accurate and forgettable. The model produces the statistical center of everything written about that concept — clear, complete, and utterly without friction. Friction, it turns out, is how things get encoded. When a concept arrives wrapped in a story, your brain does something different. It tracks characters,

Your prompts are fine. Your AI output is still garbage. You write carefully. You're specific. You ask for the format, the tone, the length. Hit enter. The AI responds with something that sounds like it was written by a committee of lawyers having a really bad day. Here's what you don't realize: You're not telling the AI to do something. You're describing the problem, and the AI is solving for the statistical average. The fix isn't more detailed instructions. It's three examples. That's it. Three. Not ten, not one, three. This post is the complete guide to few-shot prompting — the single highest-leverage move in prompt engineering. By the end, you'll have a template you can copy into any AI and watch your output quality jump 5x. Prefer watching? Here's the 3-minute version Otherwise, read on — everything's below. Why Instructions Fail (And Examples Work) When you tell an AI to "be funny," it's working off a fuzzy statistical average of everything labeled "funny" in its training data. When you show an AI what you think is funny, you're giving it a precise pattern to match. Here's the difference: ❌ Instruction: "Write a funny one-sentence movie summary" Result: A lukewarm joke that lands in the middle of the comedy bell curve. ✅ Pattern: Funny summary of The Lion King: Cub loses dad. Cub becomes king. Funny summary of Finding Nemo: Dad fish swims very far for his son. Funny summary of Titanic: [AI fills this in] Result: Boy meets girl. Boat meets iceberg. Oops. Same AI. Different universe. The only thing that changed: you showed it the pattern instead of describing it. The Science (Why This Isn't Magic) Language models predict the next token by pattern matching. They've seen millions of prompt-response pairs and learned: "When a prompt looks like this , the output usually looks like that ." One example could be a fluke. Two examples might be a coincidence. Three examples are clearly a pattern. The AI recognizes the pattern and completes it. This is exactly how humans l