LLM-Driven Forecasting and DSPy Optimization

Implement the core forecasting logic. Using the semantic abstractions generated in the previous step, prompt the LLM (GPT-5 Pro via Together AI) to generate future predictions for a given forecast horizon. Apply DSPy to systematically optimize the LLM's forecasting prompts and reasoning steps. This might involve few-shot examples, chain-of-thought prompting, or custom DSPy modules. Track the performance of different DSPy-optimized prompts using Weights & Biases.