Training Evaluation Models

🍱 How To Design Effective Dashboard UX (+ Figma Kits). With practical techniques to drive accurate decisions with the right data. 🤔 Business decisions need reliable insights to support them. ✅ Good dashboards deliver relevant and unbiased insights. ✅ They require clean, well-organized, well-formatted data. ✅ Often packed in a tight grid, with little whitespace (if any). 🚫 Scrolling is inefficient in dashboards: makes comparing hard. ✅ Start with the audience and decisions they need to make. ✅ Study where, when and how the dashboard will be used. ✅ Study what metrics/data would support user’s decisions. ✅ Explore how to aggregate, organize and filter this data. ✅ More data → more filters/views, less data → single values. 🚫 Simpler ≠ better: match user expertise when choosing charts. ✅ Prioritize metrics: key insights → top left, rest → bottom right. ✅ Then set layout density: open, table, grouped or schematic. ✅ Add customizable presets, layouts, views + guides, videos. ✅ Next, sketch dashboards on paper, get feedback, iterate. When designing dashboards, the most damaging thing we can do is to oversimplify a complex domain, or mislead the audience. Our data must be complete and unbiased, our insights accurate and up-to-date, and our UI must match users’ varying levels of data literacy. Dashboard value is measured by useful actions it prompts. So invest most of the design time scrutinizing metrics needed to drive relevant insights. Bring data owners and developers early in the process. You will need their support to find sources, but also clean, verify, aggregate, organize and filter data. Good questions to ask: 🧭 What decisions do you want to be more informed on? (Purpose) 😤 What’s the hardest thing about these decisions? (Frustrations) 📊 Describe how you are making these decisions? (Sources) 🗃️ What data helps you make these decisions? (Metrics) 🧠 How much detail is needed for each metric? (Data literacy) 🚀 How often will you be using this dashboard? (Value) 🎲 What constraints should we know about? (Risks) And, most importantly, test dashboards repeatedly with actual users. Choose key tasks and see how successful users are. It won’t be right at first, but once you get beyond 80% success rate, your users might never leave your dashboard again. ✤ Dashboard Patterns + Figma Kits: Data Dashboards UX: https://lnkd.in/eticxU-N 👍 dYdX: https://lnkd.in/eUBScaHp 👍 Ethr: https://lnkd.in/eSTzcN7V Orange: https://lnkd.in/ewBJZcgC 👍 Semrush: https://lnkd.in/dUgWtwnu 👍 UKO: https://lnkd.in/eNFv2p_a 👍 Wireframing Kit: https://lnkd.in/esqRdDyi 👍 [continues in comments ↓]

Don't just blindly use LLMs, evaluate them to see if they fit into your criteria. Not all LLMs are created equal. Here’s how to measure whether they’re right for your use case👇 Evaluating LLMs is critical to assess their performance, reliability, and suitability for specific tasks. Without evaluation, it would be impossible to determine whether a model generates coherent, relevant, or factually correct outputs, particularly in applications like translation, summarization, or question-answering. Evaluation ensures models align with human expectations, avoid biases, and improve iteratively. Different metrics cater to distinct aspects of model performance:  Perplexity quantifies how well a model predicts a sequence (lower scores indicate better familiarity with the data), making it useful for gauging fluency. ROUGE-1 measures unigram (single-word) overlap between model outputs and references, ideal for tasks like summarization where content overlap matters. BLEU focuses on n-gram precision (e.g., exact phrase matches), commonly used in machine translation to assess accuracy. METEOR extends this by incorporating synonyms, paraphrases, and stemming, offering a more flexible semantic evaluation. Exact Match (EM) is the strictest metric, requiring verbatim alignment with the reference, often used in closed-domain tasks like factual QA where precision is paramount. Each metric reflects a trade-off: EM prioritizes literal correctness, while ROUGE and BLEU balance precision with recall. METEOR and Perplexity accommodate linguistic diversity, rewarding semantic coherence over exact replication. Choosing the right metric depends on the task—e.g., EM for factual accuracy in trivia, ROUGE for summarization breadth, and Perplexity for generative fluency. Collectively, these metrics provide a multifaceted view of LLM capabilities, enabling developers to refine models, mitigate errors, and align outputs with user needs. The table’s examples, such as EM scoring 0 for paraphrased answers, highlight how minor phrasing changes impact scores, underscoring the importance of context-aware metric selection. Know more about how to evaluate LLMs: https://lnkd.in/gfPBxrWc Here is my complete in-depth guide on evaluating LLMs: https://lnkd.in/gjWt9jRu Follow me on my YouTube channel so you don't miss any AI topic: https://lnkd.in/gMCpfMKh

