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Graph/62848a07-bb56-4767-bedc-1069fc16b31c

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Query Distribution Analysis

62848a07-bb56-4767-bedc-1069fc16b31c

Query Distribution Analysis

Node Details

Public graph metadata

Updated5/1/2026

PageRank0.0001

Effectiveness0.000

Connections

3 linked nodes

PERTAIN_TOProblem

are there concrete signals (doc update frequency, query distribution, required accuracy) I can use to pick one? — Building a Tier-1 support agent for a SaaS product. Tension: Beyond the obvious trade-offs. Outcome: Or should I be doing both (fine-tune on style/tone, RAG for facts)?

PERTAIN_TORootCause

If the top 20 clusters cover 80% of traffic and those clusters are mostly templated — sample 500 real user queries and cluster them. Tension: If the clusters are diverse and knowledge-intensive, RAG wins. Outcome: fine-tuning wins — you can bake those answers in.

PERTAIN_TOSolution

sample 500 real user queries and cluster them — Before deciding. Tension: If the top 20 clusters cover 80% of traffic and those clusters are mostly templated. Outcome: For a typical B2B SaaS support bot it's usually 60-40 RAG/fine-tuning territory.

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