AI Content Hub

Every prompt change should be tested against an eval suite before deployment. Create a set of 50-100 representative input/output pairs and run automated scoring after each prompt iteration. Without evals, you are optimizing blindly.

For complex tasks, don't try to write the perfect prompt in one go. Start with a simple instruction, see the result, and then iteratively add more constraints, context, and examples to refine the output until it meets your requirements. This is more effective than starting with an overly complex prompt.

Don't expect the first output to be perfect. Use the LLM's initial response as a draft and provide feedback for refinement. Ask it to elaborate, shorten, rephrase, or change style.

Don't expect the first output to be perfect. Use the LLM's initial response as a draft and provide feedback for refinement. Ask it to elaborate, shorten, rephrase, or change style.

Similar to text, image generation benefits from iteration. Start with a basic prompt, then refine it by adding details, styles (e.g., 'photorealistic', 'impressionist'), or camera angles.

For long texts, ask the LLM to summarize section by section, then summarize the summaries. This can be more effective than a single-shot summary of a very long document.

Ask the LLM to generate several different versions or drafts of a creative piece. You can then pick the best one or combine elements from different drafts.

If LLM-generated code has errors, provide the error message back to the LLM and ask it to debug and fix the code. Iterate until it works.

For complex tasks, reveal information progressively. Start with basic requirements, get initial output, then add more specific constraints or details.