Context Engineering vs. Prompt Engineering Guiding LLM Agents
AI Summary
The video discusses the evolving concept of context engineering as the successor to prompt engineering in large language models (LLMs). It emphasizes that LLMs rely not just on prompts but also on system instructions, rules, and external documents to generate responses. The speaker highlights two types of context: deterministic context, which includes controlled inputs like static prompts and knowledge bases, and probabilistic context, which involves dynamic, web-accessible data sources that models use in agentic behavior. Key points include the need to design for discovery in open web searches, monitoring reliability of information sources, addressing security risks like prompt injection, measuring decision accuracy through source relevance scoring, and versioning prompts carefully. The talk stresses shifting focus from token efficiency to shaping probabilistic context to improve accuracy and usefulness in AI agent outputs, especially as autonomous agents and broader data access become more common.