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 how to keep up after this series)
The full catalog of local-LLM failures: OOM, slow tok/s, garbage output, instruction drift, bad RAG hits, tool-call hallucination. Plus where to follow the field once you're on your own.
- Fine-tuning a model locally
When fine-tuning is actually the right call (it usually isn't) and how to pull off a LoRA run on a 16GB Mac, with a worked Llama 3.2 3B example.
- Picking a local model by task
The 2026 open leaders, sorted by what you actually want to do: coding, chat, the small-model crowd, structured output, vision, embeddings, and audio.
- The local-LLM vocabulary
Parameters, B, dense vs MoE, base vs instruct, tokens, context windows, chat templates, GGUF, and quant suffixes. Read it once and any HuggingFace model card stops being scary.
- LLM API bills, and why a token costs what it costs
How input and output tokens get priced, why output runs 5-6x more, and how prompt caching cuts the input bill by 10x. Plus the hidden costs that ambush 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.