Skills Assessment Methods

Evaluating LLMs is hard. Evaluating agents is even harder. This is one of the most common challenges I see when teams move from using LLMs in isolation to deploying agents that act over time, use tools, interact with APIs, and coordinate across roles. These systems make a series of decisions, not just a single prediction. As a result, success or failure depends on more than whether the final answer is correct. Despite this, many teams still rely on basic task success metrics or manual reviews. Some build internal evaluation dashboards, but most of these efforts are narrowly scoped and miss the bigger picture. Observability tools exist, but they are not enough on their own. Google’s ADK telemetry provides traces of tool use and reasoning chains. LangSmith gives structured logging for LangChain-based workflows. Frameworks like CrewAI, AutoGen, and OpenAgents expose role-specific actions and memory updates. These are helpful for debugging, but they do not tell you how well the agent performed across dimensions like coordination, learning, or adaptability. Two recent research directions offer much-needed structure. One proposes breaking down agent evaluation into behavioral components like plan quality, adaptability, and inter-agent coordination. Another argues for longitudinal tracking, focusing on how agents evolve over time, whether they drift or stabilize, and whether they generalize or forget. If you are evaluating agents today, here are the most important criteria to measure: • 𝗧𝗮𝘀𝗸 𝘀𝘂𝗰𝗰𝗲𝘀𝘀: Did the agent complete the task, and was the outcome verifiable? • 𝗣𝗹𝗮𝗻 𝗾𝘂𝗮𝗹𝗶𝘁𝘆: Was the initial strategy reasonable and efficient? • 𝗔𝗱𝗮𝗽𝘁𝗮𝘁𝗶𝗼𝗻: Did the agent handle tool failures, retry intelligently, or escalate when needed? • 𝗠𝗲𝗺𝗼𝗿𝘆 𝘂𝘀𝗮𝗴𝗲: Was memory referenced meaningfully, or ignored? • 𝗖𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗶𝗼𝗻 (𝗳𝗼𝗿 𝗺𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝘀𝘆𝘀𝘁𝗲𝗺𝘀): Did agents delegate, share information, and avoid redundancy? • 𝗦𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗼𝘃𝗲𝗿 𝘁𝗶𝗺𝗲: Did behavior remain consistent across runs or drift unpredictably? For adaptive agents or those in production, this becomes even more critical. Evaluation systems should be time-aware, tracking changes in behavior, error rates, and success patterns over time. Static accuracy alone will not explain why an agent performs well one day and fails the next. Structured evaluation is not just about dashboards. It is the foundation for improving agent design. Without clear signals, you cannot diagnose whether failure came from the LLM, the plan, the tool, or the orchestration logic. If your agents are planning, adapting, or coordinating across steps or roles, now is the time to move past simple correctness checks and build a robust, multi-dimensional evaluation framework. It is the only way to scale intelligent behavior with confidence.

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.

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!

Small variations in prompts can lead to very different LLM responses. Research that measures LLM prompt sensitivity uncovers what matters, and the strategies to get the best outcomes. A new framework for prompt sensitivity, ProSA, shows that response robustness increases with factors including higher model confidence, few-shot examples, and larger model size. Some strategies you should consider given these findings: 💡 Understand Prompt Sensitivity and Test Variability: LLMs can produce different responses with minor rephrasings of the same prompt. Testing multiple prompt versions is essential, as even small wording adjustments can significantly impact the outcome. Organizations may benefit from creating a library of proven prompts, noting which styles perform best for different types of queries. 🧩 Integrate Few-Shot Examples for Consistency: Including few-shot examples (demonstrative samples within prompts) enhances the stability of responses, especially in larger models. For complex or high-priority tasks, adding a few-shot structure can reduce prompt sensitivity. Standardizing few-shot examples in key prompts across the organization helps ensure consistent output. 🧠 Match Prompt Style to Task Complexity: Different tasks benefit from different prompt strategies. Knowledge-based tasks like basic Q&A are generally less sensitive to prompt variations than complex, reasoning-heavy tasks, such as coding or creative requests. For these complex tasks, using structured, example-rich prompts can improve response reliability. 📈 Use Decoding Confidence as a Quality Check: High decoding confidence—the model’s level of certainty in its responses—indicates robustness against prompt variations. Organizations can track confidence scores to flag low-confidence responses and identify prompts that might need adjustment, enhancing the overall quality of outputs. 📜 Standardize Prompt Templates for Reliability: Simple, standardized templates reduce prompt sensitivity across users and tasks. For frequent or critical applications, well-designed, straightforward prompt templates minimize variability in responses. Organizations should consider a “best-practices” prompt set that can be shared across teams to ensure reliable outcomes. 🔄 Regularly Review and Optimize Prompts: As LLMs evolve, so may prompt performance. Routine prompt evaluations help organizations adapt to model changes and maintain high-quality, reliable responses over time. Regularly revisiting and refining key prompts ensures they stay aligned with the latest LLM behavior. Link to paper in comments.

