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. 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.