Get Started
Prompt Engineering
- Introduction
- Model Basics
- Prompt Structures
- Clarity & Specificity
- Using Context
- Role Instructions
- Step-by-Step
- Handling Ambiguity
- Creativity vs Precision
- Using Examples
- Advanced Techniques
- Troubleshooting
- Common Pitfalls
- Evaluating Quality
- Real-World Examples
- Prompt Templates
- AI Tasks
- Safety & Ethics
- Multimodal Prompts
- Data Extraction
- Conversation
- Personalization
Using Context Effectively
AI models perform better when they have the right context. Providing background information, examples, or constraints helps guide the model. Context can include the user's goals, previous interactions, relevant facts, or even the desired tone and style.
Why Context Matters
- Guides the AI: Context helps the model understand what you want and why.
- Reduces ambiguity: The more information you provide, the less likely the AI is to misinterpret your request.
- Enables personalization: Context allows you to tailor responses to specific users or situations.
Example
You are a travel agent. Suggest three destinations for a family with young children who love nature.Expanded Example:
You are a travel agent. The family has previously visited national parks and enjoyed hiking and wildlife tours. Suggest three new destinations that offer similar experiences, and explain why each would be a good fit for them.Set the scene! The more relevant context you provide, the more tailored the response. If the AI needs to remember previous steps, include that information in your prompt.
Best Practices for Using Context
- Include relevant background information or user preferences.
- Reference previous steps or outputs if the task is multi-stage.
- Specify any constraints, such as budget, time, or style.
- Use examples to clarify expectations.