• Define prompt engineering as the process of creating structured instructions that maximize the effectiveness of responses from LLMs.     

• Discuss why it’s important, especially in contexts like customer service, content generation, and research.     

• Briefly touch on how prompt engineering can reduce errors and increase the utility of AI outputs.     

• Clear Instructions: Explain that LLMs respond best to detailed, specific prompts. If an answer is too simple, request “expert-level” responses, and if it’s too long, specify “brief summaries.”     

• Persona Adoption: Instructing the model to adopt a specific tone or role (e.g., “Pretend you’re a historian discussing World War II”) can add context, making responses more relevant.     

• Formatting and Structure: Using delimiters like “triple quotes” or XML tags can help the model understand which sections are important or should be treated differently.     

• Task Decomposition: Breaking complex tasks into simpler, sequential steps for better accuracy.     

• Embedding Context: Providing reference text to reduce the chances of AI “hallucinating” or making up information.     

• Wrong Prompt: “Tell me about health tips.”     

• Correct Prompt: “List five evidence-based health tips for improving mental wellness, including practical steps to implement each one.”     

• Wrong Prompt: “What is sustainability?”     

• Correct Prompt: “Explain the concept of sustainability in everyday life and provide three actionable steps individuals can take to live more sustainably at home.”     

• Wrong Prompt: “Give advice on working from home.”     

• Correct Prompt: “Provide five effective strategies for increasing productivity while working from home, focusing on time management and workspace organization.”     

• Wrong Prompt: “What is SEO?”     

• Correct Prompt: “Define SEO and discuss three key strategies that small businesses can implement to improve their search engine rankings in 2024.”     

• Wrong Prompt: “How can I save money?”     

• Correct Prompt: “List ten practical tips for saving money on everyday expenses, with a focus on budgeting techniques and smart shopping habits.”     

   

Write Clear Instructions     

Detailed instructions provide more relevant answers. Ask for specific word counts, step-by-step responses, or formats.     

Example: “Summarize the below customer feedback in three bullet points.”     

Use Delimiters for Distinct Parts of Input     

Use separators like """ or XML tags to designate text blocks, making the input clearer.     

Example: “Summarize the text inside triple quotes in a paragraph.”     

Split Complex Tasks into Subtasks     

Breaking tasks into parts can improve accuracy.     

Example: For market research, first gather statistics on market size, then analyze trends, and finally provide a summary.     

Give Models Time to “Think”     

For complex queries, request the model to perform a “chain of thought” analysis to get logical, step-by-step reasoning.     

Example: When calculating costs or solving equations, ask for each step to ensure accuracy.     

• Customer Service Automation: In customer service, models can be prompted to categorize user issues or respond to FAQs.     

Example: A telecom company might use a prompt like, “Identify the issue based on the following customer query and suggest the next steps.”     

• Content Creation and SEO: Prompt engineering can help generate optimized content for blogs, social media, or websites.     

Example: For a blog post on “healthy eating,” a prompt might specify: “Write an introductory paragraph with benefits of healthy eating, and follow with three main health benefits.”     

• Educational Support: Models can assist in tutoring or offering explanations for academic concepts.     

Example: For math help, a tutor might prompt, “Explain the Pythagorean theorem with a real-world example.”     

• Summarization and Documentation: Summarizing long documents or generating reports based on structured prompts is also an efficient use of LLMs.     

Example: A lawyer might ask, “Summarize the key points of this contract with special attention to confidentiality clauses.”     

• Use Reference Text: Feeding a model with specific data or documents can significantly improve response accuracy.     

Example: For legal questions, referencing specific documents or statutes can help the model provide citations and avoid incorrect answers.     

• Evaluate and Test Changes Systematically: Continuous evaluation helps ensure prompts consistently yield high-quality outputs. Developing a set of prompts for different scenarios can streamline responses over time.     

Example: An e-commerce platform might test various customer service prompts to improve response quality and user satisfaction.     

• External Tool Integration: Using models alongside other tools like databases or calculators can fill gaps in the model’s capabilities.     

Example: A financial advisor might use an AI with an embedded calculator to analyze investment portfolios more effectively.     

• Context Management: For long conversations or projects, summarizing past exchanges and specifying continuity in responses can prevent context loss.     

• Feedback Loops: By frequently assessing model performance and modifying prompts, users can establish a feedback loop, making models progressively more effective.     

• Documentation of Effective Prompts: Keep a record of prompts that consistently work well. This is especially helpful for training teams or creating standardized workflows.     

• Experimentation: Encourage trying different prompt variations. For instance, testing how a “professional” versus a “casual” tone impacts user engagement can yield insights.