Abstract
- Understanding technology. AI relies on more than recognizing the functionality of technology.
- Marketing potential. Prompts open the door for marketers to leverage a variety of insights and skills.
- Diversity of skills. Different skills can help you get better answers from your LLM.
In the digital technology poker game, many experts consider syntax to be the trump card in the deck. However, like a skilled card player, a combination of skills forms a winning hand. Let's take a look at the skills needed to successfully leverage AI in marketing.
The rise of AI has focused attention on blending skills to make the most of the models currently available.
So what skills do marketers really need when leveraging AI in marketing?
In today's dynamic and volatile market, the marketing AI skills needed are shifting from focusing on complex programming details to understanding the program activity behind the prompts. . Prompts are more than just queries. Users can formulate questions based on their own knowledge. This expertise opens the door to a variety of skills that marketers can leverage to improve responsiveness and productivity of AI workflows.
Here we take a look at how these skills can potentially be demonstrated in workflows involving ChatGPT, Gemini, Claude, or other generative AI solutions.
1. Domain skills
Large-scale language models (LLMs) can generate vast amounts of content, but it's important to evaluate its quality and accuracy. Marketers need to hone their critical thinking skills regarding the specific domain topics they utilize to effectively evaluate LLMs.
For example, if you want to create a ChatGPT prompt about car buyers, you need knowledge of the automotive industry. This includes applying content evaluation criteria to evaluate the output produced by the LLM and ensure it is consistent with brand standards.
2. Data curation
Data curation is often based on domain knowledge relevant to the application. As models evolve and become more multimodal, data can appear in a variety of formats, from metadata descriptions to diverse media types. Therefore, marketers need to understand the potential of AI for information curation in order to identify the optimal workflows for using AI models.
Because LLM is data-driven, marketers must have the skills to curate and prepare high-quality queries to optimize LLM for specific marketing tasks.
Consider writing data that is sourced from an SQL database. A number of tools allow you to plan your data schema and understand potential table relationships with minimal syntax. You can use the charting framework Mermaid to map tables that are related to each other. Similarly, DrawSQL solutions allow you to map tables that are related to each other and create SQL schemas.
The use of AI in marketing gives marketers the opportunity to create prompts that can generate schema outlines, using preview tools like DrawSQL for additional guidance. These resources require marketers to have a good understanding of what data is frequently accessed, what queries are typically possible, and to describe that data and structure in a way that AI tools can understand. It suggests something.
Related Article: Top 5 ChatGPT Prompts for Customer Experience Professionals
3. Understand the basics of LLM
Marketers need to understand how LLM works at a slightly better level than the average technology user. This means understanding what information was used in the model's dataset and what information is included in the search augmentation generation (RAG), which is the augmentation vector used in the query. . This will allow you to create useful prompts more quickly on the first iteration.
Marketers also need to understand the limitations of the models, including the characteristics of different types of LLMs, how LLMs are trained, and the potential biases that LLMs may contain.
Related article: Rapid engineering basics for marketers, advertisers, and content creators
4. Rapid engineering
Creating effective prompts is important to getting the desired output from LLM. Additionally, the skills needed to create prompts are changing rapidly. Researchers are discovering new insights into performance, including variations in thought prompt chaining and automation techniques. Marketers must develop nimble engineering skills to clearly communicate goals and desired content.
Mastering prompt engineering techniques allows marketers to clearly communicate expected outcomes and guide LLMs toward desired responses. My post on Prompt Engineering explains some of the basics of prompting that marketers should practice.
5. Visualization skills
One of the benefits of AI in marketing is that it allows users to create visualizations without relying heavily on syntax or technical language. This allows users to edit content faster.
For example, in my article on ChatGPT ADA, I showed how to fix bar charts when errors are discovered in the data. Instead of going back to Excel and reloading the data, the bar chart was completely recreated by telling the AI prompt to ignore the error.
Visualization skills extend beyond data. Nowadays, many models are equipped with image creation functions, so you can also format the images. For example, Midjourney prompts can include photogenic details such as lens type or camera model to complete the artistic image.
In the end, choosing the right visualization for your data or creating images accurately can help you achieve great visual results.
6. Data analysis
In data management, curation and analysis are separate workflow steps. While curation is essentially cleaning the data, which involves editing the format, analysis focuses on uncovering the meaning of the data after it has been processed.
Analytics has several benefits for understanding AI models, beyond just interpreting the model's output. It also sheds light on LLM performance and helps identify areas for improvement, from improving prompts to enhancing the RAGs that support the model.
Analysis involves breaking down complex information. Marketers must analyze the output of the LLM to scrutinize the data produced and evaluate the model's performance to ensure that the responses are a good match to the posed query.
7. Human-involved insights into AI workflows
Although LLM is a powerful tool, it cannot replace human creativity when coordinating the deployment of AI within workflows. Marketers often establish this workflow, so the skill to effectively blend LLM capabilities with human expertise is critical to achieving optimal results. Stakeholder involvement is essential in areas such as mortgages, scholarships, and employment, where approval decisions affect people's access.
8. Ethical considerations affecting data privacy and security
With the rise of AI and LLM, ethical considerations such as bias and misuse are becoming increasingly important. These concerns are reflected in the decisions models have to make regarding data privacy and security. In a previous post, I discussed his five key questions for marketers to consider.
Marketers must be aware of these ethical considerations and use LLM responsibly. By doing so, you can develop better strategies to mitigate potential issues with AI in marketing.
9. Storytelling with information
Despite the help of an LLM, creating a compelling marketing narrative is still important. Marketers need to hone their storytelling skills to clearly communicate how model outputs support campaign planning. When using the LLM for content creation, collaborating with stakeholders to generate the right ideas and media is often essential. A thorough understanding of LLM capabilities, limitations, and insights is essential for marketers and content creators to work together effectively when shaping the story around a product, service, or event.
10. Stay informed
This last skill includes nine other skills. The field of AI is constantly changing, so marketers need to stay on top of the latest advances in his LLM technology to make the most of its potential.
Final thoughts on the skills needed for AI in marketing
As the digital marketing landscape continues to evolve, the integration of AI and LLM becomes increasingly important. The skills outlined in this article form a comprehensive toolkit for marketers navigating this space. By mastering these competencies, from domain knowledge to storytelling, marketers can make the most of their AI in marketing and ensure their strategies remain relevant and effective in an ever-changing world of digital technology. can be maintained.
