The new skill in AI is not prompting, it's context engineering (philschmid.de)

Article summary

The article discusses the concept of context engineering in AI, which involves providing the right information and tools to a large language model (LLM) to accomplish a task. This approach is seen as a key factor in building effective AI agents, as it allows them to understand the context and make informed decisions. The article highlights the importance of context engineering in overcoming the limitations of traditional prompt engineering. By providing a rich context, AI agents can produce more accurate and helpful responses.

Main themes

• context engineering
• AI agents
• large language models
• prompt engineering
• human-AI comparison
• testing and evaluation
• cost and feasibility
• tool integration

What commenters say

• Some commenters argue that context engineering is similar to human problem-solving, where having the right information at the right time is crucial for success.
• Others disagree, stating that comparing AI to human cognition is unhelpful and inaccurate.
• The importance of providing the right amount and type of context is debated, with some arguing that too much context can be harmful.
• There is a need for systematic testing of context and behavior in AI agents to identify and fix failures.
• The concept of context engineering is seen as a key factor in building reliable and effective AI agents.
• Some researchers are working on developing new techniques, such as using millions of different tools and stable long contexts, to improve AI performance.
• The cost and feasibility of using large language models with long contexts are concerns, with some arguing that it may not be viable due to token-based pricing.
• The use of smart routers to selectively append relevant tools and descriptions to the context is proposed as a potential solution to these challenges.