Real-world use cases
Discover how prompt engineering can be applied to a wide variety of tasks. These examples showcase the versatility of LLMs when guided by well-crafted prompts.
Content creation (blog post outline)
Generate outlines, drafts, or ideas for various types of content like blog posts, articles, marketing copy, or social media updates.
Example prompt:
Conceptual output:
Tips for success:
- Specify the target audience, tone, and desired length/format.
- Provide keywords or key themes to include.
- Use it for brainstorming and then refine the generated content.
Text summarization
Condense long documents, articles, or conversations into concise summaries, extracting key information and main points.
Example prompt:
Conceptual output:
Tips for success:
- Specify the desired length or format of the summary (e.g., one paragraph, bullet points).
- Indicate if you want an abstractive (rephrased) or extractive (key sentences) summary.
- For very long texts, you might need to summarize in chunks.
Question answering (closed domain)
Answer questions based on a provided context or document. Useful for creating FAQs from internal knowledge bases.
Example prompt:
Conceptual output:
Tips for success:
- Clearly separate the context/document from the question.
- Ensure the answer is directly inferable from the provided text.
- For open-domain Q&A (general knowledge), less context might be needed, but specificity in the question is still key.
Creative writing (story idea)
Brainstorm story ideas, character descriptions, plot points, or even generate short pieces of creative writing.
Example prompt:
Conceptual output:
Tips for success:
- Provide genres, themes, character archetypes, or specific elements you want to include.
- Ask for multiple variations to get a range of ideas.
- Use a higher temperature setting for more novel and unexpected creative outputs.
Code generation
LLMs can generate code snippets, functions, or even entire applications in various programming languages based on natural language descriptions.
Example prompt:
Conceptual output:
Tips for success:
- Be specific about the programming language, libraries, and desired functionality.
- Provide examples of input and expected output if possible (few-shot).
- For complex code, break down the request into smaller, manageable parts.
- Always review and test generated code thoroughly.
Code explanation
Understand complex code snippets or functions by asking the LLM to explain them in plain language.
Example prompt:
Conceptual output:
Tips for success:
- Provide the full code snippet you want explained.
- Ask for explanations at different levels of detail (e.g., high-level overview vs. line-by-line).
- Specify the programming language if it might be ambiguous.
These are just a few examples! The possibilities are vast. The key is to understand your goal, choose the right prompting techniques, and iterate based on the results.
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