Suggest edit — llm behavior improvement

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---
status: raw
tags:
- ai
title: llm behavior improvement
type: idea
updated: 2026-04-11
visibility: public
---

# llm behavior improvement

the observation that LLMs fail in predictable ways — sycophancy, context drift, hallucinated confidence, inconsistency across conversation turns — and that these failures are not just model problems but also prompt engineering and scaffolding problems. the idea is to systematically study and mitigate these failure modes through a combination of better prompting patterns, structured context management, and behavioral testing frameworks.

one concrete direction: building a suite of tests that probe specific behavioral failure modes (does the model change its answer when the user pushes back? does it maintain consistency over a long conversation? does it respect negative constraints?). another direction: studying what kinds of AGENTS.md / system prompt patterns produce reliably better behavior, which overlaps with [[agents-md-research|AGENTS.md research]]. the meta-insight is that much of what people attribute to "bad AI" is actually addressable at the prompt and scaffolding layer without waiting for better base models.

related: [[context-window-optimizer|context window optimizer]], [[spec-driven-dev|spec-driven dev kit]], [[llm-physical-intuition|LLM physical intuition]]