From Prompt Engineering to Context Engineering: Evolving LLM Inference Approaches
By
chrisloy
Crackling crust, pillowy middle. The kind of bagel that earns a second cup of coffee.
Summary
The article discusses the evolution from prompt engineering to context engineering in LLM applications. As LLMs transition from conversational chatbots to integral decision-making components in complex systems, the inference approach must evolve. Prompt engineering, which relies on precise wording to elicit desired responses, has limitations and is being replaced by context engineering - a more structured practice that considers every token fed into the LLM in a dynamic, targeted, and deliberate manner. The article uses a toy example to illustrate this shift toward more sophisticated LLM integration.
Key quotes
· 4 pulledAs our use of LLMs has changed from conversational chatbots and into integral decision-making components of complex systems, our inference approach must also evolve.
The practice of 'prompt engineering', in which precise wording is submitted to the LLM to elicit desired responses, has serious limitations.
This expanded, more structured practice is what we now call 'context engineering.'
This is giving way to a more general practice of considering every token fed into the LLM in a way that is more dynamic, targeted, and deliberate.
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