Have you ever wished your data could do more for you? Baserow AI lets you use artificial intelligence (AI) to streamline your work.
Baserow AI works with data in your Baserow table, making it easier to organize everything and find connections you might have missed before. You can analyze what your customers are saying in reviews, write the first draft of a marketing plan, create job descriptions, or even help you write blog posts.
Learn how to activate and configure the AI field in Baserow.
AI can give your most important work processes a boost. Imagine spending less time on repetitive tasks and getting more done. That’s what Baserow AI can help you achieve. Let’s explore some specific examples of how this is useful:
You don’t need to be an expert in artificial intelligence to use Baserow AI. This article provides you with instructions and helpful tips. These make it easy to use AI features in your everyday work, even if you’ve never used AI before.
We recommend the use of GPT-4-turbo-preview, as it works best with high-parameter models.
Prompts are like instructions you give the AI. By writing clear and specific prompts, you can use AI to automate your data management tasks effectively and enhance Baserow AI. Here’s how to craft them:
Tell the AI exactly what you want it to do or what information you need. Vague prompts can lead to inaccurate results. Instead of using vague words, be clear and direct to avoid any confusion.
Rather than asking, “Explain photosynthesis,” specify, “Describe the process of photosynthesis in plants and its importance for the ecosystem.”
Or, instead of saying “How do I write a Python script?”, ask for step-by-step instructions. A better prompt would be: "What are the specific steps to write a Python script that can read data from a CSV file and then display it in a table format?”
Effective AI prompts are clear, concise, and tailored to get the desired response from the AI.
When giving instructions to AI, avoid using complicated words or sentence structures. The easier it is for the AI to understand your prompt, the better the AI will understand your request and give you the answer you’re looking for.
For example, instead of asking a broad question like “How does AI work and what are its applications?”, you could break it down into two simpler questions:
One way to use AI is to give it a specific role. Maybe you tell it to be a B2B SaaS sales manager with 10 years of experience. This gives the AI a starting point. It starts to understand the kind of information and responses you would expect from such a salesperson.
For example, you could say: “Pretend you’re a Sales Manager with 10 years of experience selling B2B software (SaaS) products. You’re on a call with a potential customer who’s worried about the [price/security/scalability] of your product. Come up with a response that addresses their specific concern and highlights the benefits your product offers.”
By giving the AI this specific role as an experienced B2B SaaS salesperson, it can act like that salesperson in this situation. A good salesperson would understand this concern and address it directly. They would also highlight the reasons why your product is a good value for the customer. It can understand what information you need and craft a response that addresses the customer’s concern and emphasizes the value of your product.
To make things easier for AI to understand what you want it to do, it’s helpful to show examples. Instead of just describing what you want, give some examples of the input data and the kind of information you’d expect as an output. This helps the AI understand the format and content you’re looking for.
This is especially important for tasks like sorting things into categories. For example, if you want the AI to sort emails into “important” and “not important,” you’d give it some examples of each type of email. For the AI to do this well, it needs to see examples of what belongs in each category.
Let’s say you need an analysis on a business topic. Instead of just saying “Analyze trends,” you could provide context: “Analyze the current market trends in the tech industry, considering the recent advancements in AI and machine learning.”
Instead of questions with yes/no answers, try questions that require more details. This will help the AI give you a more thorough and informative response.
For instance, instead of asking “Does AI improve productivity?”, consider "How can AI solutions enhance productivity across diverse business sectors?”
Examples are especially helpful when you’re trying to understand something new, like a financial term.
Instead of just asking “What is compound interest?”, you can ask for an example. This gives the AI more information to work with. For instance, you could say: “Can you explain how compound interest works with a calculation?” This way, the AI can give you a specific example that helps you understand the concept better.
If you want to understand a topic fully, it’s helpful to see it from all sides. This will give you a more well-rounded picture of the topic.
Instead of asking a yes-or-no question, like “Is remote work good?”, you can ask for both the positive and negative aspects. For example, you could ask "What are the pros and cons of remote work, considering both the employee’s and employer’s perspective?”
Ambiguity means something can be understood in multiple ways. If your prompt is too broad, the AI might not know exactly what you’re looking for. This means making sure your prompt isn’t open to different interpretations.
For example, instead of a broad prompt like “Why is social media useful,” you could narrow it down to “Discuss the role of social media in modern marketing strategies for small businesses.”
This breaks the information down into an easy-to-understand sentence.
Different formats are better suited for different tasks. You need to tell the AI what kind of answer you want. Do you want it to write a story, translate a language, or answer a question?
If you need a comparison, instead of, “What is the difference between HTTP and HTTPS?”, ask, “Create a table comparing HTTP and HTTPS, focusing on security, speed, and common use cases.”
AI models are trained on a lot of data, but they might not be perfect at handling unexpected situations. When you write your prompt, try to think of any unusual variations or odd details that the AI might encounter.
Once you’ve thought of some unusual situations, tell the AI how you want it to handle them. This will help the AI give you better results, even when things get a little weird.
Let’s say you’re prompting an AI to write a news report. A normal situation might be writing about a sunny day. An edge case could be writing about a sudden snowstorm. By mentioning the edge case, you can make sure the AI knows how to handle the unexpected weather in its report.
Once you’ve written your prompt, it’s important to try it out and see what the AI generates. Based on the output you get, you can adjust your prompt to get closer to what you have in mind. This back-and-forth process of testing and refining is key to getting the best possible outcome from your AI prompts.
Baserow’s AI is a real time-saver! With it, you can analyze the information and workflows because it works right alongside the things you already do in Baserow. You won’t need to create entirely new apps or waste time copying information between different programs.
Baserow AI is easy to use and integrates with your existing data, so you can get maximum context and insights. Get started today and see how AI can transform the way your team works!
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If you run into any issues while following this tutorial, or you want to share your use cases, reach out in the Baserow Community.