*** 🚨 Discussion Piece 🚨 *** Is it Time to Move Beyond Kirkpatrick & Phillips for Measuring L&D Effectiveness? Did you know organisations spend billions on Learning & Development (L&D), yet only 10%-40% of that investment actually translates into lasting behavioral change? (Kirwan, 2024) As Brinkerhoff vividly puts it, "training today yields about an ounce of value for every pound of resources invested." 1️⃣ Limitations of Popular Models: Kirkpatrick's four-level evaluation and Phillips' ROI approach are widely used, but both neglect critical factors like learner motivation, workplace support, and learning transfer conditions. 2️⃣ Importance of Formative Evaluation: Evaluating the learning environment, individual motivations, and training design helps to significantly improve L&D outcomes, rather than simply measuring after-the-fact results. 3️⃣ A Comprehensive Evaluation Model: Kirwan proposes a holistic "learning effectiveness audit," which integrates inputs, workplace factors, and measurable outcomes, including Return on Expectations (ROE), for more practical insights. Why This Matters: Relying exclusively on traditional, outcome-focused evaluation methods may give a false sense of achievement, missing out on opportunities for meaningful improvement. Adopting a balanced, formative-summative approach could ensure that billions invested in L&D truly drive organisational success. Is your organisation still relying solely on Kirkpatrick or Phillips—or are you ready to evolve your L&D evaluation strategy?

If you’re an AI engineer, understanding how LLMs are trained and aligned is essential for building high-performance, reliable AI systems. Most large language models follow a 3-step training procedure: Step 1: Pretraining → Goal: Learn general-purpose language representations. → Method: Self-supervised learning on massive unlabeled text corpora (e.g., next-token prediction). → Output: A pretrained LLM, rich in linguistic and factual knowledge but not grounded in human preferences. → Cost: Extremely high (billions of tokens, trillions of FLOPs). → Pretraining is still centralized within a few labs due to the scale required (e.g., Meta, Google DeepMind, OpenAI), but open-weight models like LLaMA 4, DeepSeek V3, and Qwen 3 are making this more accessible. Step 2: Finetuning (Two Common Approaches) → 2a: Full-Parameter Finetuning - Updates all weights of the pretrained model. - Requires significant GPU memory and compute. - Best for scenarios where the model needs deep adaptation to a new domain or task. - Used for: Instruction-following, multilingual adaptation, industry-specific models. - Cons: Expensive, storage-heavy. → 2b: Parameter-Efficient Finetuning (PEFT) - Only a small subset of parameters is added and updated (e.g., via LoRA, Adapters, or IA³). - Base model remains frozen. - Much cheaper, ideal for rapid iteration and deployment. - Multi-LoRA architectures (e.g., used in Fireworks AI, Hugging Face PEFT) allow hosting multiple finetuned adapters on the same base model, drastically reducing cost and latency for serving. Step 3: Alignment (Usually via RLHF) Pretrained and task-tuned models can still produce unsafe or incoherent outputs. Alignment ensures they follow human intent. Alignment via RLHF (Reinforcement Learning from Human Feedback) involves: → Step 1: Supervised Fine-Tuning (SFT) - Human labelers craft ideal responses to prompts. - Model is fine-tuned on this dataset to mimic helpful behavior. - Limitation: Costly and not scalable alone. → Step 2: Reward Modeling (RM) - Humans rank multiple model outputs per prompt. - A reward model is trained to predict human preferences. - This provides a scalable, learnable signal of what “good” looks like. → Step 3: Reinforcement Learning (e.g., PPO, DPO) - The LLM is trained using the reward model’s feedback. - Algorithms like Proximal Policy Optimization (PPO) or newer Direct Preference Optimization (DPO) are used to iteratively improve model behavior. - DPO is gaining popularity over PPO for being simpler and more stable without needing sampled trajectories. Key Takeaways: → Pretraining = general knowledge (expensive) → Finetuning = domain or task adaptation (customize cheaply via PEFT) → Alignment = make it safe, helpful, and human-aligned (still labor-intensive but improving) Save the visual reference, and follow me (Aishwarya Srinivasan) for more no-fluff AI insights ❤️ PS: Visual inspiration: Sebastian Raschka, PhD

