What are the main architectural differences and trade-offs between Retrieval-Augmented Generation and fine-tuning open source models for niche technical support bots?
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The searches the AI actually ran for this prompt.
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What are the main architectural differences and trade-offs between Retrieval-Augmented Generation and fine-tuning open source models for niche technical support bots?
1 real search · 1 real-search pass
Retrieval augmented generation architecture documentation vector search chunking reranking fine tuning LoRA PEFT tradeoffs technical support LLMGoogle ↗
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Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
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Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
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Searches and sources
ChatGPT1 real searches · gpt-6
Retrieval augmented generation architecture documentation vector search chunking reranking fine tuning LoRA PEFT tradeoffs technical support LLM Google ↗
- Comparing Retrieval Augmented Generation and fine-tuning · docs.aws.amazon.com
- Retrieval augmented generation (RAG) and indexes in Microsoft Foundry - Microsoft Foundry · learn.microsoft.com
- Fine-Tune, Retrieve, or Prompt: A Decision Framework · tmls.nyc
- RAG and Generative AI - Azure AI Search · learn.microsoft.com
- architecture-center/docs/ai-ml/guide/rag/rag-information-retrieval.md at main · MicrosoftDocs/architecture-center · github.com