Set your Publisher ID in src/config/ads.ts to display live ads.
Advanced Prompt Engineering & In-Context Learning
Few-Shot, Chain-of-Thought (CoT), System Personas, and Output Constraining
Prompt engineering is the empirical craft of designing textual contexts that steer foundation models toward high accuracy, structured outputs, and reasoned problem-solving.
Key Prompting Patterns
Zero-Shot Prompting: Asking the model to perform a task directly with zero prior examples.
Few-Shot Prompting: Providing 2 to 5 canonical input/output examples inside the prompt, aligning the model with exact desired output formats and edge-case handling.
Chain-of-Thought (CoT): Prompting the model with "Think step by step" or demonstrating reasoned intermediate steps, which forces the model to allocate compute tokens to reasoning before generating final answers.
Role & Persona Framing: Setting a system prompt ("You are a principal database administrator...") primes attention layers with relevant technical terminology and rigor.
const systemPrompt = `You are an expert sentiment classification engine.
Return ONLY valid JSON matching this schema:
{"sentiment": "POSITIVE" | "NEGATIVE" | "NEUTRAL", "confidence": number, "key_aspects": string[]}
Examples:
Input: "The checkout was lightning fast, but packaging was torn."
Output: {"sentiment": "NEUTRAL", "confidence": 0.88, "key_aspects": ["checkout speed", "damaged packaging"]}
Input: "Absolutely flawless customer support, resolved my billing in 3 minutes!"
Output: {"sentiment": "POSITIVE", "confidence": 0.99, "key_aspects": ["fast support", "billing resolution"]}`;Set your Publisher ID in src/config/ads.ts to display live ads.
Test Yourself With Exercise
Why does Chain-of-Thought (CoT) prompting drastically improve LLM accuracy on multi-step reasoning problems?