Notes from AI That Works Camp

Overview

AI Tinkerers SF hosted an incredible “AI That Works Camp” event, bringing together practitioners to share real-world experiences with AI systems in production. Here are my key takeaways from the sessions.

Key Sessions

Prompt Engineering Best Practices

The session on prompt engineering revealed several production-ready techniques:

• Chain of Thought prompting significantly improves reasoning capabilities
• Few-shot examples should be carefully curated for your specific domain
• Temperature settings need fine-tuning based on use case (0.2 for factual, 0.7 for creative)

Reasoning Models in Production

Discussion around integrating reasoning models into production systems:

• Latency considerations when chaining multiple model calls
• Cost optimization strategies for token usage
• Error handling and fallback mechanisms

Building Reliable AI Systems

Key insights on production AI reliability:

1. Monitoring and observability are crucial for AI systems
2. Human-in-the-loop workflows for critical decisions
3. Gradual rollouts to catch edge cases early

Actionable Takeaways

• Start with simple prompts and iterate based on real data
• Invest in proper evaluation frameworks early
• Build robust fallback mechanisms for model failures
• Monitor token usage and optimize for cost

Conclusion

The AI That Works Camp reinforced the importance of practical, production-focused approaches to AI implementation. The community’s willingness to share both successes and failures made this an invaluable learning experience.

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Want to discuss these insights? Connect with me or find me on LinkedIn.