Prompt Engineering on AIBussin — AI applications, systems and books

Why Are We Still Hand-Writing Prompts?

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

Almost every language-model application starts as one string.

That is not a mistake. A prompt is the fastest way to find out whether a model can do the job at all, and it lets you work directly on the behavior instead of building a framework around a problem you do not yet understand. Most good LM systems begin this way and should.

The trouble is that the prompt usually survives longer than its usefulness. It stops being a probe and becomes the specification, and by the time anyone notices, the string is four hundred words long, nobody remembers why the third paragraph is there, and changing it feels dangerous.

You’ve Probably Already Felt It

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

“If you want something new, you have to stop doing something old.”

You’ve probably done it already.

You had to write a tricky email. Respond to a complaint. Explain something you didn’t want to explain. You opened ChatGPT jotted down your thoughts, hit enter and boom. It came back with something surprisingly useful. Not perfect, maybe, but something to work with.

Just like that, the job that felt heavy five minutes ago was already halfway done.

Learning to Talk to the Machine

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

“The most powerful tool we have is communication.”

🎯 Goal of this Chapter

To show you how to interact with AI in a way that feels natural, open, and effective even if you’ve never used a tool like this before.

We’re not going to teach you the full system yet. We’re just going to start talking. But we’re going to introduce one simple structure a way of shaping your interaction that helps you clarify what you actually want from the conversation and makes it easier for the AI to understand and deliver on that goal.

Codex Manager: Building a Prompt-State Runtime for Hackathon-Grade Code Optimization

Sat, 16 May 2026 20:15:59 +0100

TL;DR

Codex Manager uses AI to generate code as an artifact, then tests that artifact, diagnoses what happened, and repairs the prompt state that produced it. The code is not the thing being optimized directly. The prompt state is.

Summary

Humanity’s Last Hackathon framed the challenge as a test of context, not code: the task was hard enough that the real question was not whether someone could hand-write one clever kernel, but whether they could build a system that used AI effectively under changing constraints.

The “Negative Contrast Trap”: Why AI Writing Overuses “Not X, But Y”

Tue, 10 Mar 2026 12:35:56 +0000

Read enough AI prose and a rhythm starts to appear. Not fear. Not relief. Not strategy.Once you see it, you cannot unsee it.

🧠 Abstract

Large language models frequently produce rhetorical constructions such as “not fear, but relief” or “not intelligence, but memory.” While these patterns exist in human writing, AI systems tend to overproduce them, creating repetitive and unnatural prose. This article identifies the phenomenon as the Negative Contrast Trap, explains why it emerges from statistical language modeling, and proposes practical methods to detect and mitigate it in AI-assisted writing systems.

Learning to Learn: A LATS-Based Framework for Self-Aware AI Pipelines

Thu, 12 Jun 2025 09:23:46 +0100

📖 Summary

In this post, we introduce the LATSAgent, an implementation of LATS: Language Agent Tree Search Unifies Reasoning.. within the stephanie framework. Unlike prior agents that followed a single reasoning chain, this agent explores multiple reasoning paths in parallel, evaluates them using multidimensional scoring, and learns symbolic refinements over time. This is our most complete integration yet of search, simulation, scoring, and symbolic tuning bringing together all of our previous work on sharpening, pipeline reflection, and symbolic rules into a unified, intelligent reasoning loop.

Dimensions of Thought: A Smarter Way to Evaluate AI

Mon, 09 Jun 2025 10:00:03 +0100

📖 Summary

This post introduces a multidimensional reward modeling pipeline built on top of the stephanieanie framework. It covers:

✅ Structured Evaluation Setup How to define custom evaluation dimensions using YAML or database-backed rubrics.
🧠 Automated Scoring with LLMs Using the ScoreEvaluator to produce structured, rationale-backed scores for each dimension.
🧮 Embedding-Based Hypothesis Indexing Efficiently embedding hypotheses and comparing them for contrastive learning using similarity.
🔄 Contrast Pair Generation Creating training pairs where one hypothesis outperforms another on a given dimension.

Programming Intelligence: Using Symbolic Rules to Steer and Evolve AI

Wed, 04 Jun 2025 20:57:20 +0100

🧪 Summary

“What if AI systems could learn how to improve themselves not just at the level of weights or prompts, but at the level of strategy itself? In this post, we show how to build such a system, powered by symbolic rules and reflection.

The paper Symbolic Agents: Symbolic Learning Enables Self-Evolving Agents introduces a framework where symbolic rules guide, evaluate, and evolve agent behavior.

Adaptive Reasoning with ARM: Teaching AI the Right Way to Think

Wed, 28 May 2025 22:22:46 +0100

Summary

Chain-of-thought is powerful, but which chain? Short explanations work for easy tasks, long reflections help on hard ones, and code sometimes beats them both. What if your model could adaptively pick the best strategy, per task, and improve as it learns?

The Adaptive Reasoning Model (ARM) is a framework for teaching language models how to choose the right reasoning format direct answers, chain-of-thoughts, or code depending on the task. It works by evaluating responses, scoring them based on rarity, conciseness, and difficulty alignment, and then updating model behavior over time.

Building Clipper: An AI Image Generator You Control

Wed, 07 May 2025 00:00:00 +0000

“If you’ve ever pasted 50 prompts into an image generator one-by-one, this is for you. I hit my limit and built Clipper to solve it.”

📖 Summary

In the previous blog post I wrote a research paper: Cross-Modal Cognitive Mapping. This paper is about turning your conversations into images to gradually map your thought patterns. The implementation of this paper is an application called Prism.

A component of this app is image generation from prompts or your conversations. All of the Foundation models support this but it’s a pretty janky process where you have to generate the prompt paste it into a text box and download the image. I just went through a week of doing this while building a prompt toolkit. While I was doing this I kept wishing I built the app which I’m going to share with you now.

Mastering Prompt Engineering: A Practical Guide

Wed, 05 Feb 2025 14:58:04 +0000

Summary

This post provides a comprehensive guide to prompt engineering, the art of crafting effective inputs for Large Language Models (LLMs). Mastering prompt engineering is crucial for maximizing the potential of LLMs and achieving desired results.

Effective prompting is the easiest way to enhance your experience with Large Language Models (LLMs).

The prompts we make are our interface to LLMs. This is how we communicate with them. This is why it is important to understand how to do it well.