Reasoning Ladder

Retrieval, SQL, benchmarks, and recursive reasoning patterns turned into repeatable engineering work instead of one-off prompt tricks.

Quality comes from structure, measurement, and feedback loops. The prompt is only one layer in that stack.

Evidence firstRepeatable testsExplainable outputs

What this proves

Use retrieval to ground the answer in the right evidence.

Use SQL to make data questions deterministic where possible.

Use evaluation to compare outputs over time instead of trusting memory.

Open-source stack

SQLRAGPyTorchscikit-learnpromptfooBenchmark harness

Experience mode

Step 1

Question

Step 2

Retriever

Step 3

Context

Step 4

Answer

Context pull

The right answer starts with the right evidence.

Live pattern

Engineering lens

The system should pull evidence before it reasons.
Good retrieval keeps hallucinations from becoming policy.
The evidence set should be inspectable.

Platform fit

This project belongs in Llewellyn Systems because it turns a repeated engineering pattern into a governed operating asset. The page is not a slide deck. It is a proof surface for how the system is built and how it behaves.

Toolchain note

Use JupyterBook for publication, MyST for source text, Voilà for notebook apps, Binder for reproducible environments, and JupyterLab or Colab for interactive editing. The page itself is the front door to that workflow.

Related projects

Skills + MCP