# Tips for prompting based on LLM

| Feature / Style                 | OpenAI (GPT-4, GPT-3.5)                                                                  | LLaMA (LLaMA 2 & 3)                                                        | Mistral (Mistral 7B, Mixtral)                                            |
| ------------------------------- | ---------------------------------------------------------------------------------------- | -------------------------------------------------------------------------- | ------------------------------------------------------------------------ |
| **Role Structure**              | Uses `system`, `user`, and `assistant` roles explicitly                                  | No native role handling, but you can simulate it using text cues           | No role handling, but follows text cues and prompt templates             |
| **System Prompt Support**       | ✅ Full support — allows defining model behavior (e.g., `"You are a helpful assistant."`) | 🚫 No native support; must embed in user prompt manually                   | 🚫 No native support; simulate via prompt prefix                         |
| **Formatting Style**            | Natural conversation, JSON-compatible, markdown-friendly                                 | Structured, requires consistent formatting for few-shot and instruct modes | Concise and direct; works well with bullet lists, steps, or templates    |
| **Few-shot Learning**           | Highly effective with few-shot examples                                                  | Effective, especially with CodeLLaMA and LLaMA-Instruct variants           | Can benefit from few-shot, though prefers minimal examples               |
| **Chain-of-Thought Reasoning**  | Strong performance with "Let's think step by step" style prompts                         | Improves performance significantly with explicit CoT instructions          | Supports CoT well, especially in instruct-tuned variants                 |
| **Prompt Length Handling**      | Handles long prompts well (especially GPT-4-1 with large context windows)                | Medium capacity; recent models like LLaMA 3 support longer prompts         | Smaller context (e.g., 32K tokens), favors concise prompts               |
| **Fine-tuning Response Format** | Easily aligns to JSON, tables, and multi-part instructions                               | Needs more specificity to get consistent formatting                        | Consistent if given strict format constraints                            |
| **Use of Delimiters**           | Often uses `"""` or `###` to separate instructions from input                            | Suggested to separate examples and instructions clearly                    | Benefits from template-like structures, including consistent line breaks |
| **Multimodal Input Handling**   | GPT-4o supports images and audio                                                         | LLaMA 3 (future) may add modalities; current LLaMA is text-only            | Mistral is text-only for now                                             |