In this edition of the New Normal, we begin a new series of articles about Artificial Intelligence.
To start, we will address a profound transformation that is occurring in the business world and professional development: Large Language Models (LLMs).
These models, which are part of Generative Artificial Intelligence, have become part of the New Normal. I will explain what they are, how and why this happened, how they are creating value, and what precautions we should take in their adoption and use.
Introduction to LLMs and their Relationship with Machine Learning
LLMs are an advanced form of machine learning. Unlike classical machine learning models, which are trained to perform “specific tasks with limited and structured datasets,” LLMs are trained on vast amounts of unstructured text, from gigabytes to several terabytes of text for their initial learning, but with additional training in conversational interactions to improve their responses.
They understand and generate natural language in a manner similar to humans. This places them in the field of “Generative Artificial Intelligence,” where AI not only processes information but also creates new content from learned patterns.
Classical models, such as linear regression or decision trees, are excellent for specific predictions and classifications, but LLM models have the ability to understand “broad contexts and generate complex texts,” making them ideal for applications that require natural language understanding and generation.
Why Have LLMs Become Part of the New Normal?
LLMs entered our daily lives less than two years ago and have already become essential due to their adaptability and efficiency capabilities. Companies and professionals are always seeking ways to be more productive, innovative, and competitive.
Anyone who uses ChatGPT is, in practice, interacting with OpenAI’s GPT LLM.
LLMs meet these needs by automating repetitive tasks, improving decision-making, and offering support for continuous learning. There is still enormous potential to be explored with this new technology, promising significant value extraction as the technology continues to evolve and integrate even more into our routines.
How Are LLMs Creating Value in Business Activities?
Let’s look at some practical examples:
1. Process Automation:
- Time and Resource Savings: LLMs are capable of automating tasks such as report analysis, data extraction, and document generation. This saves hours of work that can be redirected to more strategic activities.
- Practical Example: Companies in the financial sector use LLMs to analyze transactions and identify fraud, something that previously required much time and human labor.
2. Generation of Document and Report Summaries:
- Types of Summaries: LLMs can create executive summaries, analytical abstracts, critical reviews, and even descriptive summaries of extensive documents, according to specific needs.
- Practical Example: A research team can request an executive summary of a 100-page technical report, highlighting only the main conclusions and recommendations, saving time in reviewing the complete document.
3. Comparison of Document Versions:
- Change Analysis: LLMs can compare two different versions of documents, identifying alterations and summarizing the changes that will occur with the new version.
- Practical Example: Companies working with regulations can use LLMs to compare the current version of a standard with the proposed new version, highlighting the main changes and their impacts, facilitating adaptation to new rules.
4. Assistance in Developing Ebooks and Books:
- Idea Exchange and Feedback: LLMs can act as experienced assistants, helping authors develop their books, whether fiction or technical in nature, through idea exchange and continuous feedback.
- Practical Example: A fiction author can use an LLM to suggest plots, develop dialogues, and review narrative coherence. Similarly, a technical book author can use the LLM to structure chapters, verify information accuracy, and improve content clarity.
5. Use of Thematic LLMs for Heavily Regulated Areas:
- Specialized Support: Thematic LLMs, citing as an example those focused on the legal area, can assist in legal research, document and contract drafting, contract analysis, and decision support in complex cases.
- Practical Example: A law firm can use an LLM to review complex contracts, identify important clauses and suggest improvements, in addition to providing support in researching relevant jurisprudence and laws.
6. Communication Improvement:
- Writing Efficiency: Need to write emails, reports, or create presentations? LLMs can help generate high-quality content, correct errors, and suggest improvements, making communication clearer and more efficient.
- Practical Example: Marketing teams use LLMs to create advertising campaigns, ensuring consistent and impactful messages.
7. Customer Service:
- Fast and Accurate Responses: Chatbots powered by LLMs can answer frequently asked questions and resolve simple problems, improving customer experience and freeing the support team to handle more complex issues.
- Practical Example: E-commerce companies use LLMs to serve customers 24/7, offering immediate and efficient support.
