Knowledge engineering (KE) is an engineering discipline, part of the Artificial Intelligence, whose purpose is to design and develope expert systems (or knowledge-based systems). That involves extracting the knowledge of human experts in a particular area, and encode that knowledge, so that it can be processed by a system.

The main problem is that knowledge engineering is not an expert in field that attempts to model, while the expert on the subject has no
knowledge modeling experience (based on heuristics) in a way that can be represented generically in a system.

An expert system is software that attempts to provide an answer to a problem, or clarify uncertainties where normally one or more human experts would need to be consulted.
Expert systems are most common in a specific problem domain, and is a traditional application and/or subfield of artificial intelligence. A wide variety of methods can be used to simulate the performance of the expert however common to most or all are:

1. The creation of a knowledge base which uses some knowledge representation formalism to capture the Subject Matter Expert’s (SME) knowledge

2. A process of gathering that knowledge from the SME and codifying it according to the formalism, which is called knowledge engineering. Expert systems may or may not have learning components but a third common element is that once the system is developed it is proven by being placed in the same real world problem solving situation as the human SME, typically as an aid to human workers or a supplement to some information system.