LLM and Generative AI: Practical Tools for Business Productivity
ITALIAN LANGUAGE
DURATION: 3 HOURS AND 30 MINUTES
Goals
- To provide a clear understanding of LLMs and how they work.
- Explore how LLMs can be used in various business departments.
- Provide guidelines for the safe and effective use of LLMs.
- Evaluate the improvement in productivity through the adoption of LLM in the company.
Full Program:
Module 1: Introduction to Language Models and Deep Learning
- Definition of Artificial Intelligence and Deep Learning: What is AI and how deep learning models support LLM development.
- What are Foundational Models?: Defining foundational models and their impact on artificial intelligence.
- Overview of ChatGPT and LLMs: Large language models explained, with practical examples.
Module 2: History of LLM and Evolution
- Main Stages: In-depth look at the technological developments that led to modern LLMs.
- Evolution of LLMs: From the birth of language models to the current generation of LLMs (BERT, GPT, etc.).
Module 3: Basic Operation of LLMs
- Architecture of LLMs: How LLMs work, training and inference mechanisms (Transformer model).
- How LLMs are trained: Introduction to training datasets and the use of massive amounts of text data.
Module 4: Benefits of LLMs for Business Productivity
- Improved Productivity: Reduce time spent on repetitive tasks and automate processes.
- Decision Making Process Optimization: How LLMs support rapid, informed, data-driven decisions.
- Automated Content Creation: Generation of texts for marketing, reporting, emails and communications.
Module 5: Limitations of LLMs
- Bias in Models: Risks associated with bias in training data and how it affects results.
- Gaps in Contextual Understanding: Limitations in deep and creative understanding of some complex situations.
- Implementation and Scalability Costs: Technical and cost challenges for large-scale adoption.
- Hallucinations: Risks associated with generating false or misleading claims
Module 6: Security and Sensitive Data in LLMs
- What Information to Upload: Guidelines on what data can be entered into LLMs without risk.
- Protection of Sensitive Data: Measures to prevent the loading of critical information and company policies.
Module 7: Main LLMs Available
- Model Overview: Analysis of ChatGPT, Gemini, LLAMA, Claude, Copilot and other leading LLMs.
- Advantages and disadvantages: Compare models in terms of cost, ease of use, security and business applications.
Module 8: Introduction to Prompt Engineering
- What are Prompts?: How to interact with an LLM and the importance of making specific requests.
- Prompt Engineering: Techniques for optimizing prompts to get accurate and useful responses.
Module 9: Using LLMs in Different Departments
- Marketing: Content generation, sentiment analysis,
- Sales: Preparation of presentations, virtual assistants, data analysis
- Customer Care: Chatbot to answer questions, ticket management.
- Human resources: Automated recruitment, onboarding, request management.
- Finanza: Report automation, predictive analytics,
- Other Departments: Practical case examples in administration, IT, R&D and more
.Module 10: Creating Custom LLMs (Custom GPT)
- Definition of Custom GPT: Creating customized templates for specific business needs.
- Development Process: Tools and platforms for customization of
- Practical Implementation Examples: Examples of how to create custom templates for specific departments.
Module 11: Main GenAI-based tools for multimedia use
- Tools Overview: Analysis by NapkinAI, Gamma, ElevenLabs, Runway, HeyGen and other GenAI tools.
- Advantages and disadvantages: Compare models in terms of cost, ease of use, security and business applications.
Course Conclusion
- Summary of Key Concepts: Recap on the benefits and challenges of using
- Next Steps: How to get started implementing an LLM in your company, available resources and implementation plans.
CONTENTS AND TEACHING BY Grid Plus
COURSE CODE: I0011-25 LLM Grid Catalog
