Skills Assessment Methods

Explaining the Evaluation method LLM-as-a-Judge (LLMaaJ). Token-based metrics like BLEU or ROUGE are still useful for structured tasks like translation or summarization. But for open-ended answers, RAG copilots, or complex enterprise prompts, they often miss the bigger picture. That’s where LLMaaJ changes the game. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗶𝘁? You use a powerful LLM as an evaluator, not a generator. It’s given: - The original question - The generated answer - And the retrieved context or gold answer 𝗧𝗵𝗲𝗻 𝗶𝘁 𝗮𝘀𝘀𝗲𝘀𝘀𝗲𝘀: ✅ Faithfulness to the source ✅ Factual accuracy ✅ Semantic alignment—even if phrased differently 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: LLMaaJ captures what traditional metrics can’t. It understands paraphrasing. It flags hallucinations. It mirrors human judgment, which is critical when deploying GenAI systems in the enterprise. 𝗖𝗼𝗺𝗺𝗼𝗻 𝗟𝗟𝗠𝗮𝗮𝗝-𝗯𝗮𝘀𝗲𝗱 𝗺𝗲𝘁𝗿𝗶𝗰𝘀: - Answer correctness - Answer faithfulness - Coherence, tone, and even reasoning quality 📌 If you’re building enterprise-grade copilots or RAG workflows, LLMaaJ is how you scale QA beyond manual reviews. To put LLMaaJ into practice, check out EvalAssist; a new tool from IBM Research. It offers a web-based UI to streamline LLM evaluations: - Refine your criteria iteratively using Unitxt - Generate structured evaluations - Export as Jupyter notebooks to scale effortlessly A powerful way to bring LLM-as-a-Judge into your QA stack. - Get Started guide: https://lnkd.in/g4QP3-Ue - Demo Site: https://lnkd.in/gUSrV65s - Github Repo: https://lnkd.in/gPVEQRtv - Whitepapers: https://lnkd.in/gnHi6SeW

I’ve bombed so many interviews because I thought memorizing answers would make me sound prepared. Turns out I sounded like a robot reading from a script (who knew?) Then one night, after getting yet another rejection email, I knew I needed to change my strategy. I started using ChatGPT not to write my answers, but to help me practice telling my own story. Today, these are my 10 go-to AI prompts to nail all of my interviews: 👉 1. Practice real mock interviews ↳ Get custom questions that actually match your target role, both technical and behavioral. 👉 2. Generate role-specific questions ↳ AI creates questions divided into technical, behavioral, and situational categories for YOUR specific job. 👉 3. Build STAR Stories that sound like you ↳ Structure your experiences using Situation, Task, Action, Result. Without sounding rehearsed. 👉 4. Turn your resume into stories ↳ Identify your key achievements and transform them into confident, results-driven narratives. 👉 5. Explain complex stuff simply ↳ Learn to break down technical concepts for both technical and non-technical interviewers. 👉 6. Get honest feedback on your answers ↳ AI evaluates your tone, clarity, and structure, then helps you sound more natural and confident. 👉 7. Master the HR and behavioral rounds ↳ Test your emotional intelligence and communication for those culture-fit conversations. 👉 8. Create your personal 7-day prep plan ↳ Build a daily routine with mock questions, review topics, and reflection exercises. 👉 9. Customize Answers for Each Company Align your responses with specific company values, mission, and role expectations. 👉 10. Nail "Tell Me About Yourself" ↳ Craft an intro that connects your journey, skills, and goals to the role, in under 2 minutes. Interview prep isn't about having perfect answers memorized. It's about knowing your story so well that you can tell it naturally, no matter how they ask the question. ChatGPT should be your practice partner, not your scriptwriter. Try these prompts before your next interview. You might surprise yourself with how prepared you actually are 👏 ♻️ Reshare this for someone prepping for interviews and follow me for more AI and career tips!

