Using DSPy to enrich a dataset of the Nobel laureate network
I've been working a fair bit with DSPy lately, and I did some work in combining the benefits of vector search and LLMs (via a DSPy pipeline) to disambiguate records with a high degree of accuracy to help enrich a dataset. The blog post shows how this approach scales well, is very cost-effective and super concise - all it takes is < 100 lines of DSPy code and it all runs async. The code to reproduce is in this repo if anyone's interested (all tools are 100% free and open source, and the methodology will work with open weight LLMs too).…
Using DSPy to enrich a dataset of the Nobel laureate network launched on August 5, 2025 with 8 votes, #2,487 of 8,530 launches that month and more than 75% of that year's launches.
What it does
In the maker’s words, at launch
I've been working a fair bit with DSPy lately, and I did some work in combining the benefits of vector search and LLMs (via a DSPy pipeline) to disambiguate records with a high degree of accuracy to help enrich a dataset. The blog post shows how this approach scales well, is very cost-effective and super concise…
Does a similar job
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Source: Show HN post ↗ · launch text quoted in part
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