Hvad er avancerede promptingTeknikker?

Question

Accepted Answer

Beyond basic prompting, **advanced techniques** — few-shot, chain-of-thought, structured output, system prompts, and others — significantly improve LLM results for complex tasks. Understanding them helps get the most out of LLMs.

## Vigtigste avancerede teknikker

```text
✓ FEW-SHOT → provide EXAMPLES of input/output in the prompt → the model follows the pattern
  (great for specific formats/behaviors); ZERO-SHOT = no examples (just instructions)
✓ CHAIN-OF-THOUGHT (CoT) → ask the model to REASON step by step ('think step by step') →
  improves complex reasoning/math (shows its work → more accurate)
✓ STRUCTURED OUTPUT → ask for a specific format (JSON, etc.) → reliable parsing for app
  integration (often with schemas/tools)
✓ SYSTEM PROMPTS → set overall behavior/role/rules (the model's persistent instructions)
✓ ROLE/persona → 'You are an expert X' → frames responses
```

## Flere teknikker

```text
✓ DECOMPOSITION → break complex tasks into smaller prompts/steps (prompt chaining)
✓ SELF-CONSISTENCY → generate multiple answers, take the majority (for reliability)
✓ Provide CONTEXT/data in the prompt (grounding); reference material (RAG)
✓ CONSTRAINTS → specify what to do AND NOT do; format/length/tone requirements
✓ ITERATE and refine; test prompts systematically
```

## Hvorfor det betyder noget

Forståelse af avancerede prompting-teknikker er værdifuld viden på seniorniveau, fordi de **significantly improve LLM results** for complex tasks, så de er vigtige for effektiv brug af LLMs (en stadig mere værdifuld kompetence).

Udenfor basisk prompting forbedrer avancerede teknikker væsentligt det, du kan få fra LLMs.

Forståelse af **vigtigste teknikker** — **few-shot prompting** (providing examples så modellen følger mønsteret, perfect for specific formats and behaviors), **chain-of-thought** (asking the model to reason step by step, significantly improving complex reasoning and math by having it show its work), **structured output** (asking for specific formats like JSON for reliable parsing and app integration), **system prompts** (setting overall behavior and rules), og roles/personas — giver kraftfulde værktøjer til at få bedre, mere pålidelig og mere brugbar LLM output.

Chain-of-thought og few-shot er især meget effektive teknikker, der markant forbedrer resultaterne for komplekse og specifikke opgaver.

Forståelse af **flere teknikker** — decomposition (breaking complex tasks into smaller prompts via prompt chaining), self-consistency (generating multiple answers and taking the majority for reliability), providing context and grounding (RAG), constraints (specifying what to do and not do), og iterating and testing prompts systematically — udvider værktøjskassen for sofistikeret prompting.

Disse avancerede teknikker er hvad der adskiller effektiv LLM-brug fra basisk prompting, hvilket muliggør pålidelig håndtering af komplekse opgaver, bedre ræsonnement og ren integration i applikationer (structured output).

Som LLMs bruges til stadig mere sofistikerede opgaver, er det at beherske avanceret prompting (chain-of-thought, few-shot, structured output, decomposition) en stadig mere værdifuld kompetence for udvikler og power users.

Da avancerede prompting-teknikker (few-shot, chain-of-thought, structured output, system prompts, decomposition) significantly improve LLM results for complex tasks og er vigtige for effektiv LLM-brug, og da dette er en stadig mere værdifuld kompetence, er forståelsen af avancerede prompting-teknikker værdifuld, stadig mere relevant viden på seniorniveau inden for AI — significantly improving LLM results for complex tasks (via chain-of-thought reasoning, few-shot examples, structured output, og decomposition), vigtig for effektiv LLM-brug, og en stadig mere værdifuld kompetence, som LLMs anvendes til sofistikerede opgaver.