Most candidates practice interviews the wrong way. They just… rehearse answers in their heads. ❌ No structure. ❌ No stress simulation. ❌ No feedback loop. And then they wonder why they go blank when the real interview starts. If you want to actually master problem-solving under stress → Here’s the step-by-step mock interview framework I use to train my students who now work at Google, Amazon, Deloitte & more: 🧩 Step 1: Simulate the Stress, Don’t Avoid It Your brain can’t learn resilience in comfort. 👉 Set a timer for 2 minutes to answer each problem. 👉 Ask a friend/mentor to throw curveball follow-ups. 👉 Record yourself to see body language under pressure. This mimics real interview tension → making stress your training partner, not your enemy. 🧩 Step 2: Use the CFS Formula to Structure Every Answer Every problem-solving response must hit these 3 beats: 👉 Clarify: Restate the problem in your words (“If I understood correctly, the issue is…”). 👉 Frame: Lay out 2–3 logical buckets (MECE principle). 👉 Solve: Dive into each bucket with reasoning + examples. This ensures clarity even if nerves hit. 🧩 Step 3: Practice the Think-Aloud Method According to MIT research, interviewers rate candidates higher when they can follow their reasoning. Instead of silently panicking → verbalize: “I see two possible causes for this issue… Let me evaluate both.” This signals confidence and buys time. 🧩 Step 4: Apply the Red Team Test Before finalizing your solution, challenge it. Ask yourself: “If I were the interviewer, how would I poke holes in this?” This trains you to anticipate objections and build stronger answers. 🧩 Step 5: Run the Reflect-Refine Loop After each mock session: 👉 Write down exactly where you froze. 👉 Note what structure saved you (CFS, MECE, etc.). 👉 Refine → Run again. Within 5–6 cycles, you’ll notice dramatic improvements. Interviewers aren’t looking for instant geniuses. They’re looking for candidates who show: ✅ Calm thinking ✅ Clear structure ✅ Resilience under pressure And those skills are built in practice rooms, not just interview rooms. If you follow this framework, you won’t just “answer questions.” You’ll prove you can think like the kind of professional every company wants on their team. Would you like me to also share a real problem-solving case study (with sample answers) from one of my students who cracked a top consulting firm? Comment “Case Study” and I’ll post it next. #interviewtips #mockinterview #careergrowth #dreamjob #interviewcoach

Your unit tests mean nothing for LLM features. assert output == expected That line of code — the foundation of every software test you’ve ever written — is useless the moment your system produces non-deterministic output. And most teams shipping AI features right now have no idea what to replace it with. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ December 2023. A Chevrolet dealership in California deployed a GPT-4-powered customer service chatbot on their website. Within days, users had prompt-engineered it into agreeing to sell a 2024 Chevy Tahoe — a $58,000 vehicle — for $1. The bot said, and I quote: “that’s a legally binding offer — no takesies backsies.” The screenshots went viral. The model was doing exactly what a poorly evaluated chatbot does: it had no output guardrails, no adversarial testing, and no system checking whether its responses made any sense before they reached customers. This is what happens when you ship an LLM feature with no evaluation pipeline. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ The most common response from engineers new to LLM work is to reach for BLEU or ROUGE scores. These are the standard NLP metrics — they measure how much the generated text overlaps with a reference answer. They don’t work. Consider these two responses to the same question: Reference: “The server crashed due to a memory leak” Generated: “A memory leak caused the application to go down” These mean the same thing. A human reads both and nods. ROUGE gives the second one a score of 0.22 — nearly zero — because the words don’t overlap. The metric is measuring the wrong thing entirely. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ What actually works: a three-layer stack. Layer 1 — Deterministic checks. Free, fast, CI-friendly. Does the response refuse when it shouldn’t? Is the JSON valid? Is it hallucinating URLs? These run in milliseconds on every PR. They catch structural failures before anything else. Layer 2 — LLM-as-judge. This sounds circular. You’re using an AI to evaluate an AI. But it works because evaluation is easier than generation. Use pairwise comparison instead of a 1-5 scale — “which response is better, A or B” — and validate that the judge agrees with humans on 50-100 examples before you trust it. Layer 3 — Human review on 2% of traffic. Expensive. Focused on the queries that the automated layers flag as low confidence. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ The brutal truth: Every prompt change you ship is a regression test you didn’t run. LLM systems fail silently. Your monitoring shows 200 OK and 120ms latency. Meanwhile the model has quietly started refusing queries it handled fine last week. You don’t find out until a user complains. The teams getting this right treat their eval dataset as a first-class artifact alongside their code. Full article — the full three-layer implementation, prompt regression testing in CI Link in comments ↓ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ #SystemDesign #AIEngineering #LLM #MachineLearning

