A GenAI point on your resume is believable when it says five things: who used it, the one design choice that mattered, how you knew it worked, what it cost to run, and what changed because of it. Most GenAI points say none of these. They name a framework and a model, and stop.
That used to be enough to stand out. It is not anymore.
Why most GenAI points get skimmed
When we analysed data scientist resumes, about 2 in 3 already mentioned GenAI, LLM or RAG work, and about 6 in 10 named RAG specifically. Among data engineers it was 1 in 4, usually as a line buried in the middle of a paragraph. (Our figures, by role.)
So the hiring manager reading your resume has seen "Built a RAG-based chatbot using LangChain and OpenAI" many times this month. They are not asking whether you have touched an LLM. They are asking whether you have built something that worked for real users, and whether you understand why it worked.
The good news is that this is easy to show if you did the work. You just have to write down the parts that a demo does not have.
The five things a strong GenAI point says
1. Who used it, and for what. "An internal assistant" is vague. "Answering policy questions for 400 customer support agents" is a product. If it never had users, say what it was meant for.
2. The design choice that mattered. Every LLM system has one or two decisions that made it work or not. Chunk size and overlap. Hybrid search instead of pure vector search. Adding a reranker. Choosing to prompt a large model instead of fine-tuning a small one, or the reverse. Pick the one you would want to talk about in an interview.
3. How you knew it worked. This is the part almost everyone skips, and it is the part a technical interviewer will push on. Did you build a set of test questions with known answers? Measure whether answers were grounded in the retrieved text? Have subject experts grade a sample? Compare against the old process? "Improved accuracy" without a method reads as a guess.
4. What it cost and how fast it was. Latency per answer, cost per thousand queries, or the move from a large model to a smaller one once you knew what quality you needed. These numbers mark the difference between a prototype and something a business can afford to run.
5. What changed. Fewer tickets escalated. Hours of manual review removed. A process that used to take a week now taking a day. If you do not know, ask the person who used it.
You will not fit all five into one bullet. Two or three is plenty. The rest is what you say when the interviewer asks.
Before and after, for the projects people actually do
These are illustrative examples, not real candidates. Swap in your own facts.
A RAG assistant
Developed a RAG-based chatbot using LangChain, OpenAI and Pinecone to answer user queries.
Built a retrieval assistant over 12,000 policy documents for 400 support agents. Added hybrid search and a reranker after pure vector search missed exact policy codes; on a 300-question test set, correct and grounded answers went from 61% to 84%.
Document extraction
Used LLMs to extract information from documents and automate workflows.
Replaced a rules-based parser for supplier invoices with an LLM extraction step plus schema validation; fields needing manual correction fell from about 1 in 5 invoices to 1 in 20, and the finance team stopped a daily two-hour review.
LLM classification replacing a model or rules
Leveraged GPT-4 for text classification tasks.
Tested an LLM against our existing ticket classifier on 2,000 labelled tickets. It won on rare categories but cost 30 times more per ticket, so we used it only for the 8% the old model was unsure about.
That last one is worth noticing. It describes a decision not to use the LLM for everything. Interviewers like that more than a story where the LLM wins at everything, because it shows you were measuring, not just building.
Fine-tuning
Fine-tuned LLaMA model on custom data for improved performance.
Fine-tuned a 7B open model on 9,000 reviewed call summaries so we could run it on our own servers for privacy reasons; it matched the hosted model on our summary checklist at about a tenth of the running cost.
Try it on your own resume. Paste one bullet into our free bullet checker to see which parts a hiring manager will look for and not find. No sign-up.
What to cut
"Prompt engineering" as a skill on its own. Everyone who has used an LLM has written prompts. Show it inside a bullet instead: what the prompt had to handle and how you tested it.
Every framework you tried. LangChain, LlamaIndex, Haystack, Semantic Kernel, CrewAI, AutoGen. Listing all of them suggests you tried each for an afternoon. Name the ones you shipped with.
Hallucination numbers with no method. "Reduced hallucinations by 90%" is the GenAI version of "improved performance by 40%". If you measured it, say how. If you did not, describe what you did to reduce them instead (grounding checks, citations, refusing to answer outside the documents).
Model names as the achievement. "Using GPT-4o and Claude" is a detail, not a result. Models change every few months. The judgement you showed choosing between them does not.
If it was only a prototype or a hackathon
Say so. A clearly labelled prototype is fine, and pretending it went to production is a bad idea, because the first follow-up question will be about monitoring and you will not have an answer.
A prototype bullet can still be strong if it shows what you learned:
Prototyped a contract Q&A tool in a two-week internal hackathon; found that answer quality depended far more on how we split clauses than on which model we used, which shaped the production design the platform team later built.
Questions to be ready for
If your resume has a GenAI point, expect some of these:
- How did you decide on the chunk size?
- How did you evaluate it before users saw it?
- What did it get wrong, and what did you do about it?
- Why not fine-tune? Or why fine-tune, instead of better retrieval?
- What did it cost per query, and who paid for it?
- How would you know if quality dropped after launch?
If you cannot answer one of these about your own project, that is useful to know now, not in the interview. Go and find out, or soften the claim on the resume.
One test before you send it
Read the bullet and remove the framework and model names. If what is left still says something, it is a good bullet. If nothing is left, it was a list of tools.
You can also paste a single bullet into our free bullet checker to see which parts a reader would look for are missing.