Optimizing Prompts for Language Model Pipelines: DSPy MIPROv2

I’ve been diving deep into prompt optimization for large language models (LLMs) lately, especially as we build more complex NLP pipelines — or Language Model Programs.

These are workflows that chain together multiple LLM calls to tackle sophisticated tasks.

While powerful, designing these pipelines isn’t straightforward because each module may require prompts that work well together, and crafting them by hand is both time-consuming and inefficient.

Recently, I came across a workflow by Karthik Kalyanaraman that offers a practical approach to prompt optimization for multi-stage LLM programs.

But before diving into that, I want to quickly summarize 2 main challenges:

• The proposal problem: The first challenge is the sheer size of the prompt space. With multiple modules, the number of possible prompts becomes intractably large. We need a way to efficiently generate high-quality prompt candidates without exhaustively searching the entire space.
• The credit assignment problem: The second challenge is figuring out which parts of your prompt are actually contributing to better performance. In multi-stage pipelines, it’s tough to determine how changes in one module’s prompt affect the overall outcome. We lack intermediate labels or metrics for individual LLM calls, so we need strategies to assign credit to different prompt components effectively.