Opportunities for LLM's Today in Ag: 4 -Leaf Clover Strategy

In today’s post, I introduce my 4-leaf clover strategy, highlighting four key areas that LLM's can be leveraged TODAY to enhance operations, personal efficiency and use of data.

🍀 1: Unlocking Insights from Unstructured Data

The first part of our strategy revolves around the analysis of unstructured data. Customer interactions, such as calls and emails, contain a wealth of valuable information waiting to be discovered. By analyzing past conversations, trends can be identified, and patterns can be uncovered. Sentiment analysis can reveal the steps leading to a purchase decision, and common complaints can be addressed with targeted solutions.

Furthermore, unstructured customer feedback, such as NPS scores and Twitter comments, can provide insights into areas where your organization excels or requires improvement.

Additionally, analyzing customer details, such as soil type, precipitation, and time of day/year, can help identify underlying connections influencing purchase decisions.

🍀 2: Automating Repetitive Tasks

The second part of my strategy involves automating repetitive tasks within your organization. LLMs, in conjunction with webhook tools like Zapier and Make, can automate almost any tasks. The question becomes which ones are reoccurring and solve the largest pain-points?

I’ve provided an example in my last newsletter such as the weather event text message campaigns. Here I would like to provide a few more I’ve personally made in the past such as summarizing sales calls, and drafting social media messages.

For instance, consider a sales team that spends a significant amount of time summarizing sales calls and distributing information across the organization. Recording with consent sales conversations via Otter.ai, then summarizing the conversation based on a template you would like it to follow (see below). Finishing off by notifying the required individuals in your organization about the newly created documents via Microsoft Teams/Slack/.

During the sales conversation, summarize the key points discussed, highlighting the potential client's needs and expectations. If information about the company's size, history, or other specific details was not provided or is unknown, indicate with 'N/A'. Based on the available information, detail how their customer support operates, identifying any explicit or inferred mechanisms they currently employ. Identify and elaborate on the pain points the company is facing, drawing from the conversation's context or directly stated issues. Discuss the company's future aspirations as mentioned or implied during the discussion, providing insight into their goals and ambitions. Finally, outline a sales strategy tailored to their unique situation, explaining why this approach would be effective in addressing their needs and helping achieve their future objectives.

This will allow the sales professional to focus on more strategic activities, such as building relationships and closing deals, instead of summarization tasks.

🍀 3: Enhancing Access to Internal Resources

The third part of my strategy focuses on how companies interact with their vast repositories of internal knowledge. By leveraging LLMs, organizations can create custom GPTs for various internal resources, such as HR booklets, trial data, and product documentation. This approach not only enhances the user experience but also encourages the utilization of institutional knowledge. The result is a highly intuitive and user-friendly interface that allows employees to query complex datasets and documents with simple, natural language prompts.

The implementation of custom GPT models for internal resources does more than just simplify access; it fundamentally changes the way organizational knowledge is leveraged and stored. Employees can quickly find answers to intricate questions, retrieve specific data points, or understand policies without having to navigate through multiple documents or databases manually. This not only saves time but also ensures that decisions are made based on the most accurate and comprehensive information available.

Moreover, enhancing the accessibility of internal resources encourages a more widespread use of institutional knowledge. When information is easier to find and understand, employees are more likely to consult and utilize these resources, fostering a culture of informed decision-making and continuous learning. This approach also democratizes access to information, ensuring that all team members, regardless of the tenure with the company, can benefit from the collective knowledge of the organization.

In essence, the third leaf of my strategy is about unlocking the full potential of internal documents, transforming them from static files into dynamic tools that actively contribute to the organization's success.

🍀 4: Boosting Personal Efficiency

The final part of my strategy involves increasing personal efficiency for everyone in the organization. By training individuals on prompt engineering, they can harness the power of LLMs for various tasks, such as coding, writing, reading, listening, researching, and illustrating. This investment in personal efficiency can help individuals reach 80% of the final results with 20% of the effort faster, ultimately benefiting the entire organization.

Training in prompt engineering is at the heart of this initiative. Prompt engineering involves crafting queries that effectively communicate with LLMs. The mastery of this skill can transform how individuals approach their work, allowing them to delegate routine or complex tasks to LLMs, thus freeing up valuable time to focus on higher-level strategic thinking and creativity.

For example, let's talk about a content creation team. Traditionally, the process of producing content, from initial research to final edits, is time-intensive and often fraught with inefficiencies. However, by leveraging prompt engineering, the team can guide LLMs to conduct preliminary research, generate content ideas based on trending topics or keywords, and even create rough drafts. This preliminary work, done in a fraction of the time it would take a human, allows the team to focus on refining and enhancing the content, ensuring it meets the highest standards of quality and relevance. Furthermore, LLMs can assist in editing by suggesting improvements in grammar, style, and coherence, thereby streamlining the revision process.

But the benefits of boosting personal efficiency through LLMs extends beyond content creation. Coders can use LLMs to debug or write code more efficiently; researchers can sift through massive datasets to find relevant information; and illustrators can generate preliminary designs or concepts to kickstart their creative process. This holistic improvement in productivity across various tasks means that individuals can achieve the "80/20" mark more rapidly.

Investing in the development of prompt engineering skills across the organization represents a forward-thinking approach to work. It acknowledges the evolving landscape artificial intelligence in the agricultural sector and the increasing role of technology in driving success. I am planning on offering training in that direction for individuals specifically in agriculture.

Conclusion:

The four-part strategy of LLMs in agriculture offers a wealth of opportunities for organizations. By unlocking insights from unstructured data, automating repetitive tasks, improving access to internal documents, and increasing personal efficiency, agriculture can unlock the full potential of LLMs already TODAY.

What other opportunities do you see for LLMs in agriculture? How do you think LLMs can further revolutionize the agricultural industry? Would love to hear your thoughts.

The overdrive of ChatGPT: GROQ

This week, I had the opportunity to extensively test the new player in town - Groq. An open-source interface, completely free, where you can choose if you want it to be powered by Mixtral or Llama. In an era of already fast technology, I can confidently say that it does not even compare to ChatGPT!

During my testing, I found that prompting Groq was quite successful, and I especially appreciated its standard language, without giving it specific guidelines to its tone and voice. I am so done with the verbose language of ChatGPT! Words like "unleashing," "offers a beacon of innovation," and "aimed at harnessing" are just not my style.

Although the user interface of Groq is not as sleek as ChatGPT's, I was still able to achieve quite successful results. However, context prompting it to change something earlier in the thread was much harder. Nonetheless, I am definitely interested in seeing how its API could be leveraged, as it is so much faster.

In conclusion, while Groq may not have the same polished user interface as ChatGPT, it is an open-source and free alternative that is worth considering. Its speed and ability to understand prompts without specific guidelines on tone and voice make it a promising tool for those looking for a faster and more straightforward alternative to ChatGPT.