Prompt engineering roles filter on precise vocabulary: model names, framework names, evaluation metrics, and orchestration libraries. Generic AI language fails ATS at the first pass — here is the complete keyword list for 2026.
Analyze My Prompt Engineer Resume (Free) →"ChatGPT" is a product name. Recruiters search for "GPT-4o," "Claude 3.5 Sonnet," and "OpenAI API" — none of which match "ChatGPT."
No metric, no framework, no model name. ATS systems cannot score this — and neither can recruiters doing manual review.
"Familiar with AI/ML tools" matches zero searches. LangChain, LlamaIndex, RAGAS, and Pinecone are the actual keywords.
"GPT-4o via OpenAI API," "Claude 3.5 Sonnet via Anthropic API," "Llama 3 8B on AWS Bedrock" — each is a separate ATS keyword.
"RAGAS faithfulness 0.87," "hallucination rate 3.2%," "context precision +22%" — numbers convert vague claims into verifiable outcomes.
"RAG (retrieval-augmented generation)" captures both forms that ATS systems treat as distinct strings.
72 keywords across 8 categories — the terms ATS systems scan for in prompt engineering job postings.
✓ This is for you if…
✗ This is NOT for you if…
Why a general AI assistant can't do what ZoeVera does
Real examples of how keyword gaps cost candidates interviews
Wrote prompts for AI chatbot to improve the quality of responses
Engineered chain-of-thought prompt system with 8 few-shot examples for GPT-4o customer support agent; implemented RAGAS evaluation harness measuring groundedness, faithfulness, and answer relevancy — reduced hallucination rate from 18% to 3.2% and cut average resolution time by 34%
Worked on RAG system to help users find information faster
Built RAG pipeline (LangChain + Pinecone) over enterprise knowledge base of 2M+ documents; tuned chunk size, overlap, and embedding model (text-embedding-3-large) achieving 0.87 RAGAS faithfulness score — 61% lower hallucination rate vs. base GPT-4 prompt, serving 4,000 daily queries at <400ms P95 latency
Tested AI model outputs to check accuracy and relevance
Designed automated LLM evaluation harness using RAGAS and GPT-4-as-judge across 5 dimensions (faithfulness, answer relevancy, context precision, context recall, answer correctness); ran nightly benchmark suite of 1,200 test cases — identified 3 prompt variants that reduced context precision drop-off by 22% across 4 model versions
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"GPT-4o via OpenAI API" and "Claude 3.5 Sonnet via Anthropic API" are ATS keywords. "ChatGPT" and "Claude AI" are consumer product names that match nothing recruiters search for.
ATS systems treat the abbreviation and full form as different strings. Write "RAG (retrieval-augmented generation)" on first mention to capture both variants in one line.
"RAGAS faithfulness score of 0.87" or "reduced hallucination rate from 18% to 3.2%." Evaluation claims without numbers score as generic ATS filler and are deprioritized in manual review.
"Pinecone," "Weaviate," "Chroma," and "pgvector" are distinct ATS strings. "Vector database" is a category label that matches nothing when a recruiter searches for a specific platform.
"Chain-of-thought," "few-shot prompting," and "self-consistency" each match separate job description terms. Do not collapse them into "advanced prompting techniques."
LangSmith, Weights & Biases, and Helicone appear in prompt engineering job descriptions at AI-native companies. Listing them signals production experience, not just prototype work.
Prompt engineering is a young role category with no standardized vocabulary yet — which means different companies use different terms for the same skills. One JD says "prompt design," another says "prompt crafting," a third says "system prompt engineering." ATS systems match the exact phrase in the job description.
Chain-of-thought, CoT, and "step-by-step reasoning" are three strings that describe the same technique. ATS treats each as a different keyword. Include all variants that appear in your target JDs.
LangChain vs LangGraph vs LangSmith are distinct products that ATS systems treat as separate strings. A resume that mentions "LangChain" does not automatically match a JD searching for "LangGraph."
GPT-3.5, GPT-4, and GPT-4o are treated as different keywords. A hiring manager at an AI-native company knows the difference — and so does the ATS searching for candidates with GPT-4o API experience.
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The most critical keyword categories for prompt engineer resumes are: prompting techniques (chain-of-thought, few-shot prompting, zero-shot prompting, system prompts, prompt chaining), LLM platforms (GPT-4, Claude, Gemini, Llama, OpenAI API, Anthropic API), RAG and context management (RAG, retrieval-augmented generation, LangChain, LlamaIndex, vector databases, embeddings, Pinecone, Weaviate), evaluation frameworks (RAGAS, LLM eval, hallucination detection, faithfulness, groundedness), and agent orchestration (LangGraph, AutoGen, function calling, tool use, multi-agent systems). Always include both abbreviated and expanded forms — "RAG (retrieval-augmented generation)" — because ATS systems treat them as different strings.
No — "ChatGPT experience" is a consumer-level descriptor that signals end-user familiarity, not engineering competence. ATS systems at companies hiring prompt engineers filter for API-level vocabulary: "OpenAI API," "GPT-4o," "GPT-4 Turbo," "Anthropic API," "Claude 3.5 Sonnet," "Azure OpenAI." Recruiters searching Greenhouse or Workday for prompt engineer candidates use platform API names, model names, and framework names — not "ChatGPT." Replace "ChatGPT experience" with the specific model and API surface you worked with.
Yes — model names are distinct ATS keywords. "GPT-4," "GPT-4o," "GPT-4 Turbo," "Claude 3.5 Sonnet," "Gemini 1.5 Pro," "Llama 3," and "Mistral" all appear in job descriptions as separate required or preferred terms. A prompt engineer resume that only says "large language models" matches none of them. List every model you have worked with at an API or inference level. If you have done fine-tuning or RLHF, also list the base model: "fine-tuned Llama 3 8B with QLoRA."
Evaluation vocabulary is what distinguishes senior prompt engineers from junior ones in ATS screening. Include: RAGAS (the most-searched evaluation framework), hallucination detection, faithfulness, groundedness, answer relevancy, context precision, context recall, LLM-as-judge, GPT-4-as-judge, BLEU, ROUGE, and human evaluation. Pair each with a concrete metric in a bullet point — "RAGAS faithfulness score of 0.87" or "reduced hallucination rate from 18% to 3.2% measured via RAGAS" — because evaluation claims without numbers are treated as generic ATS filler.
Tech companies and AI startups hiring prompt engineers most commonly use Greenhouse, Lever, and Ashby. Larger enterprises may use Workday. Greenhouse is the dominant ATS in Series B+ AI companies. All of these systems perform keyword matching against the job description, so exact platform and framework names — LangChain, LlamaIndex, Pinecone, RAGAS, LangSmith — must appear verbatim in your resume to surface in recruiter searches.
LangChain, RAG, vector databases, LLM APIs, and agent keywords
PyTorch, MLOps, LLMs, and model deployment keywords
Full-stack, system design, cloud, and API keywords
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