Is fine-tuning Llama 3 on custom data actually better than using retrieval augmented generation with a vector database like Qdrant?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
Is fine-tuning Llama 3 on custom data actually better than using retrieval augmented generation with a vector database like Qdrant?
1 real search · 1 real-search pass
Meta Llama 3 fine tuning documentation supervised fine tuning use cases RAG Qdrant documentation retrieval augmented generation best practicesGoogle ↗
OBSERVED
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.
Classifying 1 queries…
Searches and sources
ChatGPT1 real searches · gpt-6
Meta Llama 3 fine tuning documentation supervised fine tuning use cases RAG Qdrant documentation retrieval augmented generation best practices Google ↗
- Fine-tuning · dev.meta.ai
- Prompt engineering · dev.meta.ai
- RAFT: Sailing Llama towards better domain-specific RAG · ai.meta.com
- llama-cookbook/end-to-end-use-cases/RAFT-Chatbot/README.md at main · meta-llama/llama-cookbook · github.com
- How to fine-tune: Focus on effective datasets · ai.meta.com