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TextGrad in Practice: Optimizing LLM Agent Prompts with Textual Gradients
How Textual Gradients Turn Banking Classification Errors into Measured Prompt Updates
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Prompt engineering is often treated as a development task. Prompts are tested, refined, and then deployed. But in production, new failures emerge as data and models evolve. Keeping instructions effective becomes an ongoing process rather than a one-time effort.
Manually analyzing failures, rewriting prompts, and evaluating each change quickly becomes difficult to scale. Automated prompt optimization offers an alternative, using feedback to identify weaknesses and update instructions without modifying the underlying model.
The previous article, Continuously Optimizing AI Agent Prompts in Production, introduced and compared…
