LLM Prompt Engineering in Practice: CoT, Few-Shot, and System Prompt Design

try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 801579) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } kanta13jp1 Posted on Apr 28 LLM Prompt Engineering in Practice: CoT, Few-Shot, and System Prompt Design #ai #automation #indiedev #buildinpublic LLM Prompt Engineering in Practice: CoT, Few-Shot, and System Prompt Design Stop getting mediocre answers from great models. Practical prompt engineering techniques from real indie dev usage. Why Prompt Design Matters Same model, same question — different prompts: Poor prompt design → "adequate answer" Good prompt design → "exceeds expectation" Cost implication: haiku + excellent prompt vs sonnet + sloppy prompt → Same quality, 4× cost difference Enter fullscreen mode Exit fullscreen mode 1. System Prompt: Define the Role Precisely # ❌ BAD: vague role system = "You are an AI assistant." # ✅ GOOD: specific role + constraints + output format system = """You are a productivity coach for indie developers. ## Your role - Prioritize daily tasks - Identify focus blockers - Give actionable improvement suggestions ## Response rules - Maximum 3 suggestions (more creates confusion) - Avoid overly technical jargon - End each suggestion with a concrete "try this" action ## Do not - Give medical, legal, or investment advice - Frame suggestions as criticism of the user's behavior""" Enter fullscreen mode Exit fullscreen mode Claude API: response = client.messages.create( model="claude-haiku-4-5", system=system, # system parameter, not injected in messages messages=[{"role": "user", "content": user_input}] ) Enter fullscreen mode Exit fullscreen mode 2. Chain of Thought (CoT): Make the Model Think Step by Step # ❌ BAD: ask for conclusion directly (reasoning gets sloppy) prompt = "Should I do this task today?" # ✅ GOOD: specify the reasoning steps prompt = """Evaluate the following task. Task: {task} Reasoning steps: 1. Check deadline (today / this week / later) 2. Check impact (affects only me / affects others) 3. Estimate time (under 15 min / over 1 hour) 4. Based on above: do today / do this week / defer Think through each step before giving your final answer.""" Enter fullscreen mode Exit fullscreen mode When CoT helps vs. doesn't: ✅ Use CoT for: Multi-condition decisions Math and logical reasoning Bug root cause analysis Text quality evaluation ❌ Skip CoT for: Simple classification (sentiment analysis) Short text generation → haiku + simple prompt is enough Enter fullscreen mode Exit fullscreen mode 3. Few-Shot: Lock Down the Output Format With Examples # ❌ BAD: describe the format only prompt = "Classify this task into 3 priority levels." # ✅ GOOD: show examples prompt = """Classify each task by priority. Example: Input: "Gather documents for tax return (deadline: March 15)" Output: Priority: HIGH | Reason: hard deadline, legal requirement | Today's action: locate withholding slip Input: "Organize bookshelf" Output: Priority: LOW | Reason: no deadline, low impact | Today's action: add to backlog list Now classify: Input: {task} Output:""" Enter fullscreen mode Exit fullscreen mode 4. Prompt Caching — 89% Cost Reduction # Cache the system prompt after the first request messages = [ { "role": "user", "content": [ { "type": "text", "text": long_system_prompt, # 2000+ tokens "cache_control": {"type":…

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