What are the trade-offs between fine-tuning a smaller open-source model versus using prompt engineering with GPT-4o for specialized legal contract review?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
What are the trade-offs between fine-tuning a smaller open-source model versus using prompt engineering with GPT-4o for specialized legal contract review?
1 real search · 1 real-search pass
OpenAI GPT-4o model documentation fine-tuning guide smaller models legal contract review evaluation NIST AI risk management legal generative AIGoogle ↗
OBSERVEDReason
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 (1)
Classes come from the query-classifier (a local Laya model). 1 of 1 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT1 real searches · gpt-6
OpenAI GPT-4o model documentation fine-tuning guide smaller models legal contract review evaluation NIST AI risk management legal generative AI Google ↗
- Fine-tuning · playbooks.aip.gov.sg
- Transparency Note for Azure OpenAI in Microsoft Foundry Models - Microsoft Foundry · learn.microsoft.com
- Fine-tuning best practices · developers.openai.com
- Model optimization · developers.openai.com
- Supervised fine-tuning · developers.openai.com
- Reinforcement fine-tuning use cases · developers.openai.com