Key takeaways:
- It's possible to build reliable systems using unreliable AI agents by adopting specific strategies and tools.
- Effective prompt engineering, continuous evaluation, and the use of complementary agents are crucial for improving AI reliability.
- Leveraging observability and retrieval-augmented generation (RAG) can significantly enhance system performance over time.
# Introduction
- AI often shows great promise in demos but may lack reliability in real-world applications.
- There is a potential to build reliable systems from unreliable AI agents through a structured process.
# Developing Reliable AI Systems
- Write Simple Prompts: Start with basic prompts to solve your problem and refine them through experience.
- Use Evaluation Systems: Implement an evaluation system for continuous prompt engineering to improve AI performance.
- Deploy with Observability: Ensure the system is observable to facilitate ongoing improvements based on real user feedback.
- Invest in Retrieval-Augmented Generation: Use RAG to dynamically enhance prompts with relevant information.
- Fine-Tune Models: Continuously collect data to fine-tune the AI model for better accuracy and reliability.
# Using Complementary Agents
- Employ complementary AI agents that can work together to achieve more reliable results than a single agent could.
- Example: A "planner" agent devises a high-level strategy, while a "verifier" agent checks the details and corrects errors.
# Practical Advice and Insights
"While AI agents are not reliable, it is possible to build reliable systems out of them. This has been proven through a rigorous process of prompt engineering, evaluation, and observability."
- Integration and Testing: Integrate AI components minimally at first to build infrastructure and apply practical tweaks for reliability (e.g., using low temperature settings in model calls).
- Prompt Engineering Tips: Include essential context and use iterative improvements based on measurable success criteria.
- Observability and User Feedback: Log all interactions to learn from real-world usage and improve the system iteratively based on user
source: Building reliable systems out of unreliable agents