Behavioral questions are common in job interviews. Are you ready to tackle them? Here is a typical question and how to handle it: 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻: "Tell me about a time you had to explain a complex idea to a non-technical stakeholder." 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗔𝗻𝘀𝘄𝗲𝗿 𝗨𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝗦𝗧𝗔𝗥(𝗥) 𝗠𝗲𝘁𝗵𝗼𝗱: 1. 𝗦𝗶𝘁𝘂𝗮𝘁𝗶𝗼𝗻: Start by describing the context. "In my previous role, a senior manager requested insights from a complex forecasting model I had built, but they had limited understanding of the technical aspects." 2. 𝗧𝗮𝘀𝗸: Explain your responsibility in the situation. "My responsibility was to ensure the manager understood the insights in a way that enabled them to better judge the model accuracy and implications to support their decision making." 3. 𝗔𝗰𝘁𝗶𝗼𝗻: Detail the steps you took to address the problem. "I simplified the explanation by using visual aids, such as charts and graphs, and avoided technical jargon. I also related the insights to the business metrics they cared about, like revenue and customer satisfaction." 4. 𝗥𝗲𝘀𝘂𝗹𝘁: Highlight the outcome of your actions. "The manager fully understood the implications of the model and used the insights to make a strategic decision, which helped to improve last year's peak planning leading to an increase in Black Friday sales by 15%." 5. 𝗥𝗲𝗳𝗹𝗲𝗰𝘁𝗶𝗼𝗻 (Optional): Share what you learned or how you improved. "This experience taught me the importance of adjusting my communication style to the audience, which I’ve since applied successfully in other stakeholder interactions." Prepare for behavioral questions by using real examples and the STAR(R) (Situation, Task, Action, Result, [Refelction]) method. It helps you to demonstrate your problem-solving skills and ability to work effectively in a team. What’s the toughest behavioral question you’ve been asked in an interview? ---------------- ♻️ 𝗦𝗵𝗮𝗿𝗲 to help others prepare for tricky behavioral questions. ➕ 𝗙𝗼𝗹𝗹𝗼𝘄 for more daily insights on how to grow your career in the data field. #dataanalytics #datascience #interviewpreparation #jobinterview #careergrowth

Unlocking the Next Era of RAG System Evaluation: Insights from the Latest Comprehensive Survey Retrieval-Augmented Generation (RAG) has become a cornerstone for enhancing large language models (LLMs), especially when accuracy, timeliness, and factual grounding are critical. However, as RAG systems grow in complexity-integrating dense retrieval, multi-source knowledge, and advanced reasoning-the challenge of evaluating their true effectiveness has intensified. A recent survey from leading academic and industrial research organizations delivers the most exhaustive analysis yet of RAG evaluation in the LLM era. Here are the key technical takeaways: 1. Multi-Scale Evaluation Frameworks The survey dissects RAG evaluation into internal and external dimensions. Internal evaluation targets the core components-retrieval and generation-assessing not just their standalone performance but also their interactions. External evaluation addresses system-wide factors like safety, robustness, and efficiency, which are increasingly vital as RAG systems are deployed in real-world, high-stakes environments. 2. Technical Anatomy of RAG Systems Under the hood, a typical RAG pipeline is split into two main sections: - Retrieval: Involves document chunking, embedding generation, and sophisticated retrieval strategies (sparse, dense, hybrid, or graph-based). Preprocessing such as corpus construction and intent recognition is essential for optimizing retrieval relevance and comprehensiveness. - Generation: The LLM synthesizes retrieved knowledge, leveraging advanced prompt engineering and reasoning techniques to produce contextually faithful responses. Post-processing may include entity recognition or translation, depending on the use case. 3. Diverse and Evolving Evaluation Metrics The survey catalogues a wide array of metrics: - Traditional IR Metrics: Precision@K, Recall@K, F1, MRR, NDCG, MAP for retrieval quality. - NLG Metrics: Exact Match, ROUGE, BLEU, METEOR, BertScore, and Coverage for generation accuracy and semantic fidelity. - LLM-Based Metrics: Recent trends show a rise in LLM-as-judge approaches (e.g., RAGAS, Databricks Eval), semantic perplexity, key point recall, FactScore, and representation-based methods like GPTScore and ARES. These enable nuanced, context-aware evaluation that better aligns with real-world user expectations. 4. Safety, Robustness, and Efficiency The survey highlights specialized benchmarks and metrics for: - Safety: Evaluating robustness to adversarial attacks (e.g., knowledge poisoning, retrieval hijacking), factual consistency, privacy leakage, and fairness. - Efficiency: Measuring latency (time to first token, total response time), resource utilization, and cost-effectiveness-crucial for scalable deployment.

