How to Evaluate AI Research Outputs

Evaluations —or “Evals”— are the backbone for creating production-ready GenAI applications. Over the past year, we’ve built LLM-powered solutions for our customers and connected with AI leaders, uncovering a common struggle: the lack of clear, pluggable evaluation frameworks. If you’ve ever been stuck wondering how to evaluate your LLM effectively, today's post is for you. Here’s what I’ve learned about creating impactful Evals: 𝗪𝗵𝗮𝘁 𝗠𝗮𝗸𝗲𝘀 𝗮 𝗚𝗿𝗲𝗮𝘁 𝗘𝘃𝗮𝗹? - Clarity and Focus: Prioritize a few interpretable metrics that align closely with your application’s most important outcomes. - Efficiency: Opt for automated, fast-to-compute metrics to streamline iterative testing. - Representation Matters: Use datasets that reflect real-world diversity to ensure reliability and scalability. 𝗧𝗵𝗲 𝗘𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗼𝗳 𝗠𝗲𝘁𝗿𝗶𝗰𝘀: 𝗙𝗿𝗼𝗺 𝗕𝗟𝗘𝗨 𝘁𝗼 𝗟𝗟𝗠-𝗔𝘀𝘀𝗶𝘀𝘁𝗲𝗱 𝗘𝘃𝗮𝗹𝘀 Traditional metrics like BLEU and ROUGE paved the way but often miss nuances like tone or semantics. LLM-assisted Evals (e.g., GPTScore, LLM-Eval) now leverage AI to evaluate itself, achieving up to 80% agreement with human judgments. Combining machine feedback with human evaluators provides a balanced and effective assessment framework. 𝗙𝗿𝗼𝗺 𝗧𝗵𝗲𝗼𝗿𝘆 𝘁𝗼 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲: 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗬𝗼𝘂𝗿 𝗘𝘃𝗮𝗹 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 - Create a Golden Test Set: Use tools like Langchain or RAGAS to simulate real-world conditions. - Grade Effectively: Leverage libraries like TruLens or Llama-Index for hybrid LLM+human feedback. - Iterate and Optimize: Continuously refine metrics and evaluation flows to align with customer needs. If you’re working on LLM-powered applications, building high-quality Evals is one of the most impactful investments you can make. It’s not just about metrics — it’s about ensuring your app resonates with real-world users and delivers measurable value.

As we scale GenAI from demos to real-world deployment, one thing becomes clear: 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗱𝗮𝘁𝗮𝘀𝗲𝘁𝘀 𝗰𝗮𝗻 𝗺𝗮𝗸𝗲 𝗼𝗿 𝗯𝗿𝗲𝗮𝗸 𝗮 𝗚𝗲𝗻𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺. A model can be trained on massive amounts of data, but that doesn’t guarantee it understands context, nuance, or intent at inference time. You can teach a student all the textbook theory in the world. But unless you ask the right questions, in the right setting, under realistic pressure, you’ll never know what they truly grasp. This snapshot outlines the 6 dataset types that AI teams use to rigorously evaluate systems at every stage of maturity: The Evaluation Spectrum 1. 𝐐𝐮𝐚𝐥𝐢𝐟𝐢𝐞𝐝 𝐚𝐧𝐬𝐰𝐞𝐫𝐬 Meaning: Expert-reviewed responses Use: Measure answer quality (groundedness, coherence, etc.) Goal: High-quality, human-like responses 2. 𝐒𝐲𝐧𝐭𝐡𝐞𝐭𝐢𝐜 Meaning: AI-generated questions and answers Use: Test scale and performance Goal: Maximize response accuracy, retrieval quality, and tool use precision 3. 𝐀𝐝𝐯𝐞𝐫𝐬𝐚𝐫𝐢𝐚𝐥 Meaning: Malicious or risky prompts (e.g., jailbreaks) Use: Ensure safety and resilience Goal: Avoid unsafe outputs 4. 𝐎𝐎𝐃 (𝐎𝐮𝐭 𝐨𝐟 𝐃𝐨𝐦𝐚𝐢𝐧) Meaning: Unusual or irrelevant topics Use: See how well the model handles unfamiliar territory Goal: Avoid giving irrelevant or misleading answers 5. 𝐓𝐡𝐮𝐦𝐛𝐬 𝐝𝐨𝐰𝐧 Meaning: Real examples where users rated answers poorly Use: Identify failure modes Goal: Internal review, error analysis 6. 𝐏𝐑𝐎𝐃 Meaning: Cleaned, real user queries from deployed systems Use: Evaluate live performance Goal: Ensure production response quality This layered approach is essential for building: • Trustworthy AI • Measurable safety • Meaningful user experience Most organizations still rely on "accuracy-only" testing. But GenAI in production demands multi-dimensional evaluation — spanning risk, relevance, and realism. If you’re deploying GenAI at scale, ask: Are you testing the right things with the right datasets? Let’s sharpen the tools we use to measure intelligence. Because better testing = better AI. 👇 Would love to hear how you’re designing your eval pipelines. #genai #evaluation #llmops #promptengineering #aiarchitecture #openai

