Optimizing Tool Selection for LLM Workflows: Differentiable Programming with PyTorch and DSPy
Modern agentic architectures rely heavily on chaining LLM calls. A typical pattern looks like:Use an LLM to decide which tool to invokeCall the tool (e.g. search, calculator, API)Use another LLM call to interpret the result and generate a final responseThis structure is easy to reason about, simple to prototype, and generalizes well.But it scales poorly.Each LLM call incurs latency, cost, and token overhead. More subtly, it compounds context: every step includes not only the original query, but ...
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