Master LLM Prompts for Peak Performance

Prompt Engineering Patterns covers five core techniques for maximizing LLM performance and reliability. Few-shot learning teaches by example (2-5 input-output pairs) rather than explanation, useful for consistent formatting or edge-case handling, trading more tokens for better accuracy. Chain-of-thought prompting requests step-by-step reasoning before the final answer - either zero-shot ("Let's think step by step") or via example reasoning traces - reported to improve accuracy on analytical tasks by 30-50%. Prompt optimization is systematic: start simple, measure accuracy/consistency/token usage, then iterate through versions (e.g. "Summarize this article" → "Summarize in 3 bullet points" → "Identify the 3 main findings, then summarize each"), using A/B testing on diverse inputs including edge cases.

Use template systems for reusable prompt structures with variables and conditional sections when the same pattern applies across multiple inputs - reducing duplication in multi-turn or role-based interactions:

### Reusable code review template
template = """
Review this {language} code for {focus_area}.

Code:
{code_block}

Provide feedback on:
{checklist}
"""

### Usage
prompt = template.format(
    language="Python",
    focus_area="security vulnerabilities",
    code_block=user_code,
    checklist="1. SQL injection\n2. XSS risks\n3. Authentication"
)

System prompt design sets global behavior (role, expertise level, output format, safety guidelines) that persists across a conversation, keeping stable instructions out of per-turn user messages.

Progressive disclosure builds prompt complexity in four levels only as needed: direct instruction, then added constraints, then added reasoning steps, then added examples - rather than starting complex. The overall instruction hierarchy follows [System Context] → [Task Instruction] → [Examples] → [Input Data] → [Output Format]. Error recovery means building prompts that degrade gracefully: fallback instructions, requested confidence scores, alternative interpretations when uncertain, and explicit handling for missing information.

Seven best practices anchor the approach: be specific (vague prompts produce inconsistent results), show rather than tell (examples beat descriptions), test extensively on diverse inputs, iterate rapidly since small changes have large impacts, monitor production performance, version-control prompts like code, and document why a prompt is structured the way it is. Five common pitfalls to avoid: over-engineering before trying simple prompts, example pollution (examples that don't match the target task), context overflow from excessive examples, ambiguous instructions open to multiple interpretations, and skipping edge-case testing.

Developers and prompt engineers building production LLM prompts who need concrete techniques - few-shot examples, chain-of-thought, systematic optimization, reusable templates, and system prompt design - plus a discipline for iterating and testing rather than guessing at what works.