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These components play different roles in a retrieval or assistant system. Choosing an embedding model, a reranker, and a generator should be guided by the failure being addressed.

Key takeaways

• Distinguish embedding, reranking, and generation tasks.
• Diagnose the failing stage.
• Preserve permissions and source evidence.

Embeddings turn content into numerical representations for tasks such as semantic retrieval. Reranking reorders a supplied candidate set according to another relevance model. Generation produces an answer or other text. A failure in one stage cannot always be repaired by changing another. Evaluate the retrieval pipeline before attributing answer errors to the generator. Check whether relevant evidence entered the candidate set, whether it was ranked highly enough to be included, and whether the final answer used it correctly. Read the specific model’s documentation for input limits, languages, supported features, and deployment terms. Models within a family can differ, and direct API behavior may not match every third-party hosting configuration. Version the actual components used. Keep source permissions and provenance through the pipeline. A relevant passage may still be unauthorized or outdated. Test unanswerable queries, exact identifiers, long documents, and language-specific cases. Measure final task success and cost alongside individual model scores.

1. Imagine an answer requires a policy document absent from the initial 20 candidates.
2. Changing the reranker cannot promote that missing document. Investigate indexing, query representation, filters, and initial retrieval first.
3. Once the document appears among candidates, test whether ranking and generation use it appropriately.

The constructed example separates candidate coverage from ranking quality.