Business leaders who lay off data engineers to ramp up for AI probably look up and down before crossing the street. GenAI doesn’t make data irrelevant. It makes first-party contextual data more critical than ever. I teach executives a simple framework called the #AI College Model. Think of LLMs like a student headed off to university. Pretraining is the prerequisites courses that provide LLMs with the horizontal breadth required to build deeper capabilities. Like college prerequisites, pretraining only gives LLMs a 6-inch depth in each subject. Post-training is courses that support a college major, specialization, or advanced degree. It uses contextual first-party #data and reinforcement learning to develop vertical depth in a domain. AI researchers focus on pretraining, and that’s where all the hype is centered, but businesses care more about post-training. Value and capabilities that can be monetized get baked into the LLM during post-training. Reinforcement learning is much more expensive (computationally and/or human-labor) than high-quality contextual datasets. The more information a business has, the lower post-training costs and higher AI product margins get. BI data is formatted for people and LLMs who have already graduated from college. #DataEngineering turns BI data into information and curates new information sets that can be used for post-training #GenAI. It’s critical for executive leaders to rapidly upskill their data and AI literacy. Business and operating models increasingly rely on data and AI, so it’s ridiculous to think that the C-suite can be effective without understanding them.

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

Booking.com released a guide on how they evaluate every single AI app they build. Evaluating LLMs in prod is a different game than evaluating traditional ML models. - Golden datasets matter: human-annotated data is still the foundation for building trustworthy judge-LLMs. Without reliable labels, automated evaluation breaks down. - Annotation protocols are key: whether you go with a single annotator (basic) or multiple with consensus/weights (advanced), the consistency of annotation directly impacts evaluation quality. - Judge-LLM can't be the same as target-LLM: a stronger LLM can be used to evaluate the outputs of another, allowing scalable and automated monitoring of GenAI systems. - Pointwise vs. comparative judges: pointwise scoring works for production monitoring, but comparative evaluation (A vs. B) often provides stronger signals for ranking and system improvement. - Automation + synthetic data are emerging directions: auto-prompt pipelines and synthetic golden datasets could significantly reduce the time and cost of judge-LLM development. Link to full article by Georgios Christos Chouliaras, Antonio Castelli and Zeno Belligoli : https://lnkd.in/g3-qWFhB ♻️ Share it with anyone who might benefit :) I regularly share AI Agents and RAG projects on my newsletter: https://lnkd.in/dqJDN2NE #AI #GenAI #LLMs

Executives don’t care about content or courses. They care about impact. One of the fastest ways for L&D to lose credibility is to talk about “great training sessions” when the business wants to talk about performance. That’s why I like the simplicity of the Kirkpatrick-Phillips model, not as theory, but as a discipline. It forces L&D to stop thinking in events and start thinking in evidence. Here’s how to use it properly. Level 1 – Reaction This isn’t about smile sheets. It’s about understanding whether the environment and the experience created the right conditions for learning. Did people engage? Did it feel relevant? If the answer is no, behaviour change is already unlikely. Level 2 – Learning This is where you check what actually shifted. What did people learn? What can they now explain, understand or demonstrate? If you can’t measure it, you can’t build on it. Level 3 – Behaviour This is the most important level and the most neglected. Are people applying what they learned in the real world? What habits have changed? What’s different in how they show up at work? No behaviour change = no impact. Level 4 – Results Here is where you start to talk the language of business. What outcomes changed because of the new behaviours? Time saved, errors reduced, productivity increased, customer satisfaction improved… this is the proof leaders care about. Level 5 – ROI Now you answer the question every senior leader is quietly asking: “Was it worth it?” If the business can see the financial value of the change, L&D moves from cost centre to performance partner. But the real magic of this model is not the levels, it’s the mindset. • Start with results, not content. • Link every level so the evidence forms a clear chain. • Gather real data, not guesswork. • Involve managers early, they make or break behaviour change. • Tell the story clearly so people can see the impact, not just hear you describe it. Learning without impact is noise. Learning with evidence is strategy. ————————————— My latest book talks exactly about this. IMPACT - How to turn learning into results The book is available at Amazon. Check it out here: https://amzn.eu/d/2sWvJxK ————————————— Follow me at Sean McPheat for more L&D content and and then hit the 🔔 button to stay updated on my future posts. ♻️ Repost to help others in your network. 📃 Want a high-res PDF of this? Visit: https://www.seanmcpheat for this and 250 other infographics.

