The Yoneda embedding sends to the representable presheaf . On a morphism , it sends a map to , giving .
The Yoneda lemma states that for every categorical presheaf there is a bijection, natural in both variables,
Its inverse sends to the transformation . Apply it to to obtain
Under this bijection corresponds exactly to , so the map induced by the embedding on each hom-set is bijective. Therefore the Yoneda embedding is full and faithful.
The equations give , so equalizes and . If satisfies , put . Then . If is another such factorization, applying gives . This proves
In particular the inclusion in a retract in a category is a monomorphism.
By part (b), is the equaliser of and . Full faithfulness of the Yoneda embedding writes for a unique endomorphism . Choose an equaliser of and in .
For each , the maps are precisely the maps with . Thus is the pointwise equaliser of and , hence their equaliser in the presheaf category. Both and have the same universal property, so their vertices are isomorphic. This proves the representable retract criterion:
Use 0-presentable object in the specified sense: its hom-functor preserves all small colimits. For a representable , the Yoneda lemma identifies this hom-functor with evaluation at . Given any small diagram , pointwise colimits give
These are the canonical comparison bijections, including for an empty diagram. Therefore every representable presheaf is 0-presentable, or a small projective object in this sense.
By the canonical colimit presentation of a presheaf, write
with structure maps . Since is a 0-presentable object, the canonical map
is bijective. Every element of a set-valued colimit has a representative in some component. In particular is the image of a transformation for some , so
Thus is a retract of . The representable retract criterion from part (c), using the assumed equalisers in , now gives is representable. Only existence of the representative of is needed; no stronger uniqueness-of-factorization assertion about individual colimit injections has been assumed.

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