Fail-Open LLM Architecture: Protecting Your Pipeline from Reviewer Failures
The article discusses the importance of implementing a fail-open architecture for LLM-based pipelines to prevent cascading failures when the reviewer stage fails. It introduces the circuit breaker pattern as a solution to this problem.
Why it matters
Implementing a fail-open architecture for LLM-based pipelines is crucial to ensuring the reliability and robustness of AI-powered applications.
Key Points
- 1LLM outages and quality degradation can break production pipelines that rely on multiple LLM calls in series
- 2The common pattern of failing the entire pipeline when the reviewer stage fails is problematic
- 3The circuit breaker pattern can be used to fail-open the reviewer stage and pass the primary decision through unmodified
- 4This approach reduces the impact of reviewer failures and improves the overall reliability of the pipeline
Details
The article starts by describing real-world incidents where LLM outages and quality degradation issues at OpenAI and Anthropic caused widespread failures in dependent products. It then highlights the common pattern of failing the entire pipeline when the reviewer stage fails, which can lead to losing the output of both the primary and reviewer models. The author introduces the circuit breaker pattern as a solution to this problem. Circuit breakers are a well-established design pattern in distributed systems that can prevent cascading failures. When applied to an LLM reviewer stage, the circuit breaker should be configured to 'fail-open' and pass the primary decision through unmodified when the reviewer fails, rather than returning a null or error response. This approach reduces the impact of reviewer failures and improves the overall reliability of the pipeline.
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