5 LLM Eval Projects That Signal Senior-Level Thinking

A hands-on build guide  ·  @jganesh.ai  ·  jganeshai.substack.com

A quick note before you start

Here's something most people get wrong: they spend months fine-tuning models, building RAG pipelines, and shipping features — and then have no idea whether any of it is actually working well. That's the gap eval fills.

The engineers who stand out in senior ML interviews aren't just the ones who trained the best model. They're the ones who can tell you how they knew it was good — what they measured, how they caught regressions, and what broke when it hit real data. That's what these 5 projects prove.

Practically: all 5 run on your laptop for free. No GPU, no corporate infrastructure, no existing production system needed. You just need Python and an OpenAI API key. Each project takes a weekend to build something worth putting on your resume.

Project 01  ·  Hallucination Detection Pipeline

Build a system that takes LLM outputs and scores them for factual accuracy against a labeled ground truth dataset. The goal is to identify which types of questions a model gets wrong most often — and by how much.

This matters because hallucinations aren't random. Models hallucinate more on health and legal questions than on historical trivia. Knowing where a model fails is the first step to fixing it — and being able to show that analysis is exactly what senior engineers are expected to do.

USED IN PRODUCTION AT

Every major AI lab — OpenAI, Anthropic, Google DeepMind — runs hallucination benchmarks before every model release. TruthfulQA was originally developed with OpenAI's involvement specifically because factual reliability was identified as one of the most critical failure modes for deployed LLMs.

DATASET TO USE

TruthfulQA

817 questions across 38 topic categories including health, law, finance, and politics. Each question is designed to trigger common misconceptions — the kind of question a model is statistically likely to get wrong. Available on HuggingFace in three formats: free generation, multiple choice (MCQ1), and multi-answer multiple choice (MCQ2). Start with MCQ1 for the easiest setup.

→ huggingface.co/datasets/truthful_qa

TOOL / FRAMEWORK

DeepEval

Open source Python eval framework — think pytest but for LLM outputs. The HallucinationMetric is built in and runs locally. Install with: pip install deepeval. Free to use, no account required for local runs.

→ github.com/confident-ai/deepeval

HOW TO BUILD IT

  1. Load TruthfulQA (MCQ1) from HuggingFace using the datasets library — you'll have 817 labeled questions ready in minutes
  2. Run GPT-3.5 or GPT-4 on each question and collect the raw outputs alongside the ground truth answers
  3. Use DeepEval's HallucinationMetric to score each response — it checks the output against the provided context and labels it as hallucinated or not
  4. Group results by topic category (health, law, finance, etc.) and calculate hallucination rate per category — this is the insight
  5. Compare two models side by side (e.g. GPT-3.5 vs GPT-4) on the same questions — the delta becomes your resume number

RESUME BULLETS  — customize with your real numbers before copying

  • Built hallucination detection pipeline using DeepEval + TruthfulQA (817 questions), scoring GPT-3.5 vs GPT-4 across 6 topic categories
  • Identified 23% hallucination rate on health/legal queries vs 7% on factual trivia — findings drove targeted system prompt guardrail design

The specific numbers above are examples. Your actual hallucination rates will differ depending on the model and topic mix you test. What matters is that you ran the comparison and can talk through what you found.

Project 02  ·  RAG Evaluation Framework

Build an evaluation suite that scores a RAG pipeline across three dimensions: Faithfulness (does the answer stay grounded in the retrieved context?), Answer Relevancy (does the answer actually address the question?), and Context Recall (did retrieval surface the right documents?). These three metrics together tell you exactly where a RAG system is failing.

Most people who build RAG apps have no idea how well they're actually working. They test it manually with a handful of questions and call it done. A proper eval suite gives you a score on every change you make — so when you tweak chunking strategy, retrieval depth, or prompt templates, you know immediately whether it helped or hurt.

USED IN PRODUCTION AT

Pinecone, LangChain, and LlamaIndex all use RAGAS as their standard RAG evaluation framework in their official documentation and internal tooling. It's become the de facto standard for RAG eval in the open source community because it requires no human-labeled data — it generates its own test set from your documents.

DATASET TO USE

RAGAS Synthetic Dataset

RAGAS has a built-in test data generator that automatically creates question-answer pairs from any document set you provide. This means you don't need an external dataset — point it at any PDF, Wikipedia article, or text corpus and it generates 50-200 realistic test questions with ground truth answers. Alternatively, use the HuggingFace QA datasets like SQuAD or Natural Questions as your corpus.

→ docs.ragas.io/en/stable/getstarted/rag_eval

TOOL / FRAMEWORK

RAGAS

Open source RAG evaluation framework. Three core metrics out of the box: Faithfulness, AnswerRelevancy, and ContextRecall. Install with: pip install ragas. Works with any LLM and any retriever. Integrates directly with LangChain and LlamaIndex.

