Learning DSPy (v3.1 - 2026 Edition) | Season 2 — Episode 15

Push DSPy to its limits. The final episode covers program transformations via dspy.Ensemble and the experimental BetterTogether meta-optimizer, which combines prompt tuning with weight finetuning for maximum performance.

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Learning DSPy (v3.1 - 2026 Edition) | Season 2 — Episode 14

Connect your agents to universal tool servers. This episode explains how to use the Model Context Protocol (MCP) in DSPy to leverage standardized tools across different frameworks with minimal setup.

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Learning DSPy (v3.1 - 2026 Edition) | Season 2 — Episode 13

Take full control over tool execution. This episode covers manual tool handling in DSPy using dspy.Tool, dspy.ToolCalls, and native function calling for latency-sensitive applications.

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Learning DSPy (v3.1 - 2026 Edition) | Season 2 — Episode 12

Learn how to give language models access to external tools. This episode covers the dspy.ReAct module, demonstrating how to build autonomous agents that reason and interact with APIs dynamically.

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Learning DSPy (v3.1 - 2026 Edition) | Season 2 — Episode 11

Discover how to distill massive language models into smaller, efficient ones. This episode covers BootstrapFinetune, explaining how to convert a prompt-based DSPy program into a customized, weight-updated model.

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Learning DSPy (v3.1 - 2026 Edition) | Season 2 — Episode 10

Dive into automatic instruction tuning. This episode explores MIPROv2 and COPRO, showing how DSPy uses Bayesian Optimization and coordinate ascent to discover superior, counter-intuitive prompt instructions.

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Learning DSPy (v3.1 - 2026 Edition) | Season 2 — Episode 9

Learn how DSPy automates few-shot prompting. This episode focuses on BootstrapFewShot and BootstrapFewShotWithRandomSearch, explaining how they synthesize, filter, and inject high-quality examples into your prompts.

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Learning DSPy (v3.1 - 2026 Edition) | Season 2 — Episode 8

Enter the core magic of DSPy: Optimizers. This episode provides an overview of what optimizers do, the iterative optimization cycle, and the unusual 20/80 data splitting strategy for prompt optimization.

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