Prompt Engineering Patterns for Reliable AI Applications

Prompt engineering is the art and science of communicating with language models to produce reliable, useful outputs. In production applications, 'it usually works' isn't good enough — you need prompts that produce consistent, structured, accurate outputs across thousands of diverse inputs. These patterns are the distillation of our experience building production AI applications.

Pattern 1: Structured Output with Schema

For programmatic applications, you need structured output (JSON, XML) that your code can parse reliably. Provide the exact schema in your prompt, including field names, types, and examples. Use JSON mode or tool use/function calling when available — these constrain the model's output to valid structured formats.

const prompt = `Analyze the following customer review and extract structured data.

Return a JSON object with exactly these fields:
- sentiment: "positive" | "neutral" | "negative"
- topics: string[] (max 3 topics mentioned)
- urgency: "low" | "medium" | "high"
- actionRequired: boolean
- summary: string (one sentence, max 100 characters)

Review: "${review}"

Respond with only the JSON object, no other text.`;

Pattern 2: Chain of Thought (CoT)

For complex reasoning tasks, asking the model to 'think step by step' dramatically improves accuracy. The model's reasoning is visible in the output, which makes it auditable and debuggable. We use CoT for classification decisions, mathematical computations, code analysis, and any task requiring multi-step logic.

Pattern 3: Few-Shot Examples

Include 2-5 examples of input/output pairs in your prompt. This is more reliable than describing the desired behavior in words — the model generalizes from examples better than from abstract instructions. Choose examples that cover edge cases and common variations.

Pattern 4: Role and Constraints

Define the model's role, expertise, and constraints explicitly. 'You are a senior security engineer reviewing code for vulnerabilities' produces different (and better) output than 'Review this code.' Add explicit constraints: 'Only report issues you're confident about. For each issue, cite the specific line and explain the risk.'

Pattern 5: Guardrails and Validation

Never trust LLM output without validation. Parse structured outputs with a schema validator (Zod, Pydantic). Check that extracted entities exist in your database. Verify that generated code compiles. Implement retry logic with the validation error as feedback: 'Your previous output was invalid because: [error]. Please correct it.'

Prompt engineering is an iterative discipline. Start with a simple prompt, evaluate against diverse test cases, identify failure patterns, and refine. Keep a test suite of prompts and expected outputs — this is your regression test for AI behavior.