Optimize the Instructions

Fri, 28 Aug 2026 10:55:00 +0100

Chapter 11’s optimizer could only select demonstrations. It never touched an instruction, and it never looked at the development set — which is why upgrading its metric changed nothing.

MIPROv2 does both.

instruction candidates
+
demonstration candidates
+
metric
+
development data
      ↓
candidate program

This is a strictly larger search over a strictly larger space, evaluated against evidence the previous optimizer could not see. If chapter 11’s result was an artifact of a weak mechanism, this is where it gets corrected.

Compiling Thought: Building a Prompt Compiler for Self-Improving AI

Thu, 26 Jun 2025 13:31:41 +0100

How to design a pipeline that turns vague goals into smart prompts

🧪 Summary

Why spend hours engineering prompts when AI can optimize its own instructions. This blog post introduces a novel approach toward creating a self-improving AI by treating prompts as programs. Traditional AI systems often rely on static instructions rigid and limited in adaptability. Here, we present a different perspective: viewing the Large Language Model (LLM) as a prompt compiler capable of dynamically transforming raw instructions into optimized prompts through iterative cycles of decomposition, evaluation, and intelligent reassembly.

Prompt Optimization on AIBussin — AI applications, systems and books

Optimize the Instructions

Compiling Thought: Building a Prompt Compiler for Self-Improving AI

🧪 Summary