Tensoring preserves cokernels, while the lost injectivity is measured by Tor. Keeping the exact identity in view prevents the geometric or probabilistic intuition from drifting.
Definitions first
An \(R\)-module generalises both vector spaces and abelian groups. A map \(f:M\to N\) is understood through its kernel, image, and cokernel, which exact sequences place on one line.
The definition determines which expressions are legal; only then does the identity become meaningful. An equality in \(\mathcal A\) may change ambient meaning, so I keep \(\mathsf D\) separate from \(\mathsf C\).
A small case in full
The middle display is intentionally dense: it is where signs, bounds, multiplicities, or normalising factors are most likely to be lost.
The reusable statement
The two-row display is also a debugging tool: if the conclusion changes when only notation changes, some hidden choice has entered the argument.
A nearby false statement
Tensor product is right exact but not generally left exact, while \(\operatorname{Hom}_R(P,-)\) is exact precisely when \(P\) is projective.
This is enough machinery for one note: an exact object, a worked case, a structural law, and a clearly marked boundary. Each layer can now be tested independently.