Building Generative AI Applications

January 17, 2025 6:15 PM PST

---

This meeting focused on the development of Generative AI applications, discussing various techniques and emerging trends in the field. Key topics included fine-tuning models, prompting strategies, and the integration of advanced AI systems into business processes.

Presenter: Coach Denny

Key Topics Discussed

1. Fine-Tuning

• Definition: Fine-tuning is the process of further training a pre-trained model on a specific dataset or task to enhance its performance in that context.
• Challenges:
  • Difficulty in fine-tuning when a new version of the baseline model is released.
  • Potential degradation of the baseline model's capabilities post fine-tuning, making it hard to detect negative impacts.

2. Prompting

• Definition: Prompting involves providing specific input or instructions to guide a pre-trained model, such as ChatGPT, to generate desired outputs.
• Focus: Unlike fine-tuning, prompting emphasizes crafting effective inputs to elicit high-quality responses.

3. Retrieval-Augmented Generation (RAG)

• Definition: RAG is a hybrid approach that combines information retrieval with generative models to produce accurate and contextually informed responses.
• Benefit: Enhances the generative model's ability to answer questions or perform tasks by grounding outputs in real-time, external knowledge.

4. Emerging Areas in Generative AI

• Distributed Training
• Multi-Model Orchestration
• No-Code/Low-Code AI Platforms: These platforms allow users to provide data, which the system uses to generate models.
• AutoML
• Model Interpretability and Tracing
• Industry-Specific AI Systems
• Customer Support as a Platform
• Automated Workflow for Business: Professionals can automate work through automation frameworks.

5. Software Development Engineering (SDE) Considerations

• Complex Systems: SDE involves highly complex systems requiring integration.
• Improvements Needed:
  • System design
  • Code review
  • Business understanding
  • Collaboration with AI

6. Skills Evolution

• Deprecated Skills: Basic coding and writing skills are becoming less relevant.
• New Skills:
  • Familiarity with frameworks like LangChain
  • Coordination and memory management in AI applications.

7. Memory Types in AI

• Common Memory Types:
  • Conversational Buffer
  • Vector Databases

8. Types of Chains in AI

• Common Types:
  • Sequential Chains
  • Multi-Prompt Chains
  • Router Chains

9. Effective Prompting Strategies

• Question Techniques:
  • Ask both positive and negative questions to explore alternatives and disadvantages.

Conclusion

The meeting provided valuable insights into the current landscape of Generative AI applications, emphasizing the importance of fine-tuning, effective prompting, and the integration of AI into business workflows. Participants were encouraged to adapt to the evolving skill requirements and leverage emerging technologies for enhanced productivity.