Case-Based Reasoning on AIBussin — AI applications, systems and books

Beyond the Book: Production DSPy Systems (Appendix)

Thu, 03 Sep 2026 11:00:00 +0100

Chapter 20 assembled the book’s mechanisms into one loop. This appendix does something smaller and more specific: it shows three of those mechanisms already running in a production codebase that was not written for this book, and it holds itself to a stricter evidence rule than the chapters do.

Evidence rule for this appendix. There are two tiers here. The mechanics around each language-model call — UCT selection, the budget gate, the trace cache, the champion-update rule, the output parser — are deterministic software, and a committed fixture (experiments/dspy-from-first-principles/ch21_beyond_book/) exercises them with no LM and no DSPy import. Those results are measured. The kernels themselves — the production modules the excerpts are drawn from — are selection evidence from one codebase, not a generalisation. Their full source lives in the Stephanie repository, linked below and pinned to main at revision 32d02ee (2026-01-06).

Episteme: Distilling Knowledge into AI

Fri, 03 Oct 2025 11:24:58 +0100

🚀 Summary

When you can measure what you are speaking about… you know something about it; but when you cannot measure it… your knowledge is of a meagre and unsatisfactory kind. Lord Kelvin

Remember that time you spent an hour with an AI, and in one perfect response, it solved a problem you’d been stuck on for weeks? Where is that answer now? Lost in a scroll of chat history, a fleeting moment of brilliance that vanished as quickly as it appeared. This post is about how to make that moment permanent, and turn it into an intelligence that amplifies everything you do.

From Photo Albums to Movies: Teaching AI to See Its Own Progress

Sun, 07 Sep 2025 08:50:43 +0100

🥱 TLDR

This post details the implementation of:

PACS: Implicit Actor–Critic Coupling via a Supervised Learning Framework for RLVR
NER Retriever: Zero-Shot Named Entity Retrieval with Type-Aware Embeddings within our self-improving AI, Stephanie.

The core idea is to move beyond static, single-point feedback to a richer, more dynamic form of learning: