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Using the `response_format: {type: 'json_schema'}` parameter (OpenAI) or XML tags with output parsers (Claude) ensures the model always returns valid structured data. This eliminates regex hacks, reduces prompt length, and makes your pipelines far more reliable.

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

Use delimiters like triple quotes (''' '''), triple backticks (``` ```), or XML tags (<example></example>) to clearly separate instructions from the content you want the model to process. This helps prevent the model from getting confused.

When providing text for an LLM to process (e.g., summarize, translate), use clear delimiters like triple quotes ('''Text here''') or XML tags (<text>Text here</text>) to separate your instructions from the content.

If you need structured output (e.g., JSON, XML, a list), explicitly ask the LLM to provide it in that format. This often yields better and more parsable results. Example: 'Provide your answer as a JSON object with keys 'name' and 'summary'.'

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>'

Use delimiters like triple backticks (```), XML tags (<tag></tag>), or quotes ("") to clearly separate different parts of your prompt, such as instructions, context, examples, and input data.

When using Claude, make use of its XML tags for better structure in your prompts.

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>'