7 Things That Break LLM Apps in Production
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Seven common failure modes that break LLM-powered apps in production, drawn from real incidents. Covers missing timeouts and retries (a hung HTTP call with no timeout silently killed an email-drafting agent for five hours), prompt injection risks when untrusted input reaches the context window, writing tests that assert on schema shape rather than exact strings, unbounded token costs from naive chat history loops, silent context-window overflow causing models to ignore instructions, the need for a lightweight eval harness to measure prompt changes, and the importance of validating and parsing every model output rather than trusting it. Each issue includes concrete Python code patterns and TypeScript equivalents where relevant.
