⚙️ Prompt Optimization with Haystack and DSPy
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⚙️ Prompt Optimization with Haystack and DSPy
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Experimental notebook: 🧪📓 
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Experimental notebook: 🧪📓 
https://github.com/deepset-ai/haystack-cookbook/blob/main/notebooks/prompt_optimization_with_dspy.ipynb
https://github.com/deepset-ai/haystack-cookbook/blob/main/notebooks/prompt_optimization_with_dspy.ipynb
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When building applications with LLMs, writing effective prompts is a long process of trial and error. 🔄
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When building applications with LLMs, writing effective prompts is a long process of trial and error. 🔄
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Often, if you switch models, you also have to change the prompt. 😩
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Often, if you switch models, you also have to change the prompt. 😩
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What if you could automate this process?
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What if you could automate this process?
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💡 That's where DSPy comes in - a framework designed to algorithmically optimize prompts for Language Models.
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💡 That's where DSPy comes in - a framework designed to algorithmically optimize prompts for Language Models.
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By applying classical machine learning concepts (training and evaluation data, metrics, optimization), DSPy generates better prompts for a given model and task.
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By applying classical machine learning concepts (training and evaluation data, metrics, optimization), DSPy generates better prompts for a given model and task.
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Recently, I explored combining DSPy with the robustness of Haystack Pipelines.
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Recently, I explored combining DSPy with the robustness of Haystack Pipelines.
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Here's how it works:
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Here's how it works:
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▶️ Start from a Haystack RAG pipeline with a basic prompt
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▶️ Start from a Haystack RAG pipeline with a basic prompt
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🎯 Define a goal (in this case, get correct and concise answers)
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🎯 Define a goal (in this case, get correct and concise answers)
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📊 Create a DSPy program, define data and metrics
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📊 Create a DSPy program, define data and metrics
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✨ Optimize and evaluate -> improved prompt
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✨ Optimize and evaluate -> improved prompt
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🚀 Build a refined Haystack RAG pipeline using the optimized prompt
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🚀 Build a refined Haystack RAG pipeline using the optimized prompt
⚙️ 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
👍
victor Jofthomas adamm-hf aplrat ijohn07 ajibawa-2023 oneiroid
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Jofthomas adamm-hf mathiasn1 GPT007
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victor Jofthomas adamm-hf
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aplrat GPT007
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Joffrey THOMAS
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