How Claude makes your business work better - 1
Notes: This article is adapted from a white paper posted on Antropic's homepage 'Prompt engineering for business performance' [1]
This is an in-depth analysis and adaptation of the whitepaper with the same title. (** Adapted using Claude)
Prompt engineering is an important tool for optimizing Claude's performance. Well-designed prompts improve Claude's output results, reduce deployment costs, and ensure that the customer experience is on brand.
One Fortune 500 company leveraged effective prompt engineering to build a Claude-powered assistant that answers customer questions more accurately and quickly.
When building generative AI models in your business, crafting effective prompts is critical to achieving high-quality results. With the right prompts, businesses can unlock the full potential of AI to increase productivity across a variety of tasks.
Anthropic's Prompting Engineering team is helping Fortune 500 companies build customer-facing chat assistants that answer complex questions quickly and accurately.
Benefits of designing effective prompts include
- Improved accuracy:Effective prompts can further reduce the risk of inaccurate output.
- Maintain consistency: Well-designed prompts ensure that Claude produces consistent results in terms of quality, format, relevance, and tone.
- Increase usability: Prompt engineering helps Claude deliver experiences that are customized for the desired audience and industry.
- Reduce costs: Prompt optimization can minimize unnecessary iterations and save money.
Claude is an AI assistant that can perform a variety of tasks through natural conversations with you. You can give Claude instructions in everyday language, just like you would ask a human, and the quality of the instructions you provide can have a significant impact on the quality of Claude's output. Clear, well-organized instructions are especially important for complex tasks.
The directives you give Claude are called "prompts". Prompts are often in the form of questions or instructions, and serve to guide Claude to generate relevant output. For example, if you give Claude the prompt "Why is the sky blue?", Claude will generate an appropriate answer. The text that Claude produces in response to a prompt is called a "response", "output", or "completion".
Claude is an interactive assistant based on a Large Language Model (LLM) that works through sequence prediction, which means that it considers both the prompt you type and the text it has generated so far, and builds a response by predicting the next word or phrase that will be most helpful. At the same time, Claude can only process information within a context window of a set length, so it can't remember previous conversations unless you include them in the prompt, and it can't open links.
If you want to have a conversation with Claude, you can use the web interface at claude.ai, or you can get started quickly via the API. The maximum length of your prompts is limited by the size of Claude's contextual window, so be sure to check the contextual window size of the model you're using.
More advanced techniques and tips for creating more effective prompts are covered in the topic 'Prompt Engineering'. The Prompt Engineering guide explains in detail how to design prompts with best practices, things to watch out for, real-world examples, and more. We encourage you to try different prompts and techniques and observe how they affect Claude's response and performance.
Anthropic also provides a large collection of prompt examples for different use cases in the 'Prompt Library'. If you need ideas or want to see how Claude can be utilized to solve a specific problem, the Prompt Library is a great place to start.
Finally, Anthropic also offers experimental "helper metaprompts" that guide Claude to generate prompts based on guidelines you provide. These can be useful for creating initial prompts or for quickly generating different prompt variations.
As you can see, Claude is a powerful AI assistant that can perform a variety of tasks through conversations with you. With prompt engineering and a library of prompts, you can unlock Claude's full potential. We encourage you to try out different prompts and share your results with Anthropic's Discord community.
Prompt engineering is a critical tool for companies looking to leverage Large Language Models like Claude to drive business outcomes. Well-designed prompts can help improve the quality of Claude's output, reduce deployment costs, and ensure that customer experiences are consistent and on-brand. In fact, one Fortune 500 company built a Claude-powered customer-facing assistant through effective prompt engineering, which led to significant improvements in accuracy and speed.
As organizations adopt generative AI models, effective prompting is essential to achieving high-quality results. With the right prompts, you can unlock the full potential of AI to increase productivity across a variety of tasks. Effective prompts can improve the accuracy of your output, ensure consistency in quality, format, relevance, and tone, and deliver experiences that are tailored to your desired audience and industry. They can also save you money by minimizing unnecessary repetitive tasks.
Here are three tips for utilizing prompted engineering in your business.
