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Formatting tokens add up fast. A code block with triple backticks and a language specifier adds ~5 tokens. Markdown headers, bullet points, and bold formatting each add 1-3 tokens. For high-volume APIs, stripping unnecessary formatting in prompts can reduce token counts by 5-15%.

Use model-specific prompting techniques: Claude likes XML, GPT likes markdown, Gemini handles multimodal well.

To get structured data, explicitly ask the LLM to format its response as JSON, a markdown table, or a numbered list. For example: 'Compare the top 3 smartphones and provide the output in a markdown table with columns for Model, Price, and Key Feature.'

Challenge: Provide an LLM with a small dataset in a CSV or markdown table format (e.g., sales data with columns: Date, Product, Amount). Ask it to provide a summary of key trends, identify the best-selling product, and calculate the total revenue.

When asking an LLM to analyze or extract data, clearly specify the desired output format (e.g., JSON, CSV, markdown table). This makes the output easier to parse and use programmatically.