Prompt Engineering

Prompt engineering is the practice of systematically crafting input strings or structured messages to elicit desired behavior from large language or multimodal models. Because modern transformer models are trained on next-token prediction, the framing, ordering, and formatting of the prompt profoundly influence output quality, safety, and latency.

Definition and Scope

Prompt engineering covers single-turn instructions, multi-turn conversation design, system messages, few-shot demonstrations, chain-of-thought scaffolds, and tool invocation markers (function-calling JSON). It interfaces with grounding techniques like Retrieval-Augmented Generation and safety mitigations such as system-level guardrails.

Prompt Elements

Component	Purpose	Example
System message	Sets global persona and constraints	"You are an executive summary bot"
User message	Task description	"Summarize the following PDF"
Few-shot demo	Shows desired IO mapping	Q: "Translate to French" -> A: "Bonjour"
Tool call schema	Enables structured JSON response	function="search", "event"
Delimiters	Separate sections, prevent injection	"```json" or XML tags

Patterns and Templates

Instruction-Answer (IA)

A single instruction followed by a blank line. Simple and low latency.

Few-Shot Chain of Thought (CoT)

Demonstrations include rationales; improves reasoning on arithmetic and symbolic tasks¹.

Self-Consistency

Generate multiple CoT samples and vote for majority answer².

ReACT

Interleave reasoning and actions (tool calls, retrieval) within a single prompt to solve multi-step queries³.

Performance Metrics

Metric	Proxy	How to Measure
Task accuracy	Passing rate on eval set	LLaMA Bench, HELM
Tokens per dollar	Prompt length + output length	Model pricing sheet
Latency	Server round-trip	Stopwatch, monitoring agent
Jailbreak rate	Adversarial test set	OpenAI Red Team eval

Design Trade-offs

Brevity vs. Context: Short prompts cut latency but may under-specify the task.
Demonstration Count: More few-shot examples increase accuracy up to a plateau around 32 shots for GPT-3.5⁴.
Determinism: Setting temperature=0 yields repeatable outputs but can lower creativity.

Current Trends (2025)

Programmatic Prompt Builders: LangChain and LlamaIndex expose prompt templates with parameter substitution.
Automatic Prompt Search (APS): reinforcement search finds high-scoring prompts with zero human effort.
Multi-modal Prompts: GPT-4o accepts image regions and audio transcripts inline.

Implementation Tips

Test prompts on small models first; transfer often works but costs less.
Wrap user content in triple-backticks to avoid prompt injection.
Use explicit JSON schema to guarantee parseable outputs.
Log prompts and completions for continual refinement.

Wei et al., Chain of Thought Prompting Elicits Reasoning in Large Language Models, 2022. ↩
Wang et al., Self-Consistency Improves Chain of Thought Reasoning in Large Language Models, 2023. ↩
Yao et al., ReACT: Synergizing Reasoning and Acting in Language Models, 2023. ↩
Liu et al., Lost in the Middle: How Language Models Use Long Contexts, 2023. ↩

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