Evolving GraphQL: Designing APIs That Deliver Insights with Intelligent Resolv…
summary
Claim: embed AI into GraphQL resolvers so a single query returns review summary, sentiment, recommendations instead of raw rows. Implementation uses LLM calls (they mention Grok/GPOSS or self‑hosted models), prompt versioning and a semantic layer, plus data loader and batching to cut LLM cost from ~$400/day to ~$40/day. Demo timings show raw GraphQL 18 ms vs intelligent resolver ~5,000 ms. Operational safeguards include caching, documented responses, schema enforcement and fallbacks for API timeouts.
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chapters (9)
- 0:00API evolution and intelligence gap
- 2:35REST to GraphQL and AI APIs
- 6:53Cost of data intelligence gap
- 8:19Intelligent resolvers in GraphQL
- 11:51Resolvers with AI and LLMs
- 17:16AI challenges in GraphQL
- 20:46Cost: traditional vs intelligent GraphQL
- 22:29Live demo: intelligent resolvers
- 24:14Q&A
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