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Products

Mature, deployable applications built on genai-lab. Entries in this directory are things you can actually run — stable API, versioned checkpoints, inference docs, known limitations.

Products vs Applications

Applications Products (here)
Claim "Here's an approach you could use" "Here's a thing you can run"
Code location examples/<topic>/ scripts src/genailab/applications/<name>/
API Whatever fits the exploration Stable public interface
Tests Informal Inference path covered
Artifacts Optional Versioned checkpoints with metadata
Baseline comparison Nice to have Required
Deployability Not expected Inference without training infra

Applications are a bet on methodology. Products are a commitment to users.

Promotion Criteria

To graduate from docs/applications/<topic>.md to docs/products/<name>/, the work must satisfy all of the following:

  1. Code maturity — implementation lives under src/genailab/applications/<name>/ (not examples/), with a stable public API. Signature changes constitute a breaking change.
  2. Evaluation — benchmarked against at least one published baseline. Results reproducible from a clean environment with the documented command.
  3. Testability — test suite covers the inference path at minimum (training path coverage is a plus but not required).
  4. Deployability — a CLI entry point (via pyproject.toml [project.scripts]) or a library-level public function with stable signature. Inference must run without requiring the training-time infrastructure (e.g., no SkyPilot invocation inside predict()).
  5. Artifacts — trained checkpoints are versioned with metadata: (seed, data version / hash, training config, performance on the benchmark). Artifacts live either on a network volume or a public host (HF Hub), not in git.
  6. Documentation — docs/products/<name>/README.md covers:
  7. What the product does and does not do
  8. Installation and inference quickstart
  9. Expected performance on the benchmark, with confidence intervals
  10. Known limitations and failure modes
  11. Accompanying runnable notebook in notebooks/<topic>/ showing end-to-end use on a new input

Current Products

(none yet)

The perturbation prediction flagship is currently a Stage-2 application (docs/applications/perturbation_prediction.md) — methodology documented, implementation in progress. It becomes a product once the six promotion criteria above are met.

Demotion

Products can be demoted back to applications when they regress — e.g., an upstream dataset changes, a baseline comparison becomes invalid, or a dependency breaks the inference path. This is healthier than silently letting a stale product live in the product tier.

Adding a New Product

  1. Confirm all six promotion criteria are met (not "mostly met" — all).
  2. Create docs/products/<name>/ with README.md following the structure above.
  3. Remove or archive the predecessor docs/applications/<name>.md if it exists (or update it to point to the product).
  4. Add a one-line entry to "Current Products" above.
  5. Announce in a session summary: dev/sessions/YYYY-MM-DD_<name>-graduated.md.