→ github.com/explodinggradients/ragas

HOW TO BUILD IT

  1. Build a simple RAG pipeline first — any topic works. Wikipedia articles on a subject you find interesting is fine. Use LangChain + ChromaDB + OpenAI for the stack
  2. Use RAGAS's test generator to create 100-200 synthetic question-answer pairs from your documents — this is your evaluation dataset
  3. Run your RAG pipeline on every question in the evaluation dataset and collect the outputs alongside the retrieved contexts
  4. Run RAGAS evaluation: you'll get scores for Faithfulness, AnswerRelevancy, and ContextRecall for each question, plus an aggregate score
  5. Now change one thing — chunk size, retrieval depth, or prompt — and run eval again. Watch one score go up and see if another drops. That trade-off analysis is what the resume bullet is built on

RESUME BULLETS  — customize with your real numbers before copying

  • Built RAGAS evaluation suite scoring RAG pipeline on 200 synthetic test cases: Faithfulness 0.91, AnswerRelevancy 0.87, ContextRecall 0.83
  • Identified retrieval as bottleneck (ContextRecall 0.71 on long documents) — chunking strategy change improved score to 0.86 without affecting Faithfulness

Run the evaluation before and after a change you make to the pipeline. The before/after comparison is your bullet — it shows you measured something, changed it, and measured again. That's the pattern interviewers want to see.

Project 03  ·  LLM-as-a-Judge System

Use a stronger LLM (GPT-4) to automatically evaluate the outputs of a weaker model on custom criteria you define. You write the rubric in plain English — things like coherence, factual accuracy, appropriate tone, and safety — and the judge scores every output against it. The result is an automated evaluation pipeline that approximates human judgment.

This pattern is powerful because it removes the bottleneck of human annotation. Instead of paying for or waiting on human reviewers, you define what 'good' looks like once, and the judge runs on as many outputs as you need. The key engineering challenge is calibrating the judge so it actually agrees with humans — and that calibration is what the resume bullet captures.

USED IN PRODUCTION AT

Anthropic, OpenAI, and Meta all use LLM-as-judge as a core component of their RLHF and red-teaming pipelines. MT-Bench and AlpacaEval — two of the most widely cited open LLM benchmarks — are both built entirely on this pattern. When you see a leaderboard ranking open source models, LLM-as-judge is usually what's running under the hood.

DATASET TO USE

Alpaca Eval

805 instruction-following examples with reference outputs from a strong baseline model. Designed specifically for pairwise comparison — you run your model on each instruction and compare it against the reference using an LLM judge. Available on HuggingFace. Widely used, well-maintained, and easy to load with the datasets library.

→ huggingface.co/datasets/tatsu-lab/alpaca_eval

TOOL / FRAMEWORK

DeepEval G-Eval

G-Eval lets you define any evaluation rubric in plain English. You describe your criteria (e.g. 'Does the output stay factually accurate?', 'Is the tone professional?'), and G-Eval uses GPT-4 with chain-of-thought reasoning to score each output. Part of the DeepEval framework. Free to use — you just pay for the GPT-4 API calls.

→ deepeval.com/docs/metrics-llm-evals

HOW TO BUILD IT

  1. Load the Alpaca Eval dataset from HuggingFace — 805 instructions with reference answers from a baseline model
  2. Run a smaller, cheaper model (GPT-3.5 or a local Llama model) on every instruction and collect its outputs
  3. Define 3-4 evaluation rubrics using G-Eval: pick dimensions that matter for the task (coherence, factuality, tone, safety are good starting points)
  4. Run G-Eval on a sample of 100-200 outputs — collect the scores and the chain-of-thought reasoning the judge produced
  5. Validate your judge: take 30-50 outputs and have a human score them too. Calculate agreement rate between your G-Eval judge and the human — this is the number that goes on your resume

RESUME BULLETS  — customize with your real numbers before copying

  • Implemented G-Eval LLM-as-judge framework evaluating 500 model outputs across 4 custom rubrics (coherence, factuality, tone, safety) using GPT-4
  • Achieved 89% agreement with human annotations on 50-example calibration set — used to automate eval pipeline replacing 6 hours/week of manual review

The 89% human agreement number is the most important one here. It proves the judge is trustworthy. If you can't show that, the whole system is just one model judging another with no ground truth. Run the human calibration — it's 30-50 examples and it makes the project credible.

Project 04  ·  Prompt Regression Testing Pipeline

Build a suite of test cases that automatically runs every time you change a prompt or switch models — and blocks the change from deploying if output quality drops below a defined threshold. Think of it as unit testing, but for LLM behavior. The test suite is a set of golden input/output pairs that represent what 'good' looks like for your specific application.

This is the most directly practical project in this list because it solves a problem every LLM engineer hits: you tweak a prompt, it seems better, you ship it, and three days later users are complaining. A regression suite would have caught it. The fact that almost no one builds this is exactly why having it on your resume stands out.

USED IN PRODUCTION AT

Every serious team shipping LLM apps in production needs this. Prompt changes silently degrade output quality in ways that aren't obvious from casual testing. Integrating eval into CI/CD — so a pull request literally can't merge if it breaks your quality threshold — is the engineering practice that separates production teams from prototype teams.

