Is it worth fine tuning Llama 3 ourselves or should we just pay for an enterprise wrapper with better data privacy for our family budget planning app?
Download run
Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
Is it worth fine tuning Llama 3 ourselves or should we just pay for an enterprise wrapper with better data privacy for our family budget planning app?
3 real searches · 1 real-search pass
Meta Llama 3 license commercial use fine tuning official documentation 2026Google ↗
OBSERVEDComparison
enterprise AI API data privacy retention training controls official documentation OpenAI Anthropic AWS BedrockGoogle ↗
OBSERVEDOther / navigational
LLM fine tuning versus RAG structured data financial application best practices evaluationGoogle ↗
OBSERVEDComparison
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 (3)
Classes come from the query-classifier (a local Laya model). 2 of 3 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT3 real searches · gpt-6
Meta Llama 3 license commercial use fine tuning official documentation 2026 Google ↗
enterprise AI API data privacy retention training controls official documentation OpenAI Anthropic AWS Bedrock Google ↗
LLM fine tuning versus RAG structured data financial application best practices evaluation Google ↗
- Data retention · docs.aws.amazon.com
- AWS Bedrock · aws.amazon.com
- Data controls in the OpenAI platform · developers.openai.com
- Offering Zero Data Retention for frontier models · openai.com
- API and data retention · platform.claude.com
- Llama 3 license · github.com
Queries
3 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
Meta Llama 3 license commercial use fine tuning official documentation 2026 OBSERVED | Google ↗ AI Mode ↗ | — | Comparison | — | — | 1x of 1 | |
enterprise AI API data privacy retention training controls official documentation OpenAI Anthropic AWS Bedrock OBSERVED | Google ↗ AI Mode ↗ | — | Other / navigational | — | — | 1x of 1 | |
LLM fine tuning versus RAG structured data financial application best practices evaluation OBSERVED | Google ↗ AI Mode ↗ | — | Comparison | — | — | 1x of 1 |