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Tutorial Intermediate

Effective Few-Shot Prompting

Few-shot prompting improves LLM outputs by providing examples. Step 1: Define your task clearly. Step 2: Create 3-5 diverse, high-quality examples. Step 3: Format consistently (input→output). Step 4: Order examples from simple to complex. Step 5: Test and refine your examples based on model outputs.

Estimated time: 15 minutes
few-shotexamplesprompting
Trick

Use XML Tags for Structured Output

When you need structured responses, instruct the model to use XML tags. For example: 'Provide your answer in the following format: <summary>Brief overview</summary> <pros>List of advantages</pros> <cons>List of disadvantages</cons>'

structured outputxmlformatting
Challenge Advanced

Minimal Token Maximum Impact

Challenge: Create the most effective prompt for a complex task using the fewest possible tokens. Test different approaches and measure both output quality and token efficiency.

token optimizationefficiencyprompt engineering
Challenge Advanced

Cross-Model Consistency

Challenge: Design a prompt that produces consistent, high-quality results across different LLM models (GPT, Claude, Gemini). Test and refine until you achieve similar outputs.

consistencycross-modelrobustness

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