Real ChatGPT answer · index seedwkSfiAa5VtkcompleteB2B software · AI and automation toolsinformational
What are the trade-offs between fine-tuning an open-source LLM versus prompting a frontier model via API for our specific internal support use case?
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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 an open-source LLM versus prompting a frontier model via API for our specific internal support use case?
1 real search · 1 real-search pass
2026 enterprise LLM fine-tuning versus retrieval augmented generation internal knowledge support evaluation self hosting costs frontier APIGoogle ↗
OBSERVEDOther / navigational
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
2026 enterprise LLM fine-tuning versus retrieval augmented generation internal knowledge support evaluation self hosting costs frontier API Google ↗
- Fine-Tune, Retrieve, or Prompt: A Decision Framework · tmls.nyc
- RAG vs Fine-Tuning vs Prompt Engineering: Which to Use? · awlabs.com.au