11 Ways to Get Better LLM Outputs with Claude

The article provides 11 tips for designing effective prompts for the AI model, Claude 2.1. The tips include using XML tags to separate instructions from context, being direct and concise, helping Claude with the output, assigning a role to Claude, giving Claude time to think, providing examples, allowing Claude to say "I don't know", placing long documents before instructions, thinking step by step, breaking complex tasks into steps, and prompt chaining. The article also suggests a test-driven prompt engineering approach to ensure the prompt's effectiveness across various use-cases. The article concludes by offering assistance from Vellum for evaluating prompts and experimenting with models.

Key takeaways

Claude, unlike GPT models, requires a different type of prompting design due to its unique training methods and data. Using XML tags to separate instructions from context is one effective technique.
Being direct, concise, and specific in your instructions can yield better results. Assigning a role to Claude and providing a specific format for the output can also enhance the quality of the responses.
Other techniques include giving Claude time to think, providing examples, allowing Claude to say "I don't know", placing long documents before instructions, thinking step by step, breaking complex tasks into steps, and prompt chaining.
Test-driven prompt engineering can help ensure the effectiveness of your prompts across various use-cases. Constant iteration and monitoring are key, and tools like Vellum can assist in evaluating and managing prompts.

11 Ways to Get Better LLM Outputs with Claude

Key takeaways

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