dspy-framework

Automate prompt optimization by replacing manual trial-and-error with a declarative, code-driven workflow. You define inputs and outputs using signatures, then apply optimizers like BootstrapFewShot or MIPROv2 to compile reproducible, production-grade LLM pipelines.

Build reproducible RAG pipelines by using a declarative DSL to define signatures for inputs and outputs. This code-driven approach replaces manual prompt tweaking with automatic optimization, ensuring consistent classification and multi-hop QA results across pipeline versions.

Yes, compiling optimized prompts requires Python 3.9 or higher and the dspy-ai package. You need these environments set up to utilize signatures, modules, and teleprompters effectively for your LLM workflows and classification tasks.

Yes, you can wrap declarative signatures with a ChainOfThought module and manage them using version control. This allows you to track prompt iterations and deploy production patterns with integrated LangSmith evaluation for robust LLM systems.

A declarative DSL replaces manual prompt tuning with state-of-the-art teleprompters that automatically optimize LLM workflows. This approach ensures reproducible pipelines, enables version control, and significantly reduces the trial-and-error effort for production systems.

Automatic prompt optimization using teleprompters requires providing a training set of examples and a defined signature. Without sufficient, high-quality training data, the optimizer cannot effectively compile accurate, production-ready prompts for complex multi-hop QA or classification tasks.