How scaffolding extends LLM capabilities without changing their architecture

Scaffolding has emerged as a critical approach to enhancing large language model (LLM) capabilities without modifying their internal architecture. This methodology allows developers to build external systems that significantly expand what LLMs can accomplish, from using tools to reducing errors, while simultaneously creating new opportunities for safety evaluation and interpretability research.

The big picture: Scaffolding refers to code structures built around LLMs that augment their abilities without altering their internal workings like fine-tuning or activation steering would.

Why this matters: Understanding scaffolding is crucial for safety evaluations because once deployed, users inevitably attempt to enhance LLM power through external systems, potentially revealing latent capabilities that might not be detected in standard prompting tests.

• These latent capabilities are particularly relevant when assessing potential risks of advanced AI systems.

Key capabilities: Scaffolding systems allow LLMs to perform functions beyond their baseline abilities, including using tools, searching for information, and reducing error rates.

• Another significant benefit is improved interpretability, as scaffolded reasoning happens via visible text or code rather than within the LLM’s opaque neural networks.
• This visibility provides researchers with greater insight into how an AI system reaches its conclusions.

Common implementations: The scaffolding ecosystem includes various approaches of varying complexity, from simple prompt templates to sophisticated multi-agent systems.

• Retrieval Augmented Generation (RAG), search engine integration, agent scaffolds, function calling, and “bureaucracies” of LLMs all represent different scaffolding methodologies.
• Simple scaffolds capture most practical value for typical applications, though specialized implementations have enabled impressive capabilities in narrow domains.

Looking ahead: Despite slower progress than initially anticipated, researchers continue exploring scaffolding as a potential alternative path toward artificial general intelligence.

• The ultimate limits of what scaffolding can achieve remain unknown and represent an open question in AI research.