Use cases of using AI models effectively

To use artificial intelligence effectively, you need to implement best practices to improve the quality of your work and productivity.

‍A Large Language Model (LLM) is a computer program that is trained on huge amounts of text data, which allows it to understand and generate coherent text, answer questions, translate languages, and perform other tasks. They have simplified business operations, automated daily tasks, and provided a deeper understanding of information.

When it comes to artificial intelligence, many of us think: "And it will do everything for me, I may not work anymore," but this is far from the case. And it's not an assistant, colleague, or others - it's a tool that can boost your productivity tenfold.

‘The most complicated tool won't help the person who doesn't know how to use it.’ - Kevin Sands, Canadian writer

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1. Role and context

Role and context narrow down the scope of information that AI will work with, helping it to focus on relevant data and make more accurate recommendations. This is especially important when solving complex problems where expert opinion or highly specialised knowledge is required.

At the beginning of the dialogue, assign a role to the model based on who can best perform your task.

For example:

• If you need a strategic plan for a business, specify not only the role ‘You're a business analyst with 10 years of experience’ but also the business specifics ‘This is a fintech startup operating in Europe’.
• When generating content, add more details, ‘You're a copywriter, an expert in UX writing, and work with a SaaS service that helps entrepreneurs automate sales.’

‍What does this provide?

• Useful answers - AI takes context into account and doesn't make generalised recommendations.
• Natural communication style - the model adapts to your task, whether it's a formal document or creative text.
• Reduced revision time - the better the role and context, the fewer revisions required.

Give the role of a language model right now and solve your problem with Sigma.

Try it now with Sigma AI Browser — download for free

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2. More examples

A language model adapts to your needs best when it has references - examples it can rely on. The more examples you provide, the more accurate the result will be.

How does it work?

When generating a post for social media, proceed as follows:

• Mistake: ‘Write a post about the benefits of remote work’
• Better approach: ‘You're an SMM specialist. Write an engaging post about the perks of remote work in the style of these examples.’ and attach examples (3-6 pieces) 

How does this help AI?

• Analyses style - for example, posts can be concise or detailed.
• Takes structure into account - uses lists, paragraphs, emoji, an intriguing headline.
• Understands tone - serious, expert, friendly or humorous.

Other application examples:

• Generating commercial texts - provide examples of sales headlines.
• Script writing - upload a storyboard or text sample.
• Creating email newsletters - give examples of emails with high conversion rates.

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3. Step-by-step work

Large Language Models (LLMs) tend to give a ready-made, final result at once, but if the task is complex, fuzzy or requires analysis, it is better to break it down into stages. If you ask the AI to do everything at once, it may miss important details, overload the answer with unnecessary information, or make mistakes in logic, especially if the process requires analytics. 

How to formulate requests correctly?

• Mistake: ‘Write a detailed marketing plan for a new IT product.’
• Better approach: ‘Let's work in stages. First, analyse the market and competitors. Give 5 key findings.’

Examples of step-by-step requests:

1. ‘Gather information on the topic, structure it into 5-7 key points.’
2. ‘Based on the collected data, propose a strategy, including audience and promotion channels.’
3. ‘Now write a step-by-step marketing plan with concrete actions.’

Where is this particularly useful?

• Research and analytics - gather information first, then draw conclusions.
• Writing and editing - draft, refine, final version.
• Programming - break down the task first, then write code and test.

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4. AI self-learning

If you're not sure how to set the right task, don't know all the nuances, or want to act in the most efficient way, ask the model itself how best to approach the solution! Language models are trained on huge amounts of data, including methods for solving complex problems. 

How to align the work?

1. Start with a general query, ‘I need to develop a marketing strategy. How do I properly tackle this task?’ and the Model will suggest a step-by-step process.
2. Ask for details of each step ‘Now spell out step 1 in detail: how do I analyse the target audience?’ and the AI will provide detailed recommendations.
3. Ask for a plan and rules ‘Create a structured plan for this strategy with specific steps and timelines.’ AI will produce a clear and logical document that you can tweak and use in your work.
4. Iterate through each step

Where is this particularly useful?

• Business - developing strategies, business plans, presentations.
• Programming - creating algorithms step-by-step.
• Copywriting - writing texts according to a given logic.
• Personal development - planning training, career development, etc.

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5. Double-check information: AI is not always right

Language models and can produce plausible but inaccurate or outdated data. This is especially true in science, medicine, finance and legal fields.

How to cross-check information?

1. Request references and sources ‘Cite the evidence and research on which your answer is based.’

2. Use multiple independent sources, compare your answer with data from official websites, scientific publications, or major news outlets.

3. Clarify the wording ‘Are you sure about this information? What are some alternative opinions?’.

5. Trust proven experts, AI can help structure information, but key decisions should be based on proven data and expert opinion.

Using these practices will allow you to work with AI faster, more accurately, and more productively. The key is not to expect it to give you the perfect answer on the first try, but to customize the result for you, step by step.

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Knowledge requires practice! The recommendations obtained can be applied to several language models in the Sigma AI Browser.

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To learn more about the rules of working with AI, check out the following resources:

🔗Google Cloud: Best Practices with Large Language Models (LLMs) – Learn how to craft effective prompts and optimize interactions with LLMs. ​

🔗Cohere: Best Practices for Deploying Language Models – Guidelines on responsible and effective deployment of language models in various applications. 

🔗The New Stack: Best Practices for Working with Large Language Models – Insights into guiding principles for effectively utilizing LLMs in your projects. ​

🔗University of Illinois: Best Practices in Using Generative AI in Research – Explore ethical considerations and methodologies for integrating generative AI into research practices.