Post
2009
⚙️ Prompt Optimization with Haystack and DSPy
Experimental notebook: 🧪📓 https://github.com/deepset-ai/haystack-cookbook/blob/main/notebooks/prompt_optimization_with_dspy.ipynb
When building applications with LLMs, writing effective prompts is a long process of trial and error. 🔄
Often, if you switch models, you also have to change the prompt. 😩
What if you could automate this process?
💡 That's where DSPy comes in - a framework designed to algorithmically optimize prompts for Language Models.
By applying classical machine learning concepts (training and evaluation data, metrics, optimization), DSPy generates better prompts for a given model and task.
Recently, I explored combining DSPy with the robustness of Haystack Pipelines.
Here's how it works:
▶️ Start from a Haystack RAG pipeline with a basic prompt
🎯 Define a goal (in this case, get correct and concise answers)
📊 Create a DSPy program, define data and metrics
✨ Optimize and evaluate -> improved prompt
🚀 Build a refined Haystack RAG pipeline using the optimized prompt
Experimental notebook: 🧪📓 https://github.com/deepset-ai/haystack-cookbook/blob/main/notebooks/prompt_optimization_with_dspy.ipynb
When building applications with LLMs, writing effective prompts is a long process of trial and error. 🔄
Often, if you switch models, you also have to change the prompt. 😩
What if you could automate this process?
💡 That's where DSPy comes in - a framework designed to algorithmically optimize prompts for Language Models.
By applying classical machine learning concepts (training and evaluation data, metrics, optimization), DSPy generates better prompts for a given model and task.
Recently, I explored combining DSPy with the robustness of Haystack Pipelines.
Here's how it works:
▶️ Start from a Haystack RAG pipeline with a basic prompt
🎯 Define a goal (in this case, get correct and concise answers)
📊 Create a DSPy program, define data and metrics
✨ Optimize and evaluate -> improved prompt
🚀 Build a refined Haystack RAG pipeline using the optimized prompt
