When you start working with models, you often want to "tweak them to your needs" - so they understand the project context, speak in the right tone, and solve specific tasks better. The first thing that comes to mind is to fine-tune the model. But this is not always the best (and certainly not the first) step.
🍭 Fine-tuning is powerful. But it is also very expensive: you need data, computational resources, and time. Therefore, people usually resort to it when all other methods have been tried.
Here is what is definitely worth trying first 👇
- Few-shot prompting - when you show the model a few examples of correct answers directly in the prompt. This helps guide its style and logic without additional training.
- RAG (Retrieval-Augmented Generation) - an approach where the model is "enriched" with necessary knowledge from external sources (e.g., a document database or wiki).
- SGR (Schema-Guided Reasoning) - a relatively new approach: structuring model responses to make their format more deterministic and predictable.
💡 Sometimes even a simple adjustment of temperature (the "creativity" parameter) solves half the problems:
- Lower temperature → more accurate and stable responses
- Higher → more variability and creativity
If you want to experiment with fine-tuning, you can take lightweight models (e.g., Qwen-8B or Mistral) and train them on narrow tasks. A large number of ready-made datasets can be found on HuggingFace.
But most often, it is enough to simply work with prompts, context, and hyperparameters.
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