Recap — When Each Pattern Wins

# When Each Pattern Wins Three high-impact prompting techniques cover the bulk of production work. Each solves a specific problem. ## Pattern 1 — Few-Shot Prompting Give the model 2-5 input/output examples before the actual request. ``` Classify the sentiment of customer reviews. Examples: Review: "Loved the shoes, fit perfectly." Sentiment: positive Review: "Quality is okay but shipping took forever." Sentiment: mixed Review: "Worst purchase ever, returning immediately." Sentiment: negative Now classify: Review: "Decent product but customer service was unhelpful." Sentiment: ``` Why it works: the model pattern-matches your examples and produces outputs in the same format. When to use: - Tasks with non-obvious output format (custom labels, specific structures). - Stylistic consistency (always use specific phrasing). - Reducing variance in outputs. Choosing examples: - Diverse — cover the cases you'll see. - Representative — match the distribution of real requests. - Include edge cases — especially the ones you've seen the model handle wrong. Limits: too many examples (10+) burns tokens for diminishing returns. 3-5 examples is the sweet spot. ## Pattern 2 — Chain-of-Thought (CoT) Ask the model to reason step-by-step before producing the final answer. ``` Question: A store has 50 apples on Monday. They sell 60% on Tuesday and receive 30 more on Wednesday. How many do they have now? Think through this step by step before giving the final answer. ``` vs. zero-shot: ``` Question: A store has 50 apples on Monday. They sell 60% on Tuesday and receive 30 more on Wednesday. How many do they have now? Answer: ``` The CoT version usually gets it right; the zero-shot version often fails on multi-step math. Why it works: the model literally generates intermediate steps. Each step becomes part of the next token's context. Errors compound less. Modern frontier models (Claude 4, GPT-5) often do CoT implicitly — they include reasoning blocks before final answers. For mid-tier models, you may need to prompt it explicitly. When to use: - Multi-step reasoning. - Math problems. - Logic puzzles. - Code debugging. - Anything where the model needs to "think before speaking". When NOT to use: - Simple factual recall ("What's the capital of France?") — adds latency without benefit. - Pattern-matching tasks where the right answer is immediate. Trade-off: CoT increases output tokens (cost + latency). For high-volume simple tasks, skip it. ## Pattern 3 — Structured Output Constrain the model to produce JSON, XML, or other structured formats. ``` Output a JSON object with the following schema: { "person_name": "string", "email": "string", "phone": "string or null", "city": "string or null", "confidence": 0.0 to 1.0 } Extract from this text: "Hi, my name is Anuj Saini, you can reach me at anuj@example.com..." ``` Modern model APIs (Claude, GPT, Gemini) support **structured output mode** — pass a JSON schema, get guaranteed valid JSON back. Use it. When to use: - Pipeline outputs (downstream systems will parse them). - Extraction tasks. - Classification with multiple fields. - Any task where the response feeds into code. Practical tip: even when the API supports schema enforcement, include the schema in the prompt as well. Belt and suspenders. ## Combining patterns Production prompts often use all three: ``` SYSTEM: You are an extraction assistant. Read the customer message and extract key information. EXAMPLES (few-shot): Message: "Hi, I'm John, john@x.com, NYC, just bought widget 5 days ago, broken." Output: { "name": "John", "email": "john@x.com", "city": "NYC", "purchase_days_ago": 5, "issue": "broken product", "extraction_confidence": 0.95 } (more examples...) INSTRUCTION: For the message below, think through what fields are present, then output JSON matching the schema. Schema: { "name": "string", "email": "string", "city": "string or null", "purchase_days_ago": "integer or null", "issue": "string", "extraction_confidence": 0.0-1.0 } Message: "Hi I'm Priya from Bangalore. Bought shoes last week, want to return them." ``` The CoT element ("think through") + few-shot pattern + schema = consistent, accurate extraction. ## ReAct, Tree-of-Thought, and others Beyond the basics: - **ReAct** (Reasoning + Acting) — interleave reasoning and tool use. The model decides "I need data" → calls a tool → gets result → continues reasoning. Module 2 lesson on tool use covers this. - **Tree-of-Thought** — explore multiple reasoning branches in parallel, pick the best. Mostly research-grade; rare in production. - **Self-consistency** — generate multiple outputs with high temperature, take the majority vote. Improves accuracy for hard problems at 5-10x cost. For most production work, master few-shot + CoT + structured output. The rest is optimization on the margin. ## Common pattern mistakes 1. **Few-shot examples too similar.** Cover variety; show edge cases. 2. **CoT fo