PromptWeave architecture series

Start with design scenarios where prompts, models, and experiments become coupled, then derive versioning, routing, fallback, and release snapshots.

For developers building AI applications with multiple prompts, models, or experiments.

What you will ship

You will be able to define configuration boundaries and design an auditable, verifiable, recoverable release path.

01

Establish the problem boundary

Start with a design scenario where a configuration table stops being enough.

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1. 01 A Prompt Is More Than Text Derive the ownership boundary from a scenario where prompts, models, and experiments become coupled. Planned
2. 02 PromptCode, ModelConfig, and Routing Separate caller facts, prompt revisions, model configuration, and typed routing. Planned

02

Protect one execution

Continue the same scenario to derive fallback and execution trace boundaries.

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1. 03 A Trustworthy Fallback Path Use explicit fallback bindings and run traces instead of ad hoc model substitution. Planned

03

Ship the verified version

Use a winner-promotion scenario to derive approval, loaded-hash checks, and full rollback.

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1. 04 Why Releases and Rollbacks Use Full Snapshots Derive immutable versions, environment pointers, approval hashes, and full rollback from an A/B winner promotion. Planned

Reference baseline

These sources support the concepts and terminology. The articles use original explanations and Pi implementations.

• project PromptWeave README
• architecture PromptWeave Architecture Contract