AI is changing how data analysts work. The advantage now comes from knowing which tools can help you clean data, write SQL, build dashboards, automate reporting, and explain insights faster. Here are 20 AI tools every data analyst should know in 2026: → 𝗔𝗱 𝗛𝗼𝗰 & 𝗗𝗲𝗲𝗽 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 ChatGPT and Claude help analyze files, review SQL, compare scenarios, create charts, and summarize findings. → 𝗦𝗽𝗿𝗲𝗮𝗱𝘀𝗵𝗲𝗲𝘁 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 Gemini in Sheets, Copilot in Excel, and Microsoft 365 Analyst support formulas, pattern detection, forecasting, and reporting. → 𝗕𝗜 & 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀 Power BI Copilot, Tableau Agent, and Tableau Pulse help generate calculations, monitor KPIs, and explain changes. → 𝗡𝗼-𝗖𝗼𝗱𝗲 & 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 Julius AI, Rows AI, Hex, and ThoughtSpot Spotter make it easier to explore data through natural language. → 𝗚𝗼𝘃𝗲𝗿𝗻𝗲𝗱 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 Alteryx One, Dataiku, Databricks Genie, and Snowflake Cortex Analyst support reusable, governed analytical workflows. → 𝗖𝗼𝗱𝗶𝗻𝗴 & 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 Snowflake Cortex Code, GitHub Copilot, and n8n help with SQL, Python, data pipelines, alerts, and recurring reporting. → 𝗗𝗮𝘁𝗮 𝗦𝘁𝗼𝗿𝘆𝘁𝗲𝗹𝗹𝗶𝗻𝗴 Gamma turns analytical findings into polished presentations, reports, and executive summaries. AI will not replace strong analytical thinking. But analysts who combine business understanding with AI-assisted analysis, automation, and communication will move much faster. Which AI tool has improved your data analysis workflow the most?

I reviewed the literature on the LLM-as-a-judge technique. Here are the key findings. 📉 Model Performance Variability LLMs show inconsistent performance across datasets and tasks. No single model dominates all scenarios. GPT-4 generally leads, with open-source models like Llama-3-70B close behind. 👥 Alignment with Human Judgments LLMs correlate better with non-expert human judgments than expert annotations. Top models approach but don't match human-to-human alignment levels. Improved alignment comes mainly from increased recall, not precision. ⚖️ Evaluation Method Comparison Comparative assessment outperforms absolute scoring in robustness and accuracy. Reference-guided evaluation shows promise but has limitations. Simple methods sometimes unexpectedly outperform complex ones in specific tasks. ⚠️ Vulnerabilities and Biases LLMs struggle with toxicity, safety assessments, and basic perturbations like spelling errors. They show leniency bias and are susceptible to simple adversarial attacks, especially in absolute scoring scenarios. 🛑 Limitations of Fine-tuned Judges Fine-tuned models excel in-domain but lack generalizability and aspect-specific evaluation capabilities. They're prone to superficial biases and don't benefit from prompt engineering techniques (e.g., few-shot prompting). 👩⚖️ LLMs-as-a-jury Using LLMs-as-a-jury (PoLL) outperforms single large judges, reducing bias and cost while improving consistency across tasks. This approach mitigates intra-model favoritism. 💡 Practical Recommendations Use both quantitative metrics and qualitative analysis. Consider perplexity-based detection for adversarial inputs. Multiple judges are better than one. This is a technique used at scale to review models and filter data. It's imperfect, but a poor correlation with human judgment doesn't necessarily mean it's bad. Careful prompt engineering and multiple iterations can give you excellent results in most use cases.

Claude recently introduced its Data tool, and ChatGPT recently added its Data Science tool. Both can leverage established open source libraries like pandas, statsmodels, scikit-learn, and PyMC for advanced quantitative analysis. Our Survey Science team has been exploring how these tools perform on advanced quant analysis, and we’re putting together practical guides for the most common research methods. Each guide will include the exact prompts, workflows, and step-by-step process our team tested and validated. We’re starting with: • conjoint • maxdiff • gabor-granger • cross-tab analysis We’re also considering: • hierarchical bayes • cluster analysis • driver analysis • correlation analysis • t-tests & chi-square • Van Westendorp What other survey analysis guides would you like to see?