DEJAN's Bayesian Content Optimizer runs an iterative loop that tests and updates seeded prior beliefs about what makes content rank in LLM responses, using a multi-armed bandit acquisition policy to sample hypotheses across structured rank factors and continuously rewrite snippets or pages until target positions are achieved.
flowchart TD
A[Initial Content & SERP Baseline] --> B[Seeder: Generate Hypotheses & Priors]
B --> C[Classifier: Assign Rank Factor Classes]
C --> D[Acquisition Policy: Thompson / UCB Sampling]
D --> E[Ideator: Craft Snippet / Page Edit]
E --> F[Ranker: Evaluate Ranks Across Samples]
F --> G{Target Improved?}
G -- Yes: Reward = 1 --> H[Update Beta Distribution Posterior]
G -- No: Reward = 0 --> H
H --> I{Rank 1 or Budget Spent?}
I -- No --> D
I -- Yes --> J[Winning Content & Content Brief]Referenced by