How does the automated resume screening workflow using Workday AI compare to building a custom RAG pipeline with LangChain and Llama 3?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
How does the automated resume screening workflow using Workday AI compare to building a custom RAG pipeline with LangChain and Llama 3?
2 real searches · 1 real-search pass
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
ChatGPT real searches (2)
Classes come from the query-classifier (a local Laya model). 2 of 2 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT2 real searches · gpt-6
Workday AI recruiting candidate matching resume screening official Workday AI features Google ↗
LangChain retrieval augmented generation documentation Llama 3 Meta official model documentation resume screening bias hiring AI EEOC Google ↗
- Recruitment Privacy Statement · workday.com
- Reference: HiredScore AI for Recruiting · doc.workday.com
- LangChain RAG tutorial · python.langchain.com
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
Workday AI recruiting candidate matching resume screening official Workday AI features OBSERVED | Google ↗ AI Mode ↗ | — | Other / navigational | — | — | 1x of 1 | |
LangChain retrieval augmented generation documentation Llama 3 Meta official model documentation resume screening bias hiring AI EEOC OBSERVED | Google ↗ AI Mode ↗ | — | Reason | — | — | 1x of 1 |