How do I estimate token usage and costs for fine tuning an OpenAI model for a specialized industry vocabulary compared to using few shot prompting with gpt-4?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
How do I estimate token usage and costs for fine tuning an OpenAI model for a specialized industry vocabulary compared to using few shot prompting with gpt-4?
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
OpenAI fine-tuning pricing training tokens inference pricing GPT-4 API official Google ↗
OpenAI fine-tuning guide token counting training dataset epochs official Google ↗
- Pricing · developers.openai.com
- Supervised fine-tuning · developers.openai.com
- GPT-4 Model · developers.openai.com
- Fine-tuning best practices · developers.openai.com
- Model optimization · developers.openai.com
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
OpenAI fine-tuning pricing training tokens inference pricing GPT-4 API official OBSERVED | Google ↗ AI Mode ↗ | — | Comparison | — | — | 1x of 1 | |
OpenAI fine-tuning guide token counting training dataset epochs official OBSERVED | Google ↗ AI Mode ↗ | — | Other / navigational | — | — | 1x of 1 |