 I use this Framework to present my PBI projects (and win high-value clients) A lot of analysts start their presentations like this - “This dashboard has 19 visuals, 4 data sources, and custom DAX measures.” That’s not how you impress decision-makers. Because leaders don’t care how many charts you built. They care about What problem did you solved. Here’s a simple 4-step framework I use called LEAD to explain my work clearly and make it valuable for business leaders. 1️⃣ L - Landscape Start by setting the context. What business problem were you solving, and why did it matter? Example: |“The sales team used to manually combine data from 5 sources every week. It delayed insights by 2–3 weeks and led to lost sales worth $50,000.”| Once the listener understands the pain and its cost, you’ve got their full attention. 2️⃣ E - Essentials Then, talk about the metrics that matter. Don’t show every number you can calculate,  show the ones that truly move the business. I usually break it down like this:  • North Star: the main goal.   The one number the business is trying to improve, say revenue, customer retention, or MRR. It gives direction to everything else.  • Drivers: what moves that goal.  These are the levers: new customers, churn, repeat sales, expansion revenue. If these move, your North Star moves too.  • Diagnostics: what explains the drivers. These are the clues - complaints, usage patterns, response times, conversion rates. They tell you why something went up or down. When you structure your metrics this way, your report becomes more than a dashboard; it becomes a decision-making system. 3️⃣ A - Architecture Now explain how you solved it, but with a business context,  not just tool talk. Example: |“The model was slow because we had 7 years of data. Since decisions only need the last 2 years, we built a rolling model. The refresh went from 7 minutes to 2, and the report runs 4× faster.”| That’s how you show technical depth and practical thinking. 4️⃣ D - Design Design comes last, not first. Start with the problem, then metrics, then logic and then visuals. I follow four small design rules -  • Contrast: make the key numbers stand out  • Repetition: use consistent styles  • Alignment: nothing should float randomly  • Proximity: keep related visuals close together A simple, well-aligned report beats a colourful one any day. The point - When you show your Power BI work, don’t start with the visuals. Start with the thinking behind it. The best dashboards aren’t impressive because they’re fancy, They’re impressive because they solve real problems faster and better. I recently explained this entire LEAD framework step-by-step in my latest video - https://lnkd.in/giqr_Sam

I really appreciated the following study, which evaluated the efficacy of a culturally adapted cognitive behavioural therapy for depression in the United Arab Emirates and the way they reported it. Not only was this paper strong evidence for the use of CBT in such contexts, but it also provided substantial information about how they adapted the modality and the depth of consideration of what needed to change. https://lnkd.in/gm4HZMP4 Recognizing the importance of culture, they ensured that therapists received training on the cultural values, family dynamics, and religious beliefs of their sample communities (Arab and Filipino), and the intervention explicitly acknowledged and integrated these aspects. For example, automatic negative thoughts were discussed within religiously familiar contexts to help them reframe seeking help as an act compatible with their religious principles. Therapists also explored issues related to family honor and helped them see how therapy could be viewed as a tool that would allow them to restore family well-being and achieve their other personal goals. Culturally relevant examples, metaphors, and stories were also used, and even the activities suggested for common CBT elements, such as behavioural activation, were adjusted to include tasks with which participants would be familiar. Lastly, and perhaps my favourite part of the modification was the inclusion of family members as people who could help support the psychoeducation process while also teaching them how to foster a supportive home environment. While the control group was only a treatment-as-usual one, this is a significant step toward demonstrating how culturally oriented CBT can make a difference, as the CBT group had significantly lower depressive symptoms after the intervention period. However, I would have appreciated it if they had compared it with an unadapted version of CBT to tease out further the differences introduced by the cultural adaptations. Nevertheless, this was an interesting read, and I highly encourage people to consider how CBT can be adapted, especially across different cultural contexts. #mentalhealth #psychology #psychiatry #wellness #mentalillness