What does a Prompt Engineer do?
A Prompt Engineer designs, tests, and maintains the instructions, context, and tooling that guide Large Language Models (LLMs) to produce reliable, useful outputs. You translate business goals into structured prompts, evaluate quality, reduce hallucinations, and ship prompt-driven features to production.
Day-to-day responsibilities and deliverables
- Turn product requirements into prompt specs with clear inputs/outputs and acceptance criteria.
- Design prompt patterns (role, examples, constraints) and iterate based on evaluation metrics.
- Build and maintain small evaluation sets and automated checks for quality, safety, and regressions.
- Integrate prompts with tools/APIs (function calling, retrieval) to ground answers and take actions.
- Track costs, latency, and usage; tune prompts and settings (temperature, max tokens) for performance.
- Document prompt versions, decisions, and known limitations.
Typical deliverables
- Prompt specs with rationale and example I/O pairs
- Evaluation reports (accuracy, pass@k, error taxonomy)
- Safety guardrails and red-team notes
- Prompt version history with changelog
- Templates and components for reuse (e.g., system messages, rubrics)
Hiring expectations by level
Junior
- Can follow established prompt templates and run structured experiments.
- Writes clear examples and basic rubrics; flags safety issues.
- Needs guidance on evaluation design and trade-offs.
Mid-level
- Designs prompts end-to-end with patterns like ReAct and chain-of-thought (when appropriate).
- Builds small eval harnesses, tracks metrics, reduces cost/latency via prompt and settings.
- Collaborates cross-functionally; documents and version-controls prompts.
Senior
- Owns prompt strategy for a product surface; defines evaluation methodology and guardrails.
- Leads domain adaptation (retrieval, style guides), scales prompt components across teams.
- Mentors others; balances safety, UX, and business impact.
Salary ranges
- Junior: ~$60k–$100k USD
- Mid: ~$90k–$150k USD
- Senior: ~$140k–$220k+ USD
Varies by country/company; treat as rough ranges.
Where you can work
- Industries: SaaS, consumer apps, healthcare, finance, e-commerce, education, support, gaming, media.
- Teams: Product, Data/ML, UX Research/Content, Customer Support Automation, Developer Tools.
- Common titles: Prompt Engineer, LLM Engineer, AI UX Writer, Applied LLM Engineer, ML Product Engineer.
Who this is for
- Builders who enjoy structured writing plus data-driven iteration.
- Product-minded folks who can define acceptance criteria and measure outcomes.
- People comfortable with some scripting and working with APIs.
Prerequisites
- Comfort writing clear, concise instructions.
- Basic understanding of how LLMs work (tokens, context window, temperature).
- Optional but helpful: Python or JavaScript basics, JSON, HTTP APIs.
Learning path
- Foundations. Learn roles, tokens, temperature, few-shot examples. Mini task: write a concise system message and two example pairs.
- Patterns. Practice structures like step-by-step, ReAct, and rubrics. Mini task: convert a vague prompt into a constrained template with evaluation rubric.
- Evaluation & iteration. Build a small golden set; run A/B prompt tests. Mini task: design 10 test cases with acceptance criteria.
- Domain adaptation. Ground with retrieval or docs; add style guides. Mini task: extract a style guide from 5 samples and turn it into a checklist.
- Safety & reliability. Add guardrails and tests for jailbreaks, PII, bias. Mini task: write three targeted red-team probes and expected denials.
- Tooling & deployment. Use function calling, caching, versioning, and cost tracking. Mini task: create a simple prompt changelog entry with measured impact.
Portfolio projects (show outcomes)
- Support reply assistant: turn tickets into empathetic, policy-compliant responses. Outcome: 30–50% time saved, <10% escalation.
- Product spec summarizer: convert long briefs into action items and risks. Outcome: reduce reading time by 40% with <5% critical-miss rate.
- Data-augmented Q&A: retrieval over help center or PDFs. Outcome: 60%+ correct grounded answers; cite sources.
- Content style enforcer: enforce voice guidelines for marketing copy. Outcome: 90%+ rubric pass rate on tone and claims.
- Form-to-API agent: extract structured JSON and call functions. Outcome: 95%+ schema validity, cost per request under target.
How to present your projects
- Include prompt versions, evaluation sets, and pre/post metrics.
- Show error taxonomy and how you fixed top issues.
- Add safety notes: blocked jailbreaks, PII handling, refusals.
Practical projects (hands-on)
- Design a rubric: write a 5-criterion rubric for code review or email tone; test on 10 examples.
- Hallucination hunt: create a 20-item test set with known answers; measure grounded accuracy before/after retrieval.
- Cost/latency tuning: compare temperature, max tokens, and system message variants; chart quality vs cost.
- Guardrail bakeoff: try three prompt-level safety patterns; log red-team prompts and results.
Mini tasks you can do in 30 minutes
- Rewrite a vague prompt into a structured template with delimiters and constraints.
- Add 3 few-shot examples to improve format fidelity; measure error rate drop.
- Create a prompt changelog entry: hypothesis, change, metric, result.
Skill map for Prompt Engineer
- Prompt Engineering Foundations: roles, instructions, tokens, context windows, temperature.
- Prompt Patterns and Techniques: step-by-step, few-shot, ReAct, tree-of-thought, critique-and-revise.
- Evaluation and Iteration: golden sets, rubrics, A/B tests, regression checks, error taxonomy.
- Domain Adaptation and Knowledge: retrieval, citations, style guides, schema constraints.
- Safety and Reliability: refusals, sensitive topics, jailbreak resistance, PII masking, content filters.
- Tooling and Deployment: function calling, caching, versioning, cost/latency tracking, observability.
How these skills connect
Start with Foundations and Patterns to ship a working prototype. Add Evaluation to make it reliable. Use Domain Adaptation to ground answers. Layer Safety to avoid harm. Finally, Tooling & Deployment turns your prompts into production features.
Interview preparation checklist
- Explain how temperature, top-p, and max tokens affect outputs.
- Show a prompt spec with inputs, outputs, constraints, and acceptance criteria.
- Walk through an error taxonomy and how you prioritized fixes.
- Discuss patterns you tried (and why you kept or dropped them).
- Demonstrate an eval harness and a small golden set.
- Describe safety guardrails and red-team strategy.
- Show cost/latency trade-offs and versioning discipline.
Practice questions
- When would you use ReAct vs simple step-by-step?
- How do you reduce hallucinations without over-constraining the model?
- How do you measure quality for subjective tasks like tone?
Common mistakes and how to avoid them
- Vague instructions. Fix: use explicit roles, constraints, and delimiters.
- Too few examples. Fix: add diverse few-shot pairs that match edge cases.
- No evaluation. Fix: build a small golden set and automate checks early.
- Ignoring safety. Fix: add refusal criteria, test jailbreaks, and mask sensitive data.
- No versioning. Fix: keep a prompt changelog with metrics and rollback plan.
- Overfitting to the test set. Fix: rotate fresh examples and hold out a blind set.
- Cost surprises. Fix: estimate token budgets, monitor, and cache stable outputs.
Next steps
- Take the fit test on this page to confirm your match.
- Start a small portfolio project and iterate with a golden set.
- Then tackle Tooling & Deployment basics to ship something real.
Pick a skill to start — see the Skills section above.