What factors should a lean engineering team consider when choosing between fine tuning an open source LLM and using prompt engineering with GPT 4o
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
What factors should a lean engineering team consider when choosing between fine tuning an open source LLM and using prompt engineering with GPT 4o
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). 1 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
OpenAI GPT-4o API pricing official documentation fine tuning open weight model operational costs comparison Google ↗
OpenAI GPT-4o model documentation capabilities context window official Google ↗
- GPT-4o Model · developers.openai.com
- Pricing · developers.openai.com
- Fine-tuning now available for GPT-4o · openai.com
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
OpenAI GPT-4o API pricing official documentation fine tuning open weight model operational costs comparison OBSERVED | Google ↗ AI Mode ↗ | — | Comparison | — | — | 1x of 1 | |
OpenAI GPT-4o model documentation capabilities context window official OBSERVED | Google ↗ AI Mode ↗ | — | Other / navigational | — | — | 1x of 1 |