Implementing Semantic Abstraction with GPT-5 Pro

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Prompt Content

Develop a module that generates 'semantic abstractions' from raw time series data and associated contextual information (e.g., event logs, market news). Utilize GPT-5 Pro (or an equivalent model via Together AI) with DSPy to prompt the LLM to identify trends, seasonality, anomalies, and potential causal factors, expressing them in natural language. The output should be a structured textual summary that can guide subsequent forecasting. Focus on creating robust DSPy programs that reliably extract these insights even with noisy data.

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