Evals are to GenAI what unit tests & QA scripts are to conventional software. LLMs break traditional software development. So, when it comes to building great AI products, great AI evals are non-negotiable. You see the most important people in AI saying it: Garry Tan (CEO, Y Combinator): "Evals are emerging as the real moat for AI startups." Greg Brockman (President, OpenAI): "Most overlooked skill in machine learning is creating evals." Logan Kilpatrick (HOP, Google AI Studio): "The world needs [more, better, harder, etc] evals for AI. This is one of the most important problems of our lifetime, and critical for continual progress." Let's break down why they're saying this. — ONE - LLMs break traditional software development Traditional software testing is: - Deterministic → same input = same output - Binary → either it passes or it fails - Fast → feedback comes in milliseconds - Easy → clear error logs and stack traces LLMs don’t work that way: - Non-deterministic → same input = many outputs - Graded → not right/wrong but good enough or not - Slow → evaluations take minutes, not milliseconds - Hard to debug → you’ll often ask "why did it say that?" That’s where evals come in. — TWO - Evals are the QA system for GenAI Think of evals like unit tests for LLMs. They help you: → Track performance across edge cases → Catch regressions before users do → Understand where your system fails (and why) The best AI teams build ruthless evals before they ship anything. Because if you can’t measure what "good" looks like, you’ll never know when your model is failing. Now, let's understand more about where Evals come. — THREE - What LLMs do well LLMs are amazing at: 1. Generating fluent, coherent language 2. Handling translation, summarization, and Q&A 3. Generalizing from a few examples 4. Adapting tone, style, and output structure But they struggle with: 1. Consistency (tiny prompt changes = big output shifts) 2. Truth vs. plausibility (they sound right, but are wrong) 3. Reasoning (multi-step logic and math are still hard) 4. Knowledge boundaries (cutoffs = blind spots) Evals help you catch all of this. — SO - What does a good eval system look like? The best eval systems follow a 3-step lifecycle: 1. Analyze → Study ~100 real traces to identify specific failure modes (not generic ones) 2. Measure → Build automated evaluators (prefer code-based checks over LLM-as-Judge when possible) 3. Improve → Fix highest-impact failures first, then repeat the cycle The key insight here is not to start with generic metrics and instead build evals from the ground up by studying how your specific system actually fails on your specific data. — READY - To Go Deeper? 1. Hamel Husain & Shreya Shankar created the most practical course. Get it here (you get $800 off with this link): https://lnkd.in/ek9ixfDR 2. Check out our deep dive in the newsletter: https://lnkd.in/eGbzWMxf

If you’re building with or evaluating LLMs, I am sure, you’re already thinking about benchmarks. But with so many options- MMLU, GSM8K, HumanEval, SWE-bench, MMMU, and dozens more, it’s easy to get overwhelmed. Each benchmark measures something different: → reasoning breadth → math accuracy → code correctness → multimodal understanding → scientific reasoning, and more. This one-pager is a quick reference to help you navigate that landscape. 🧠 You can use the one-pager to understand: → What each benchmark is testing → Which domain it applies to (code, math, vision, science, language) → Where it fits in your evaluation pipeline 📌 For example: → Need a code assistant? Start with HumanEval, MBPP, and LiveCodeBench → Building tutor bots? Look at MMLU, GSM8K, and MathVista → Multimodal agents? Test with SEED-Bench, MMMU, TextVQA, and MathVista → Debugging or auto-fix agents? Use SWE-bench Verified and compare fix times 🧪 Don’t stop at out-of-the-box scores. → Think about what you want the model to do → Select benchmarks aligned with your use case → Build a custom eval set that mirrors your task distribution → Run side-by-side comparisons with human evaluators for qualitative checks Benchmarks aren’t just numbers on a leaderboard, they’re tools for making informed model decisions, so use them intentionally. PS: If you want a cheat sheet that maps benchmarks to common GenAI use cases (e.g. RAG agents, code assistants, AI tutors), let me know in the comments- happy to put them together. Happy building ❤️ 〰️〰️〰️ Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg