The author suggests that most companies start with Pattern 2 (in-context learning) due to its balance of effectiveness and feasibility. However, as the field of GenAI matures, the author expects Pattern 3 (fine-tuning models) to gain prominence. The author advises companies to choose a solution pattern that aligns with their readiness, capabilities, and goals, and to start experimenting, learning, and continuously improving.
Key takeaways:
- Rajul Rana, the CTO at Orion Innovation, discusses the rise of generative AI (GenAI) and its potential to revolutionize various sectors.
- GenAI is based on foundational models that allow it to understand context and relevance within the content it processes, making it incredibly powerful and versatile.
- Rana outlines four solution patterns for implementing GenAI, ranging from using out-of-the-box API-based solutions to building your own foundation model.
- When choosing the appropriate pattern for your use case, it's important to consider the balance between cost, complexity, and accuracy, and to start experimenting, learning, and continuously improving.