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

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

How to Excel in Behavioral Cybersecurity Interviews Behavioral interviews can be trickier than technical ones if you are not prepared A few tips from my end 👇 Behavioral interviews dive deep into how you handle real-world challenges, collaborate with teams, and align with company culture. Expect questions around teamwork, conflict resolution, critical thinking, and ethics. 1 . Use the tried-and-tested STAR methodology for behavioral questions: - Situation: Set the scene of your story. - Task: Describe what needed to be done. - Action: Explain your specific actions. - Result: Highlight the positive outcomes. Example: Situation: Our cybersecurity team was working on a critical incident response project when a disagreement arose between two team members about the best approach to patch a vulnerability. The conflict was causing delays and affecting team morale. Task: As the team lead, I needed to resolve the conflict quickly to ensure the project stayed on track while maintaining a positive working environment. Action: I organized a meeting to facilitate open communication between the two team members. I encouraged each to explain their perspective, asked probing questions to clarify their positions, and then worked with the team to identify a solution that incorporated the strengths of both approaches. I also set clear guidelines for future communication to prevent similar conflicts. Result: The issue was resolved, and we successfully implemented a hybrid solution that enhanced the security patch. The team felt heard and appreciated, which improved collaboration moving forward. We completed the project ahead of schedule, and the incident was handled without further disruptions. 2 . Be authentic: Genuine responses foster trust and connection. Do not sound like you reading from ChatGPT ! 3. Listen carefully: Tailor your answers to directly address the questions asked. Good luck on your next interview !

Exciting New Research on LLM Evaluation Validity! I just read a fascinating paper titled "LLM-Evaluation Tropes: Perspectives on the Validity of LLM-Evaluations" that addresses a critical issue in our field: as Large Language Models (LLMs) increasingly replace human judges in evaluating information retrieval systems, how can we ensure these evaluations remain valid? The paper, authored by researchers from universities and companies across multiple countries (including University of New Hampshire, RMIT, Canva, University of Waterloo, The University of Edinburgh, Radboud University, and Microsoft), identifies 14 "tropes" or recurring patterns that can undermine LLM-based evaluations. The most concerning trope is "Circularity" - when the same LLM is used both to evaluate systems and within the systems themselves. The authors demonstrate this problem using TREC RAG 2024 data, showing that when systems are reranked using the Umbrela LLM evaluator and then evaluated with the same tool, it creates artificially inflated scores (some systems scored >0.95 on LLM metrics but only 0.68-0.72 on human evaluations). Other key tropes include: - LLM Narcissism: LLMs prefer outputs from their own model family - Loss of Variety of Opinion: LLMs homogenize judgment - Self-Training Collapse: Training LLMs on LLM outputs leads to concept drift - Predictable Secrets: When LLMs can guess evaluation criteria For each trope, the authors propose practical guardrails and quantification methods. They also suggest a "Coopetition" framework - a collaborative competition where researchers submit systems, evaluators, and content modification strategies to build robust test collections. If you work with LLM evaluations, this paper is essential reading. It offers a balanced perspective on when and how to use LLMs as judges while maintaining scientific rigor.

A painful (yet powerful) learning trick is to record yourself answering interview questions. Then watch the recording and give yourself feedback. You'll see the good, the bad, and ugly of your answers. Even better is if you have a trusted friend watch the recording and get their feedback. Watching others is also great, if you can get your hands on their actual interview. Alan Stein has worked at Google, Facebook, and Salesforce, where he's also conducted 100s of interviews. Alan graciously shared his own answers to real questions from a behavioral interview. He also shares an interview Bar Raiser giving him raw feedback and advice on what to do, and not do, in interviews. Watch the mock interview and feedback here: https://lnkd.in/gAAX3mRW