8. Data Analysis and Insights:
- Informed Decisions: LLMs can analyze large volumes of data, identify patterns, and generate valuable insights that help in strategic decision-making.
- Practical Example: In telecommunications projects, LLMs analyze network data to identify improvement areas and predict failures, ensuring more reliable service.
The New Assistant for Professional Development
In the field of professional development, the concept of continuous learning is fundamental. LLMs play a crucial role in this process:
1. Personalized Learning:
- Adaptation to Learning Style: Online learning platforms are using LLMs to offer courses that adapt to each student’s pace and style, providing a more effective learning experience.
- Practical Example: Tools like Coursera and edX use LLMs to create personalized study roadmaps, helping students focus on areas where they most need improvement.
2. Support in Research and Content Creation:
- Facilitating Research: LLMs can help find relevant information, suggest reliable sources, and even write parts of academic papers or professional reports.
- Practical Example: Research professionals use LLMs to compile data and generate detailed analyses, saving time and ensuring accuracy.
3. Skill Development:
- Real Scenario Simulation: LLMs can simulate job interviews, provide feedback on presentations, and help practice new languages, improving skills in a practical and interactive manner.
- Practical Example: Applications like Duolingo use LLMs to offer real-time feedback, improving users’ fluency in new languages.
Precautions and Care in LLM Adoption and Use
While LLMs bring many benefits, it is essential to adopt some precautions to ensure their responsible and effective use:
1. Data Privacy and Security:
- Protection of Sensitive Information: Ensure that data used and generated by LLMs are protected against unauthorized access. Use encryption and other security mechanisms to protect sensitive information.
- Practical Example: Companies should implement strict security policies when using LLMs to process customer data or confidential information.
2. Data Quality:
- Training with Accurate and Unbiased Data: LLMs should be trained with high-quality and representative data to avoid biases and ensure accurate results.
- Practical Example: When developing an LLM for market analysis, use a wide range of data from diverse sources to avoid prejudices and provide balanced insights.
3. Continuous Monitoring and Evaluation:
- Performance Assessment: It is important to continuously monitor LLM performance and adjust them as necessary to maintain accuracy and relevance.
- Practical Example: Establish evaluation metrics and regularly review LLM performance in different usage scenarios.
4. Prevention of Hallucinations and Text Reliability:
- Quality Control: Develop mechanisms to detect and avoid hallucinations, which are incorrect or fictitious information generated by the model. Ensure review and validation of generated texts to guarantee reliability.
- Practical Example: Implement peer review systems and fact-checking to monitor the accuracy of information generated by LLMs, especially in critical contexts such as legal and medical areas.
5. Transparency and Ethics:
- Explainability: Users should be able to understand how LLMs make decisions. Promote transparency by explaining the processes and algorithms used.
- Practical Example: When using LLMs in recruitment decisions, explain to candidates how AI is being used and ensure that the process is fair and transparent.
The Future of Work with LLMs
Large Language Models are shaping the future of work by enhancing our capabilities and allowing us to focus on what really matters: innovation, creativity, and strategy. They do not replace human talent but complement and amplify our skills, creating a more dynamic and efficient work environment.
We are living through a silent revolution where technology and continuous learning walk together, creating a new normal where we are constantly evolving and adapting. I hope this article has shown how LLMs are creating value both in business activities and professional development and how we can adopt these tools responsibly and effectively.
In the next newsletter, we will explore the backstage of LLMs, their engine with Transformer architecture, their communication base with NLP (Natural Language Processing), and the form of interaction with Prompt Engineering.
Note: This newsletter was written with ChatGPT 4.0 as my assistant for research, development, and interaction of the subjects and examples presented. I can estimate that about 40% of the creative and development work of this newsletter was developed by the GPT LLM of ChatGPT, and the remaining 60% from my “personal model,” developed with my learning and experience throughout my professional life.
Text from The New Normal Newsletter: https://www.linkedin.com/newsletters/o-novo-normal-7200542437621059585/
