Advanced Generative AI

1.    Custom Prompt Engineering:

• Basics of prompt engineering for specific outcomes.
• Advanced techniques for refining prompts to improve responses.
• Strategies for iterative prompt testing and optimization.
• Examples of effective prompts for various business scenarios.
• Exercise: Refining prompts in CprimeAI

2.   Advanced Gen-AI Applications:

• Introduction to advanced features and capabilities of CprimeAI.
  • (Note that other tools like ChatGPT have many of the same features. However, CprimeAI supports fully private mode – important to many organizations. Also, the free version of ChatGPT is extremely out of date.)
• Case studies of advanced applications.
• Integration with other software and platforms.
  • Multiple different modes of integration.
  • Vectorization: Pull relevant records and create a cache, e.g. recent/frequent records.
  • Direct: Pull specific records e.g. using JQL for Jira.
  • Either way, the relevant records are used as context.
  • Introduce “RAG” concept.
• Customizing responses for specific organizational needs.
  • You can type a prompt such as “Remember: if you are asked x, do y.”
  • This can be injected “under the covers” as a set of system prompts.
• Discussion: List your organization’s potential applications of Gen-AI

3.   File Manipulation:

• Techniques for automating document creation, editing, and formatting.
• Strategies for comparing and merging documents.
• Best practices for managing file storage and retrieval integration.
• Security considerations when manipulating files.
• Exercise: File manipulation in CprimeAI

4.   Data Analysis:

• Fundamentals of data querying and analysis.
• Visualizing data analysis results.
  • (Currently, LLM image generation is primitive and unreliable. However, LLMs are good at transforming output into different formats, e.g., you can output as CSV, then attach that to an Excel chart. More advanced integration is possible using commercial or open-source tools, so long as the data format can be explained to the LLM.)
• Enhancing data accuracy and reliability in reports. Introduce “do this, then think about the result of this.”
• Advanced data analysis techniques with external tools.
  • (Note: This agenda item can be customized for a specific tool, as requested for private delivery. At the very least, you can get a report from an external tool, load it, and tell the LLM to look for certain things in it. But if we know more what they want to do, we can do better.)
• Exercise: Data analysis in CprimeAI

5.   Productivity Enhancement:

• Streamlining workflow processes.
• Automating routine tasks and communications.
• Customizing for team collaboration and project management.
• Time-saving tips and tricks for daily tasks.
• Discussion: Your organization’s productivity-enhancement opportunities

6.   Integration with Internal Data Sources:

• Overview of connecting to internal databases and data lakes.
• How to ensure data privacy and security during integration.
• Techniques for real-time data analysis and insight generation.
• Case studies on successful internal data source integrations.
• Discussion: Your organization’s data that would need to be integrated with Gen-AI

7.    Business Development Applications:

• Market research and competitive analysis.
• Generating leads and identifying business opportunities.
• Crafting personalized outreach and marketing strategies.
• Analyzing customer feedback and trends.
• Exercise: Research competitors/clients using CprimeAI

8.   Limitations and Best Practices:

• Understanding the computational and contextual limitations of an LLM interface.
• Ethical considerations and responsible use.
• Troubleshooting common issues and challenges.
• Staying updated with the latest Gen-AI developments and best practices.
• Discussion: Roadmap for Gen-AI at Your organization

This course presumes a basic familiarity with generative AI concepts, and experience using a Gen-AI Chat tool like ChatGPT. A technical/programming background is not required (although individuals with such a background will benefit from this course.

After completing this course, you will be able to:

• Leverage the advanced techniques and capabilities of generative AI  to solve complex problems.
• Apply prompt engineering strategies to refine AI responses for specific outcomes, enhancing the effectiveness of AI applications in your organization.
• Integrate generative AI tools with internal data sources and external platforms, facilitating seamless data analysis and insight generation.
• Navigate the ethical considerations and best practices in deploying AI solutions, ensuring responsible and beneficial use of technology.
• Automate routine tasks and streamline workflow processes, significantly improving operational efficiency and productivity.
• Craft personalized AI-driven strategies for business development, including market research, competitive analysis, and customer engagement.
• Stay up to date with the latest developments in generative AI, positioning yourself and your organization at the cutting edge of technological innovation.