diff --git a/spacy/errors.py b/spacy/errors.py
index bfdf71652..4af8b756c 100644
--- a/spacy/errors.py
+++ b/spacy/errors.py
@@ -409,6 +409,10 @@ class Errors(object):
     E144 = ("Could not find parameter `{param}` when building the entity linker model.")
     E145 = ("Error reading `{param}` from input file.")
     E146 = ("Could not access `{path}`.")
+    E147 = ("Unexpected error in the {method} functionality of the EntityLinker: {msg}. "
+            "This is likely a bug in spaCy, so feel free to open an issue.")
+    E148 = ("Expected {ents} KB identifiers but got {ids}. Make sure that each entity in `doc.ents` "
+            "is assigned to a KB identifier.")
 
 
 @add_codes
diff --git a/spacy/pipeline/pipes.pyx b/spacy/pipeline/pipes.pyx
index f5cc58411..63efc3f49 100644
--- a/spacy/pipeline/pipes.pyx
+++ b/spacy/pipeline/pipes.pyx
@@ -1160,7 +1160,9 @@ class EntityLinker(Pipe):
                     prior_prob = self.kb.get_prior_prob(kb_id, mention)
 
                     gold_ent = ents_by_offset["{}_{}".format(start, end)]
-                    assert gold_ent is not None
+                    if gold_ent is None:
+                        raise RuntimeError(Errors.E147.format(method="update", msg="gold entity not found"))
+
                     type_vector = [0 for i in range(len(type_to_int))]
                     if len(type_to_int) > 0:
                         type_vector[type_to_int[gold_ent.label_]] = 1
@@ -1176,7 +1178,8 @@ class EntityLinker(Pipe):
                         priors.append([0])
 
         if len(entity_encodings) > 0:
-            assert len(priors) == len(entity_encodings) == len(context_docs) == len(type_vectors)
+            if not (len(priors) == len(entity_encodings) == len(context_docs) == len(type_vectors)):
+                raise RuntimeError(Errors.E147.format(method="update", msg="vector lengths not equal"))
 
             entity_encodings = self.model.ops.asarray(entity_encodings, dtype="float32")
 
@@ -1204,7 +1207,8 @@ class EntityLinker(Pipe):
                     cats.append([value])
 
         cats = self.model.ops.asarray(cats, dtype="float32")
-        assert len(scores) == len(cats)
+        if len(scores) != len(cats):
+            raise RuntimeError(Errors.E147.format(method="get_loss", msg="gold entities do not match up"))
 
         d_scores = (scores - cats)
         loss = (d_scores ** 2).sum()
@@ -1267,7 +1271,9 @@ class EntityLinker(Pipe):
 
                         if self.cfg.get("context_weight", 1) > 0:
                             entity_encodings = xp.asarray([c.entity_vector for c in candidates])
-                            assert len(entity_encodings) == len(prior_probs)
+                            if len(entity_encodings) != len(prior_probs):
+                                raise RuntimeError(Errors.E147.format(method="predict", msg="vectors not of equal length"))
+
                             mention_encodings = [list(context_encoding) + list(entity_encodings[i])
                                                  + list(prior_probs[i]) + type_vector
                                                  for i in range(len(entity_encodings))]
@@ -1279,13 +1285,15 @@ class EntityLinker(Pipe):
                         final_kb_ids.append(best_candidate.entity_)
                         final_tensors.append(context_encoding)
 
-        assert len(final_tensors) == len(final_kb_ids) == entity_count
+        if not (len(final_tensors) == len(final_kb_ids) == entity_count):
+            raise RuntimeError(Errors.E147.format(method="predict", msg="result variables not of equal length"))
 
         return final_kb_ids, final_tensors
 
     def set_annotations(self, docs, kb_ids, tensors=None):
         count_ents = len([ent for doc in docs for ent in doc.ents])
-        assert count_ents == len(kb_ids)
+        if count_ents != len(kb_ids):
+            raise ValueError(Errors.E148.format(ents=count_ents, ids=len(kb_ids)))
 
         i=0
         for doc in docs: