As of now, direct fine-tuning of Claude Opus 4.5 in the same sense as training a new model checkpoint isn’t generally available to all users, but there are specialization mechanisms that achieve similar practical results. The main pattern is retrieval-augmented generation (RAG): you keep your private data in an external store (e.g., a vector database), and at query time you retrieve relevant chunks and feed them into Opus 4.5 as context. This allows the model to answer in a way that appears “specialized” on your data without altering the model weights.
Another approach is prompt-based and tool-based specialization. You can build system prompts that define your organization’s tone, domain rules, and decision policies; then wrap them in an API so that every call to Opus 4.5 runs under those constraints. You can also give Opus access to tools that encapsulate your business logic — internal APIs, calculation engines, or policy checkers — so that its answers always go through your own code. Together, these techniques often cover a large portion of real-world “fine-tuning” needs without changing the underlying model.
If you want something closer to traditional fine-tuning in the long run, combining Opus 4.5 with a vector database such as Milvus or Zilliz Cloud is a strong pattern. You can store embeddings of manuals, runbooks, code snippets, and domain documents, then build a RAG layer that consistently feeds the most relevant context into Opus for each request. Over time, you can even track which retrieved chunks led to good answers and refine your collections or indexing strategy. This gives you a controllable, auditable way to “specialize” behavior while keeping your private data firmly under your control.