Having nurtured a passion for technology from a young age, I've developed a keen understanding of its potential to streamline processes and enhance productivity. It's easy to assume that the utility of available market tools is universally recognized; however, I am aware that the true value of these resources may not be immediately apparent to everyone. Many of these accessible solutions can significantly benefit your business. Allow me to provide a concise overview of several key strategies you can implement immediately. Remarkably, you can leverage many of these solutions without the need for extensive technical knowledge or the employment of engineers.
So, let's dive in.
Applications and services
Let's start with the dedicated tools that you, as a business owner, can begin using even today. There are two types of integrations:
Direct
There are already a few tools that I can recommend for you to start using. Some of them are obvious, and some are not.
You can start with popular AI tools like:
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ChatGPT: It can assist you in various ways, including automating customer service through intelligent and responsive communication, providing content creation for marketing materials, aiding in data analysis by summarizing large volumes of text, and enhancing productivity by answering queries and generating reports.
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Midjourney: This tool focuses on generating high-quality visual content. It could be a game-changer in areas like marketing, branding, and product development. It enables the rapid creation of visual assets, helping you enhance your online presence, attract more customers, and effectively communicate your brand message.
Or you can dig deeper and try using some less popular tools like:
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HeyGen: Helps you create avatars and multilingual voices for videos. It's a cost-effective and time-saving solution, eliminating the need for traditional, resource-intensive video production methods. Its user-friendly nature makes high-quality video production accessible, even for those without technical expertise. Adopting this AI-driven approach can significantly enhance your marketing efforts. Check out Dominika's article about HeyGen.
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MacWhisper: This tool can quickly and easily transcribe your audio files into text using OpenAI's state-of-the-art transcription technology, Whisper. Whether you're recording a meeting, lecture, or any other important audio, it accurately transcribes your files into text.
Indirect
It's highly possible that you are already using some form of AI in your daily life, such as the autocomplete feature in your Gmail account, or you are using tools like Notion, which has already introduced support for text generation in its tool. You can explore how it can improve your daily work.
Prompt engineering
It's highly likely that you've already engaged in prompt engineering, perhaps by experimenting with the capabilities of tools like ChatGPT, Dall-e, or Midjourney. But what exactly is prompt engineering?
Prompt engineering is the process of carefully designing and formulating prompts (inputs) to communicate with AI models effectively. The goal is to elicit the most accurate, relevant, and coherent responses from the AI. This involves understanding the AI's capabilities and limitations and crafting prompts that are clear, specific, and structured in a way that guides the AI towards providing the desired output. It's a crucial skill for effective AI interactions.
So, what can you do, and where can you seek advice? I would suggest the following:
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Start simple and observe the results.
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Don't be afraid; try new things and observe how the model reacts to your prompts.
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Seek inspiration by observing what others have done.
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Read official and community-based documentation, such as: OpenAI's guide on prompt engineering or a great example of multiple resources here. An amazing guide can be found at LearnPrompting.org.
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You can also exchange prompts with your team and build a unique internal knowledge base for your organization.
No-code integrations and automation
There are several tools and services that you can start using with a little bit of knowledge, either by learning new things or by asking someone who is 'more technical' on your team. What can you do using these tools? You can automate your processes, leveraging, for example, ChatGPT to help you as one piece of your workflow puzzle. Imagine that every time you receive a new email, ChatGPT automatically checks the business context of this email. If it's, for example, some kind of critical situation (e.g., a system is down), you receive a direct notification via SMS or even a direct call where the bot reads you this email.
This is the easiest possible way to use different pieces of services, connect them with current AI capabilities, and optimize your internal processes on a daily basis for you or the business you are running.
You can check out platforms like Make.com, IFTTT.com, or Zapier.com if you are interested, or contact us so we can help you as well.
Foundation models
This is the point where you will probably need an external or internal technical provider to help you utilize the currently available technology. But for now, I just want to give you a high-level idea of what you can achieve using Large Language Models (LLMs) for commercial or private use.
What is a foundation model?
A foundation model is a type of artificial intelligence model that is trained on a large, diverse dataset from a wide range of sources. These models are called "foundation" because they provide a broad, general understanding of the world, language, images, or other data types, which can then be fine-tuned or adapted for a wide variety of specific tasks without needing to be trained from scratch for each new task.
So, you can use commercial models like the one provided by OpenAI or utilize open-source models like Mistral or Lama. However, using them relies on large, universal datasets of data that were used to train the model.
How can you use Large Language Models (LLMs) with your own data? There are two options that we have already built for our internal needs, both of which are based on our internal knowledge.
Finetuning
It's a process where a pre-trained model is further trained on a smaller, specific dataset to adapt it for a particular task. This process leverages the broad knowledge the model has acquired during its initial extensive training on a large and diverse dataset, allowing it to quickly learn the nuances of the new task with relatively few additional examples.
This approach is efficient and effective, requiring less data and computational resources than training a model from scratch for each new task.
You can think of it as creating a general model tailored to your specific needs or specialized tasks within various industries. It still requires data for training and resources with computing power to fine-tune the model, but the process takes only minutes or hours rather than weeks or months, as is the case with foundation models.
How can you use it in your business?
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Chatbot for Customer Service - Fine-tuning a foundation model on a dataset specific to a company's products or services can create highly effective chatbots for customer service.
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Personalized Education - Educational content can be used to fine-tune models to create personalized tutoring systems that cater to the unique learning styles and needs of individual students.
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Sentiment Analysis - Companies can fine-tune models on customer reviews, social media posts, and other feedback to perform sentiment analysis tailored to their products or services.
But you may ask, what if my data is constantly changing or if I need to analyze large documents on a daily basis? The idea of fine-tuning the model for each document might seem impractical. This is where we can introduce another idea:
RAG architecture
It is an innovative approach that combines the best of two worlds in natural language processing: retrieval-based and generative models. It was introduced to enhance the ability of generative models to produce more accurate, relevant, and informed content by leveraging external knowledge sources.
How can it help you?
One of the examples we've already built for our internal needs is a Knowledge-Enhanced Chatbot - imagine a bot on your Slack/Teams/whatever which can provide detailed, accurate, and contextually relevant responses to user queries by fetching information from a vast database of domain-specific knowledge. This capability makes it particularly useful in areas where expertise and up-to-date information are crucial. The domain doesn't really matter so it could be useful in:
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Education: Assisting in learning and research by sourcing detailed explanations, examples, and references from educational materials.
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Finance: Providing personalized investment advice by accessing real-time market data, financial news, and analysis reports.
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HR: Can automate the initial screening process of resumes and applications by retrieving information from job descriptions, required qualifications, and ideal candidate profiles. It can then generate a summary or score for each applicant, highlighting how well their background, skills, and experiences match the job criteria.
But can I create my own LLM?
Yes, although it's unlikely that you would. You need time and money to collect data, training time can range from weeks to months. For example, GPT-3, with its 175 billion parameters, was trained for weeks on thousands of GPUs. OpenAI’s GPT-3 is rumoured to have cost around $4.6 million in direct computing costs. Building a foundation LLM is a resource-intensive endeavour that demands careful planning and significant investment.
At the same time, currently available tools for end users and engineering teams are more than sufficient to utilise Large Language Models (LLMs) to accelerate business processes, optimize our time, and automate tasks.