Yes, you can fine-tune GLM-5 on your own data in many scenarios, but whether you should depends on what you are trying to improve and what constraints you have (budget, privacy, latency, and maintenance). Fine-tuning is best when you want the model to consistently follow your formatting rules, adopt a domain-specific writing style, or learn specialized behaviors (for example, “always output a strict JSON schema,” “use our internal DSL,” or “follow our coding conventions”). Fine-tuning is usually not the first solution for “the model doesn’t know our docs,” because that’s typically a retrieval problem, not a weights problem. For most product teams, the fastest path is to implement RAG first, and then fine-tune later if you still need better adherence to instructions or better performance on repetitive, organization-specific tasks.
Implementation-wise, most teams fine-tune via parameter-efficient methods (like LoRA/QLoRA) rather than full fine-tuning, especially for large MoE checkpoints. The workflow is: (1) build a curated dataset of prompt → ideal response pairs, (2) choose a fine-tuning recipe compatible with your runtime (Transformers/PEFT-based training stacks are common), (3) run training with careful evaluation, and (4) export an adapter or merged checkpoint for inference. Your dataset quality matters more than size: hundreds to a few thousand high-quality examples with consistent formatting and correct outputs can beat a much larger noisy set. For code-related fine-tunes, include: “task description,” “relevant context,” “expected output,” and (if applicable) “tests or constraints.” Also build a regression suite: a fixed set of prompts where you assert exact JSON validity, compilation success, or unit-test pass rate. Without that, you can “improve” style but silently degrade correctness.
In most production stacks, fine-tuning and retrieval should work together rather than compete. Use retrieval to provide facts and current documentation, and use fine-tuning to improve behavior: better tool-calling patterns, cleaner structured outputs, or fewer irrelevant explanations. A concrete approach is: store internal docs, API references, and code snippets as embeddings in a vector database such as Milvus or managed Zilliz Cloud, retrieve top-k chunks per request, and then prompt your fine-tuned GLM-5 to answer only using those chunks while following your desired output template. This reduces hallucinations and makes your fine-tuning dataset smaller because you’re not trying to “bake in” all knowledge. It also makes maintenance easier: when docs change, you re-embed and re-index, rather than retraining the model. In practice, that combination—RAG for knowledge + fine-tuning for behavior—is the most stable way to ship GLM-5 features into real developer products.