First. Apply step-by-step thinking
When solving a complex problem or making a decision, step by step is a technique for breaking down a problem into smaller steps that are analyzed and solved sequentially. This allows you to understand the problem more clearly and approach it in a systematic way. Especially when working with AI models, applying step by step can make the model's reasoning process explicit, increasing the logic and reliability of the answer.
There are two ways to apply step-by-step thinking to prompted engineering. The first is to use the "<thinking>" tag, and the second is to include "Think step by step" directly in the prompt.
1. Use the "<thinking>" tag:
The <thinking> tag allows you to explicitly represent the model's reasoning process. The user can see the model's thought process step by step, making it easier to understand the rationale behind the answer. In addition, the content within the <thinking> tag can be excluded from the final output or processed separately, avoiding exposing unnecessary information to the user.
2. Include "Think step by step" prompts:
Including "Think step by step" directly in the prompt is to instruct the model to analyze the problem step by step and show the intermediate steps. This method can be simpler and more intuitive than using the <thinking> tag.
The main differences between the two methods are
1. output format:
- Use the <thinking> tag to clearly separate the model's thinking process from the final answer.
- With "Think step by step", the model's thought process and final answer are presented in one output.
2. Easy to extract information:
- The <thinking> tag makes it easy for users to extract the part they want (final answer or intermediate thought process).
- When using "Think step by step", additional post-processing may be required to extract only the part the user wants.
3. Prompt engineering considerations:
- When using the <thinking> tag, you must design your prompts with the tag usage and structure in mind.
- When using "Think step by step", the model's thought process is directly exposed in the prompt, so the prompt should be written with this in mind.
The Think step by step technique can be used in a variety of areas, such as analyzing legal issues, constructing investment portfolios, creating marketing strategies, human resources assessments and feedback, and managing project schedules. However, you don't need to use it in every situation, and it's flexible enough to adapt to the nature of the task and your needs. For simple questions or clear instructions, it might be more effective to skip the step-by-step thought process and just present the final answer. On the other hand, for complex problems or tasks that involve helping users make decisions, it might be useful to utilize a step-by-step thought process to explain in detail the model's reasoning process.
It is very useful to analyze and solve problems using a step by step thinking technique. The <thinking> tag allows you to explicitly show the reasoning behind your model, making your answers more logical and reliable. Here are some examples of how it can be utilized
The step-by-step thinking technique utilizing the thinking> tag can be utilized in a variety of areas. When a model explicitly shows intermediate thought processes, users can easily see the logical flow of answers and ask additional questions or request corrections as needed. It also helps to debug and improve the model's reasoning process. However, this technique should not be used in every situation, and can be flexibly applied to suit the nature of the task and the needs of the user.
1. Analyze the legal issues:
Prompt: "Identify the issues in the case presented, and analyze your anticipated ruling based on relevant law and precedent, using the <thinking> tag to explain step-by-step."
2. Investment portfolio construction:
Prompt: "Considering the client's investment objectives and risk tolerance, suggest the optimal asset allocation for him/her. Show the portfolio construction process step-by-step with <thinking> tags."
3. Develop a marketing strategy:
Prompt: "Analyzing the characteristics of the product and the target audience, suggest effective marketing channels and messages. Using <thinking> tags, describe the strategy formulation process step by step."
4. Human Resources Assessment and Feedback:
Prompt: "Evaluate an employee's job performance and competencies, and suggest ways to improve. Use <thinking> tags to show the steps in the evaluation process and the process of deriving feedback."
5. Manage project schedules:
Prompt: "Analyzing the project's work and resources, develop an optimal schedule for the project. Using <thinking> tags, describe the process of developing the schedule step by step."
For example,
Prompt: Evaluate an employee's job performance and competencies, and suggest ways to improve. Use the <thinking> tag to show the steps in your evaluation process and feedback."
Explanation: This prompt asks Claude to analyze an employee's job performance and competencies and provide suggestions for improvement. By specifying that Claude thinks step-by-step using the <thinking> tag rather than simply drawing a conclusion, it encourages Claude to analyze the problem systematically and provide an accurate response.
