ai-building-pipelines

What problem does it solve?

Decomposing hard AI tasks into modular, multi-step pipelines makes complex problems tractable, enabling clearer reasoning, easier testing, and scalable workflows.

Core Features & Use Cases

• Modular DSPy pipelines: wire multiple stages in forward() to build end-to-end AI workflows.
• Common patterns: chain classification, retrieval, drafting, verification; parallel analysis; ensemble candidate generation.
• Use cases: building multi-stage assistants, knowledge-base assistants, and automated decision pipelines across domains such as support, content generation, and data processing.

Quick Start

Define DSPy stages, implement forward() to pass data through them, and test the end-to-end pipeline on a representative task.