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From Clicks to Conversations: Designing LLM-powered Apps by Marie-Alice Blete
Designing LLM-powered apps requires careful consideration of goals, abilities, and output format, as well as careful planning for autonomy, flexibility, and scalability.
- Mix LLM with LNG to create a new model, combining the strengths of both.
- Designing an LLM-powered app requires careful consideration of goals, abilities, and output format.
- Use agents instead of prop solutions or state machines for increased autonomy and flexibility.
- Guardrails are essential to prevent non-deterministic behavior and ensure consistent results.
- Control loops and retry mechanisms are necessary for handling potential errors and failures.
- Tokens matter, as they determine the LLM’s predictions and output.
- Monitoring and tracking prompts is crucial for debugging and improving the system.
- Consider using a cache to reduce the number of API calls and improve performance.
- Integration with other technologies, like hyperlink and mouse, can enhance user experience.
- LLMs are not deterministic and can behave unexpectedly, requiring robust testing and validation.
- Using a state machine can help mix code with rules and provide more control over the system.
- Demos and experimentation are essential for testing and refining the system.
- Be cautious when using LLMs, as they can be non-deterministic and unpredictable.
- Developing an LLM-powered app requires a deep understanding of both LLMs and the specific application domain.
- Consider the costs and scalability of the system, as LLMs can be expensive and resource-intensive.
- Use metrics and tracking to monitor the system’s performance and identify areas for improvement.
- Be prepared to adapt to changes in the LLM landscape and keep the system up-to-date with the latest models and techniques.