Training and Using Generative AI Models with GraphQL
summary
Claims concrete workflows: use LLMs to turn natural-language-to-schema prompts into GraphQL schemas (some open-source projects already prototype this); train models on many subgraph → supergraph pairs so the model synthesizes a merged supergraph instead of manual federation; run a learned next-query prefetcher that predicts the immediate next GraphQL query to pre-warm resolvers and reduce tail latency; and apply log-based ML to do field-correlation anomaly detection that spots query combos driving outages. Presenter frames these as prototypes and roadmaps rather than production-validated systems.
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chapters (7)
- 0:00Introduction and context
- 1:51The evolution of AI
- 7:25AI core principles
- 9:34AI in schema generation
- 11:08Super graph optimization
- 13:27Predictive query execution
- 14:09Outage and anomaly detection
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