Navigating the AI Hype: Practical Observability and Realistic Expectations

The world is buzzing about AI, but like the blockchain craze of years past, it's essential to separate genuine solutions from overhyped applications. Shane from Swept.ai (a company specializing in AI observability) joined Brad to discuss how developers can realistically approach AI implementation, focusing on practical solutions and avoiding the trap of chasing the latest shiny object.

Key Takeaways:

• AI Skepticism is Healthy: Don't blindly adopt AI solutions. Understand the business problem first and then explore if AI is the right tool. Sometimes linear regression or simple heuristics are more appropriate and cost-effective.
• Observability is Key: Swept focuses on "supervision" for AI, particularly algorithms with non-determinism. This helps ensure consistent performance, especially when dealing with biases in training data or changes in underlying systems (like Vector databases in RAG).
• Synthetic Testing for Confidence: Move beyond basic unit testing. Synthetically test AI systems to statistically determine their effectiveness. This is crucial for gaining confidence and mitigating risks.
• Realistic Expectations for Unit Tests: LLM-powered features will rarely achieve and maintain 100% passing unit tests. The mindset needs to shift to measuring tests and setting acceptable pass rate benchmarks (e.g., 75% as the "new green").
• Functional, Variant, and Invariant Testing: Implement a mix of test types. Functional tests ensure basic functionality (e.g., "What color is the sky?"). Invariant tests check for consistent behavior when irrelevant data is changed. Variant tests analyze how outputs change when relevant inputs are modified.
• Structured Outputs (JSON) are Your Friend: Force LLMs to output structured data like JSON to make assertions and comparisons easier. Tools like OpenAI's structured outputs can help.
• The "Judge" Problem: Using LLMs to judge the equivalence of other LLM outputs can lead to an endless loop. Limit the judge model's slop (error rate) and accept that near-perfect accuracy might be unattainable.
• Vectors Reign Supreme (For Now): Vector databases are the underlying technology for many AI applications. Docker and Kubernetes provide scaling capabilities. However, simpler solutions often suffice, and open-source models are increasingly competitive with proprietary ones.
• Graph Databases Face Challenges: While conceptually appealing, graph databases currently lack the simplicity and power of vector databases for many AI tasks.
• Internal Use Cases Dominate: Many companies are hesitant to expose AI-powered features directly to customers due to security concerns. Internal applications are more common.
• Security is Paramount: AI systems with memory are vulnerable to attacks and bias injection. "AI red teaming" is crucial to identify and mitigate these risks.
• Code Training Considerations: Training models on code bases presents unique challenges. The quality of publicly available code can be questionable, leading to mediocre results. Proprietary codebases may offer better training data, particularly for legacy systems.
• Sentiment Matters (Even in Training): The sentiment of the data used to train AI models can influence their behavior. Negative or misleading data can skew results.

Developers Need to Learn the Spectrum of AI: Developers can step back and solve the full spectrum of AI problems from heuristics to LLMs.

Swept's Role:

Swept.ai helps companies that have built a demo but cannot scale it, find use cases, or get it released. They show how to get it consistent.

In Conclusion:

The AI landscape is evolving rapidly. By focusing on practical observability, realistic expectations, and robust testing, developers can leverage AI to solve real-world problems while mitigating the risks associated with this powerful technology. Don't just jump on the AI bandwagon; thoughtfully consider whether AI is the right solution for your specific needs.