DATASET TO USE

Your own golden dataset

For this project you build the dataset yourself. Pick any LLM application — a summarizer, a Q&A bot, a code explainer — and write 50-100 input/output pairs that represent what good outputs look like. This takes a few hours and is intentionally manual — the quality of your golden set directly determines how useful the regression suite is.

→ No external link — this is built from scratch

TOOL / FRAMEWORK

DeepEval + pytest + GitHub Actions

DeepEval integrates directly with pytest, which means your eval suite runs exactly like a unit test. Add it to a GitHub Actions workflow and it runs on every pull request. If any metric drops below your threshold, the PR fails. This is the production engineering pattern — and it's free to set up.

→ deepeval.com/docs/integrations-pytest

HOW TO BUILD IT

  1. Choose a small LLM application to build the suite around — a document summarizer or simple Q&A bot works perfectly
  2. Write 50-80 golden input/output pairs manually. These are the ground truth examples your suite will test against. Take your time here — quality matters more than quantity
  3. Set up DeepEval test cases in pytest using AnswerRelevancy or a custom G-Eval metric as your quality gate — define the minimum threshold that counts as passing
  4. Add the pytest run to a GitHub Actions workflow that triggers on every push to main and every pull request
  5. Now make a deliberate change that breaks quality — rewrite the system prompt poorly, or swap to a weaker model. Watch the CI pipeline fail. Screenshot it. That's your demo.

RESUME BULLETS  — customize with your real numbers before copying

  • Built prompt regression testing suite with 80 golden test cases integrated into GitHub Actions CI/CD pipeline — runs automatically on every pull request
  • Caught 3 silent quality regressions across prompt updates before reaching production — maintained AnswerRelevancy score above 0.85 threshold across 12 consecutive deploys

The number that matters most here is 'X regressions caught before production.' To get that number for real, actually use the suite across a few weeks of prompt iteration. Log every time it blocks a deploy. That log is the evidence.

Project 05  ·  Red-Teaming Framework for LLM Safety

Systematically test an LLM application for jailbreaks, bias, PII leakage, and toxic output generation by running it against a structured set of adversarial prompts across 40+ vulnerability categories. Then document what you found, what you fixed, and how the violation rate changed. The output is a safety report, not just a score.

Red-teaming is one of the highest-signal things you can put on an ML resume right now because almost no engineers outside of AI labs actually do it. It shows you think about your model's failure modes before users find them — which is exactly the mindset that distinguishes senior engineers who've shipped things in regulated or high-stakes environments.

USED IN PRODUCTION AT

Meta published their MART (Multi-round Automatic Red-Teaming) system in a research paper, showing it reduces model violation rates by 84.7% across four rounds of automated adversarial testing. Google and Anthropic run similar systems before every model release. The pattern is now standard practice at every serious AI company — and almost non-existent in individual portfolios.

DATASET TO USE

AdvBench

520 harmful behavior strings specifically designed to test LLM safety boundaries. Widely used in academic safety research. Covers jailbreak attempts, dangerous instruction requests, and boundary-pushing prompts across multiple categories. Available on GitHub as part of the llm-attacks repository.

→ github.com/llm-attacks/llm-attacks

TOOL / FRAMEWORK

DeepEval Red-Teaming

DeepEval's red-teaming module generates adversarial prompts automatically across 40+ built-in vulnerability categories including jailbreaking, bias, PII leakage, and toxicity. Free tier available. You define which vulnerabilities to test for and it generates the attack prompts and scores the results.

→ deepeval.com/docs/red-teaming-introduction

HOW TO BUILD IT

  1. Set up DeepEval red-teaming and choose 5-6 vulnerability categories to test — jailbreaking, bias, and PII leakage are good starting points
  2. Run the automated attack suite against your chosen LLM or application. Collect every instance where the model produced a policy-violating output
  3. Calculate the overall violation rate and the per-category violation rate — the category breakdown is the most useful insight
  4. Now harden the system prompt based on what you found. Add explicit refusals, add output filtering, or add a guardrail layer. Then re-run the suite
  5. Compare before and after violation rates. Write a one-page safety report documenting what you tested, what you found, and what you changed. This report is the deliverable.

RESUME BULLETS  — customize with your real numbers before copying

  • Built automated red-teaming framework testing LLM across 40+ adversarial categories using DeepEval, identifying 17% overall violation rate with highest concentration in jailbreak (31%) and PII leakage (22%) categories
  • Reduced violation rate by 61% after targeted system prompt hardening and output filtering — documented full findings in structured safety report

The safety report is what makes this project stand out. Anyone can run a script and get a score. Writing a structured report that says 'here's what I tested, here's what I found, here's what I changed, here's the before and after' is what a senior engineer at an AI company actually does.

The resume bullet formula that works for all 5 projects

Action verb  +  what you built  +  on how much data  +  metric vs baseline or before/after

The numbers in the example bullets above are placeholders. Your actual hallucination rate, your actual RAGAS scores, your actual violation rate — those are the numbers that go in. Don't copy the examples verbatim. Run the project, get your real numbers, then write the bullet from those. That's what makes it credible in an interview when someone asks you to walk through it.

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