Prompting techniques

𝐆𝐨𝐨𝐝 𝐩𝐫𝐨𝐦𝐩𝐭𝐢𝐧𝐠 𝐢𝐬𝐧’𝐭 𝐚𝐛𝐨𝐮𝐭 𝐚𝐬𝐤𝐢𝐧𝐠 𝐛𝐞𝐭𝐭𝐞𝐫 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 𝐢𝐭’𝐬 𝐚𝐛𝐨𝐮𝐭 𝐠𝐞𝐭𝐭𝐢𝐧𝐠 𝐛𝐞𝐭𝐭𝐞𝐫 𝐚𝐧𝐬𝐰𝐞𝐫𝐬

In most AI projects, the difference between mediocre outputs and powerful results often comes down to how prompts are designed.
That’s why understanding different prompting techniques is becoming a must-have skill for anyone working with LLMs.

𝐇𝐞𝐫𝐞’𝐬 𝐚 𝐛𝐫𝐞𝐚𝐤𝐝𝐨𝐰𝐧 𝐨𝐟 𝐭𝐡𝐞 𝐦𝐚𝐣𝐨𝐫 𝐋𝐋𝐌 𝐩𝐫𝐨𝐦𝐩𝐭𝐢𝐧𝐠 𝐭𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬 𝐭𝐡𝐚𝐭 𝐜𝐚𝐧 𝐝𝐫𝐚𝐦𝐚𝐭𝐢𝐜𝐚𝐥𝐥𝐲 𝐥𝐞𝐯𝐞𝐥 𝐮𝐩 𝐲𝐨𝐮𝐫 𝐫𝐞𝐬𝐮𝐥𝐭𝐬:

𝟏. 𝐂𝐨𝐫𝐞 𝐏𝐫𝐨𝐦𝐩𝐭𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬
* Zero-shot prompting: Ask the AI directly without giving examples.
* One-shot prompting: Provide one example to set the format or structure.
* Few-shot prompting: Share multiple examples so the model understands your intent better.

𝟐. 𝐑𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠-𝐄𝐧𝐡𝐚𝐧𝐜𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬
* Self-consistency: Ask for multiple answers, then select the most accurate or common.
* Tree-of-Thought: Let the model explore different reasoning paths before finalizing.
* Chain-of-Thought: Force step-by-step reasoning instead of direct answers.
ReAct: Combine reasoning with tool usage or actions.

𝟑. 𝐏𝐫𝐨𝐦𝐩𝐭 𝐂𝐨𝐦𝐩𝐨𝐬𝐢𝐭𝐢𝐨𝐧 𝐓𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬
* Prompt chaining: Use the AI’s previous response as the next input.
* Dynamic prompting: Insert real-time or updated variables.
* Meta prompting: Ask the AI to evaluate and improve its own output.

𝟒. 𝐈𝐧𝐬𝐭𝐫𝐮𝐜𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐑𝐨𝐥𝐞-𝐁𝐚𝐬𝐞𝐝 𝐏𝐫𝐨𝐦𝐩𝐭𝐢𝐧𝐠
* Instruction prompting: Give direct, clear instructions.
* Role prompting: Ask the AI to act like a domain expert or specific persona.
* Instruction + Few-shot: Combine clear instructions with examples for precision.

𝟓. 𝐌𝐮𝐥𝐭𝐢𝐦𝐨𝐝𝐚𝐥 𝐏𝐫𝐨𝐦𝐩𝐭𝐢𝐧𝐠
* Image + text prompting: Feed both text and visuals for richer context.
* Audio/video prompting: Enable the model to interpret voice or video input.

Prompting isn’t just an input trick. It’s a structured approach to guide the AI’s reasoning process and the difference shows in the quality of outputs.

𝐖𝐡𝐢𝐜𝐡 𝐨𝐟 𝐭𝐡𝐞𝐬𝐞 𝐩𝐫𝐨𝐦𝐩𝐭𝐢𝐧𝐠 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬 𝐝𝐨 𝐲𝐨𝐮 𝐮𝐬𝐞 𝐦𝐨𝐬𝐭 𝐨𝐟𝐭𝐞𝐧 𝐢𝐧 𝐲𝐨𝐮𝐫 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬?