LLM Lifecycle
Complete lifecycle of large language models from pre-training through fine-tuning, RLHF, and deployment — with architecture diagrams and production considerations.
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After this section you can
- Walk the five phases from training data to serving, and say which costs are one-off and which recur on every request
- Explain what pretraining learns versus what post-training (SFT and RLHF) adds
- Point at the phase responsible for a given quality, safety, cost, or latency problem
LLM Lifecycle — From Data to Deployment
The end-to-end journey of a modern large language model — from raw web text to a low-latency production endpoint. Every Staff FDE should be able to trace all five phases, because each one quietly sets the ceiling on quality, safety, cost, and latency that you inherit downstream.
A shipped model is the frozen output of a pipeline you never see. Pretraining decides what it knows; post-training decides how it behaves; inference is where you pay for it — over and over, on every single token, forever. Training is a one-time capital cost. Inference is the bill that never stops.