The original title is about debugging LLM errors by looking at traces and tools that make it easy. Let me rewrite this as a punchy headline.

Short summary

LLM hallucinations are typically caused by bad context, stale documents, or tool failures—not the model itself. Debugging requires the full trace of a single request: retrieved chunks, templated prompts, and tool IO. This guide compares six tracing platforms (Helicone, LangSmith, Langfuse, Future AGI, Braintrust, and Arize Phoenix) on integration speed and observability, highlighting that OpenTelemetry-native tools provide superior cross-system debugging.

• •LLM hallucinations usually stem from bad context, stale documents, or tool failures—not model creativity
• •Six tracing tools compared: Helicone (fastest setup), LangSmith (richest LangChain view), Langfuse (open-source + OTel), Future AGI (OTel-native), Braintrust (eval-focused), Arize Phoenix (local/persistent)
• •OpenTelemetry-native platforms correlate LLM spans with HTTP, database, and downstream service calls in a single trace

Generated with AI, which can make mistakes.