The difference in prompting with and without the <thinking> tag can be described as follows
1. with the <thinking> tag:
- You are explicitly asking the model to show you the thought process of the intermediate steps.
- The model will explain in detail, step by step, the reasoning process that led to the final answer.
- Users can easily understand the model's thought process and see the logical basis for the answer.
- The content within the thinking> tag can be excluded from the final output or handled separately, so you don't expose unnecessary information to the user.
Example:
Prompt:
Evaluate an employee's job performance and competencies, and suggest ways to improve. Use the <thinking> tag to show us step-by-step the process of evaluation and deriving feedback."
Claude answers:
<Thinking>
Step 1: Know who your employees are and what they do
- Name: Jiyoung Kim
- Job Title: Representative
- Department: Marketing.
- Primary responsibilities: Marketing campaign planning and execution, market research, customer data analysis.
Step 2: Evaluate your performance
- Planning and executing marketing campaigns: You have above-average performance based on goal achievement and ROI analysis of recent campaigns. Creative ideas and organized execution stand out.
- Market research: Analyzed competitors and identified trends, generating useful insights, but needs to work on better utilization of findings.
- Customer data analysis: Good data analytics capabilities, contributing to customer segmentation and targeting. However, strategic recommendations based on analytics are somewhat lacking.
Step 3: Assess competencies
- Communication skills: Communicates and collaborates well within the team and with other departments. Possesses persuasive presentation skills.
- Problem-solving skills: Proactively seeks solutions to work challenges and offers creative ideas.
- Subject matter expertise: You have knowledge and skills in marketing and data analytics, but need to continue learning and developing your skills.
- Leadership: Your ability to lead and motivate team members has room for improvement.
Step 4: Identify ways to improve
- Better utilize your market research findings: Develop specific action plans based on research findings and enhance sharing with relevant departments.
- Strengthen data analytics capabilities: Focus on deriving strategic insights from customer data analysis and incorporate them into marketing strategies.
- Professionalize your work: Learn the latest trends and techniques in marketing and data analytics, and develop your skills through participation in external training programs.
- Develop leadership skills: Provide coaching and feedback to grow and empower team members, and drive communication and collaboration within the team.
</thinking>
Evaluation results and feedback:
Ms. Kim has shown excellent performance in planning and executing marketing campaigns, conducting market research, and analyzing customer data. We particularly appreciate her creative ideas, systematic execution, and data analysis capabilities.
However, we believe that you need to focus more on utilizing market research results and deriving strategic insights through data analysis. To this end, we recommend that you develop specific action plans and strengthen collaboration with relevant departments.
We also encourage you to further professionalize your work through continuous learning and competency development. We encourage you to learn about the latest trends and techniques in marketing and data analytics, and to participate in external training programs.
In terms of leadership skills, we expect you to be committed to the growth and empowerment of your team members. Providing coaching and feedback to team members, and driving communication and collaboration within the team will help you become a better leader.
We recognize your talent and growth potential and look forward to your continued contributions to the company. We hope you will take the suggested improvements into consideration and further develop your performance and capabilities.
2. without the <thinking> tag:
- The model presents only the final answer and does not reveal the intermediate thought process.
- It's hard for users to see how the model reached its conclusion.
- The rationale or logical flow of answers may be unclear.
- You may have difficulty debugging or refining the model's thought process.
Prompt: Evaluate an employee's job performance and competencies, and provide suggestions for improvement
Claude's answer:
Based on the employee information and job description, we evaluated your performance and competencies, and found that you performed well in the areas of marketing campaign planning and execution, market research, and customer data analysis. In particular, your creative ideas, systematic execution, and data analysis skills stood out.
However, there are some areas for improvement. We need to put more practical use of our market research findings and focus more on deriving strategic insights from data analysis. To this end, we recommend that you develop a concrete action plan and strengthen collaboration with relevant departments.
We also encourage you to continue to learn about the latest trends and techniques in marketing and data analytics to enhance your professionalism. We encourage you to develop your skills by participating in external training programs.
In terms of leadership, I would like to encourage you to provide coaching and feedback to your team members to help them grow and strengthen their capabilities, and to lead communication and collaboration within the team.
