Getting Started with RAG in DSPy!

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AI Summary

Tutorial Summary: Getting Started with Rag and DSP

Introduction

• Excitement about DSP (Data Science Programming) after completing the tutorial.
• Overview of DSP’s end-to-end process: loading datasets, defining LL (Language Learning) metrics, and optimizing prompts.
• Anticipation of entering a new era of LLM programming with DSP’s prompting and optimization framework.

Tutorial Content

• Full end-to-end tutorial on DSP.
• Loading datasets and converting data into ds.example objects.
• Defining an LL metric and writing Rag programs using the DSPI programming model.
• Exploring built-in modules like Chain of Thought and React.
• Compiling and optimizing prompts using techniques like bootstrapping few-shot examples and Bayesian signature optimization.
• Utilizing machine learning practices like train/test data splitting for program evaluation.
• Improvement of uncompiled Rag program by about 30% using optimizers.

Additional Information

• Code for the tutorial is open source on GitHub (we8 recipes).
• Acknowledgments to colleagues at we8 and members of the DSP community for their contributions.
• Apologies for any zooming issues in the video and assurance of zero zooming for better readability.

DSP Programming Model

• Writing LLM programs with default modules for enhanced prompting.
• Compilation or optimization of prompts used in LLMs.
• Adding depth to LLM programs for improved performance, analogous to adding layers in neural network optimization.

Optimization Process

• Using DSP’s bootstrap few-shot and Bayesian signature optimizer to refine prompts.
• Evaluating the performance of Rag programs with an LLM metric.
• The tutorial demonstrates the optimization process and its impact on program performance.

Conclusion

• Encouragement to explore DSP and its potential for creating various LLM programs.
• Invitation to subscribe to the channel for more DSP-related content and to join the DSP Discord community.
• Openness to feedback and discussion in the comments section.

Links and Resources

• GitHub repository for code examples.
• DSP Discord community for support and idea exchange.
• Future videos on connecting DSP to local LLMs and extending Rag programs.

Final Thoughts

• The tutorial serves as a “hello world” for DSP, similar to a classification tutorial in PyTorch.
• The presenter shares their excitement and hopes to inspire interest in DSP.
• The tutorial aims to demonstrate the use of DSP, program writing, and optimization.