# #selection — MDRSS hashtag feed

> Public MDRSS cards tagged #selection.
> Canonical feed: https://mdrss.com/feeds/selection

## Cards (3)

### [Fine-Tuning Method Selection](https://mdrss.com/llm-engineering/training-and-fine-tuning/901366/901366.md)

This is the router skill for the fine-tuning lifecycle: it decides whether fine-tuning is the right tool at all, and if so, which method and which base-model size class. Every other skill in this plugin assumes this routing already happened — start here before opening lora-qlora. Use it to give an agent explicit responsibilities, steps and constraints.

Classification: llm-engineering/training-and-fine-tuning · Feed: llm-engineering · Updated: 2026-08-04T13:54:51.641Z · Version: 1

### [Dataset Curation](https://mdrss.com/llm-engineering/training-and-fine-tuning/901360/901360.md)

This skill assumes finetuning-method-selection already routed here — the next step is preparing data, not choosing a method. What follows: format selection by target method, the template/packing mechanics behind the most common silent training failures, rules for mixing in synt. Use it to give an agent explicit responsibilities, steps and constraints.

Classification: llm-engineering/training-and-fine-tuning · Feed: llm-engineering · Updated: 2026-08-04T13:54:51.641Z · Version: 1

### [Few-Shot Learning Guide](https://mdrss.com/ai-agents/agent-design-and-orchestration/901348/901348.md)

Few-shot learning enables LLMs to perform tasks by providing a small number of examples (typically 1-10) within the prompt. This technique is highly effective for tasks requiring specific formats, styles, or domain knowledge. Use it to give an agent explicit responsibilities, steps and constraints.

Classification: ai-agents/agent-design-and-orchestration · Feed: ai-agents · Updated: 2026-08-04T13:54:51.641Z · Version: 1