We recognize your high level of competence and growth potential, and look forward to seeing you take the suggested improvements to further develop your performance and capabilities.
Let's take a look at a possible application of "Think step by step" in the enterprise.
Example: Developing a marketing strategy for a new product launch.
Prompt:
My company is launching a new smartphone. The product's primary target audience is young people in their 20s and 30s, and we want to emphasize its innovative features and sleek design. Let's think step by step to create an effective marketing strategy.
Claude answers:
Step 1: Analyze your target audience.
- Understand the characteristics and needs of young people in their 20s and 30s
- Analyze the key features and design elements they expect in a smartphone
- Research their lifestyles, spending patterns, media consumption behaviors, etc.
Step 2: Identify product strengths and differentiators
- Innovative features: AI-powered camera, 5G support, high-capacity battery, etc.
- Sleek design: premium materials, bezel-less display, multiple color options, etc.
- Analyze how you stack up against the competition to highlight your points of differentiation
Step Three: Select marketing channels
- Select social media platforms frequented by your target audience (e.g., Instagram, YouTube, TikTok, etc.)
- Utilize online and mobile advertising channels (e.g., Google search ads, mobile app ads, etc.)
- Consider offline channels (e.g., outdoor advertising in college towns and younger demographics, pop-up stores, etc.)
Step Four: Develop a creative concept
- Establish key messages that highlight your product's strengths and differentiators
- Develop visuals and copy that appeal to the emotions of your target audience
- Maintain a tone and manner consistent with your brand identity
Step 5: Plan promotions and events
- Generate initial demand through pre-booked events
- Provide product experience opportunities (e.g., pop-up stores, road shows, etc.)
- Collaborating with social media influencers to promote your products
- Promote free gifts or discounts for customers who make a purchase
Step 6: Measure and optimize performance
- Set and monitor performance metrics for each marketing channel
- Optimize creative and messaging through A/B testing and more
- Analyze customer feedback and responses to modify and refine marketing strategy
Step 7: Manage long-term customer relationships
- Keep customers engaged by providing product usage tips and updates
- Foster brand loyalty by running loyalty programs
- Run promotions for existing customers at launch
By creating and executing your marketing strategy in the steps above, you'll be well on your way to a successful new product launch. It's important to be flexible and responsive to the insights gleaned from each step, and to continually monitor customer feedback to optimize your strategy.
Let's explain the difference between "Think step by step" with and without the prompt.
Prompt 1 (without Think step by step):
My company is launching a new smartphone. The main target audience for this product is young people in their 20s and 30s, and we want to emphasize its innovative features and sleek design. Please present your ideas for creating an effective marketing strategy.
Prompt 2 (Give Think step by step):
My company is launching a new smartphone. The primary target audience for this product is young people in their 20s and 30s, and we want to emphasize its innovative features and sleek design. Let's think step-by-step to create an effective marketing strategy.
Your model's answer to prompt 1 is likely to be a list of ideas for a marketing strategy-for example, they might list ideas like social media marketing, influencer collaboration, and giveaways. In this case, the connection or prioritization between the ideas is not clear, and it's difficult to see a systematic strategy development process.
On the other hand, if you assigned "Think step by step" to prompt 2, the model would present a step-by-step approach to creating a marketing strategy: analyze your target audience, identify product strengths and differentiators, select marketing channels, develop creative concepts, and so on, which would lead to subsequent steps such as planning promotions and events, measuring and optimizing performance, and managing long-term customer relationships.
This "Think step by step" allows the model to show a systematic thought process for solving a problem, so users can understand the context of strategy formulation and see the connections between each step. In addition, this step-by-step thought process can be used as a roadmap for actually creating and executing a marketing strategy.
In sum, without "Think step by step," you're more likely to get a list of sporadic ideas, whereas with it, you're likely to get an organized, sequential problem-solving process. So, for complex problems or situations that require strategy formulation, the "Think step by step" technique can be more effective.
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In the next post, we will discuss: 2. Utilizing Few-shot prompting , and third. Utilize prompt chaining techniques in more detail.
