System Prompt
NLP/dictionary/system-prompt
Definition
A special instruction at the start of an LLM conversation that sets role, behaviour, format, and constraints. Most "the model isnt doing what I want" problems are solved here, before reaching for RAG or fine-tuning.
Posts that use this term
- Troubleshooting local LLMs and keeping up
The catalog of common local-LLM failures: OOM, slow tok/s, garbage output, instruction drift, RAG miss, tool-call hallucination. Plus where to follow the field as it moves.
- Fine-tuning a model locally
When fine-tuning is the right answer (rarely) and how to do it on consumer hardware: LoRA, QLoRA, MLX-LM, Unsloth. A worked example fine-tuning Llama 3.2 3B on a 16GB Mac.
- Picking a local model by task
The 2026 open leaders by task: coding (Qwen 2.5 Coder, DeepSeek-Coder), chat (Llama, Qwen, Mistral), small-model renaissance (Phi-3, Gemma 2), structured output, multimodal, embeddings.
- The local-LLM vocabulary
Parameters, B, dense vs MoE, base vs instruct, tokens, context window, chat template, GGUF, quantization suffixes. After this post you can read any HuggingFace model card.
- LLM APIs and the economics of tokens
How input vs output tokens are priced, why output is 5-6x more, what prompt caching saves you (10x), and the hidden costs (tokenizer drift, reasoning tokens, tool-call loops) that surprise people.
- Prompt, RAG, fine-tune: three ways to shape a model
Three levers for shaping what an LLM does: prompting (ask better), RAG (give it the right context), fine-tuning (change the weights). What each costs, what each fixes, and how to pick.
- The context window, and why models hallucinate
An LLM only sees a fixed-size slice of text at a time. When it doesn't know something, it predicts anyway — that's a hallucination, not a bug.