Testing + Automation Resources

Types of Prompting Techniques while interacting with LLM models 1. Zero-Shot Prompting Definition: Direct query without examples. Example: "Translate 'Hello' to French." → "Bonjour." 2. One-Shot Prompting Definition: One example provided before query. Example: Prompt: "Example: English: 'Hello' → French: 'Bonjour'. Now, translate 'Goodbye'." Response: "Au revoir." 3. Few-Shot Prompting Definition: Multiple examples provided before query. Example: Prompt: "English: 'Apple' → French: 'Pomme'. English: 'Dog' → French: 'Chien'. Now, translate 'Cat'." Response: "Chat." 4. Chain-of-Thought (CoT) Prompting Definition: Encourages reasoning by breaking steps. Example: Prompt: "A store had 5 apples. It bought 10 more. Then sold 3. How many left? Think step by step." Response: "5 + 10 = 15, 15 - 3 = 12. Answer: 12." 5. Self-Consistency Prompting Definition: Generates multiple outputs and selects the best. Example: Model answers a math problem multiple times, and the most common result is chosen. 6. Role-Based Prompting Definition: Assigns a role for better response. Example: Prompt: "You are a financial expert. Explain Bitcoin in simple terms." 7. Instruction-Based Prompting Definition: Clear, explicit instructions for precise output. Example: "Summarize this paragraph in 20 words or less." 8. Multimodal Prompting Definition: Uses multiple input types (text, image, etc.). Example: "Describe this image in 2 sentences." (With an attached image)

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