Just-in-Time (JIT) compilation is a technique used by some programming languages and runtime environments to improve the performance of interpreted code. It combines the benefits of both interpretation and compilation by dynamically translating and optimizing parts of the code during runtime.

In traditional interpretation, the source code is executed line by line, with each line being analyzed and executed in sequence. This approach can be relatively slower compared to compiled languages because the interpreter needs to perform the analysis and execution steps repeatedly.

JIT compilation addresses this performance issue by introducing a dynamic compilation step. Instead of interpreting the entire program at once, the JIT compiler selectively identifies frequently executed or performance-critical portions of the code, such as loops or hot paths, and compiles them into machine code or a more efficient intermediate representation.

The key steps in JIT compilation are as follows:

1. Interpretation: The source code is initially executed by the interpreter, following the regular interpretation process.
2. Profiling: As the code is being executed, the JIT compiler gathers information about the runtime behavior, such as which portions of the code are executed most frequently (hot spots) and which types of data are commonly used (type information). This profiling information helps the JIT compiler make informed optimization decisions.
3. Compilation: The JIT compiler selects the identified hot spots or performance-critical sections and compiles them into machine code or an optimized intermediate representation. This compiled code is typically stored in a cache for future use.
4. Replacement: Once the hot spots are compiled, the interpreter replaces the corresponding interpreted code with the compiled code. Subsequent executions of the same code will bypass interpretation and directly execute the compiled code, resulting in improved performance.
5. Deoptimization: If the assumptions made during compilation are no longer valid, for example, due to changes in the runtime behavior or unexpected conditions, the compiled code may need to be deoptimized. Deoptimization involves reverting the execution back to interpretation and recompiling the code with new optimizations based on the updated profiling information.

JIT compilation provides a balance between the flexibility and interactivity of interpretation and the performance benefits of ahead-of-time compilation. It allows dynamically typed languages like JavaScript, Python, and Java to achieve competitive performance levels, especially in scenarios where code execution patterns can change dynamically.