AI-Augmented Platform Engineering
summary
Claims that applying LLMs and retrieval‑augmented generation to platform engineering raises developer productive time from about 30% to 60% and can approach 90% with domain‑specific RAGs. Lists concrete tools and practices: GPT‑4 and Codex for code/IAC generation, explicit training steps (epochs, batch size, hyperparameters, optimizers), and platform pieces: identifying BFSI APIs (accounts, balances, payments, loans), building RAGs, a self‑serve portal, dev containers and CI pipelines plus observability and security checks.
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chapters (10)
- 0:00intro
- 0:14overview of ai augmented platform engineering
- 0:40applying gen ai principles to platform engineering
- 1:07defining platform engineering
- 3:23components of platform engineering
- 7:00evolution of platform engineering
- 9:33role of ai and llms in platform engineering
- 13:37case study: enhancing developer experience in bfsi
- 14:39steps to implement domain-specific rags
- 21:22conclusion and future outlook
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