{"id":1417,"date":"2025-02-15T14:35:55","date_gmt":"2025-02-15T09:05:55","guid":{"rendered":"https:\/\/www.techandtrain.com\/blog\/?p=1417"},"modified":"2025-10-22T14:50:33","modified_gmt":"2025-10-22T09:20:33","slug":"notes-on-metadata-of-llms-gai-ai-to-tie-accountability-liability-and-more-in-legal-terms-plus-study-its-evolution-and-analyze-part-1","status":"publish","type":"post","link":"https:\/\/www.techandtrain.com\/blog\/2025\/02\/notes-on-metadata-of-llms-gai-ai-to-tie-accountability-liability-and-more-in-legal-terms-plus-study-its-evolution-and-analyze-part-1\/","title":{"rendered":"Notes on metadata of LLMs\/GAI\/AI to tie accountability, liability and more in legal terms plus study it&#8217;s evolution and analyze &#8211; Part 1"},"content":{"rendered":"\n<p id=\"ember2944\">Following up on my thoughts here: <a href=\"https:\/\/www.linkedin.com\/posts\/activity-7296196592175321089-jk1B\">https:\/\/www.linkedin.com\/posts\/activity-7296196592175321089-jk1B<\/a> &#8211; I asked ChatGPT on metadata to achieve my goals as in the post. Here are it&#8217;s inputs. My focus is on legal accountability and evolution along with visualization \/ analysis of this meta-data.<\/p>\n\n\n\n<p id=\"ember2945\">Some notes on metadata for AI\/GAI\/LLM and it&#8217;s legal ramifications for accountability:<\/p>\n\n\n\n<p id=\"ember2946\">Wouldn&#8217;t it be nice if LLMs \/ GAI when giving us an output also give us sources (many of them already give) and with it give us some parameters\/factors\/weights that make the decision explainable \/ interpretable. We can then compare over time how they are evolving, was the decision similar for two different questions in same domain and so on. It can take us to a whole new area of AI which is related to it&#8217;s evolution and analytics if we visualize it and run statistics \/ mathematics \/ time series over it.Just a thought. Food for the next blog.<\/p>\n\n\n\n<p id=\"ember2947\">&#8211;&gt; Tagging: Creative, Factual, etc.<\/p>\n\n\n\n<p id=\"ember2948\">&#8211;&gt; Explainable output of factors: Built from weights of used nodes and connections \/ inputs \/ outputs, etc. like hashing in file integrity<\/p>\n\n\n\n<p id=\"ember2949\">&#8211;&gt; Sources<\/p>\n\n\n\n<p id=\"ember2950\">&#8211;&gt; Notice for copyrights, etc. specific to LLMs like when was it generated and methodology<\/p>\n\n\n\n<p id=\"ember2951\">&#8211;&gt; And so on.<\/p>\n\n\n\n<p id=\"ember2952\">Public meta data for LLMs \/ GAI can be formed this way leading to accountability. This has legal ramifications as well for liability.<\/p>\n\n\n\n<p id=\"ember2953\">Stay informed at:<\/p>\n\n\n\n<p id=\"ember2954\"><a href=\"http:\/\/www.techandtrain.com\/blog\">www.TechAndTrain.com\/blog<\/a><\/p>\n\n\n\n<p id=\"ember2955\"><a href=\"http:\/\/www.harwanisystems.in\/\">www.HarwaniSystems.in<\/a><\/p>\n\n\n\n<p id=\"ember2956\">Additional reference: <a href=\"https:\/\/www.linkedin.com\/pulse\/what-points-can-part-fair-use-disclaimer-generativeai-neil-harwani\/\">https:\/\/www.linkedin.com\/pulse\/what-points-can-part-fair-use-disclaimer-generativeai-neil-harwani\/<\/a><\/p>\n\n\n\n<p id=\"ember2957\">******************<\/p>\n\n\n\n<p id=\"ember2958\">What all can be used as public metadata from LLMs for running statistics, mathematics and time series analysis on that meta data?<\/p>\n\n\n\n<p id=\"ember2959\">Yes! Metadata related to <strong>weights and neural network usage<\/strong> in Large Language Models (LLMs) can provide valuable insights for <strong>statistics, mathematics, and time series analysis<\/strong>. These metadata fields can be collected and analyzed for <strong>model performance optimization, efficiency tracking, interpretability, and trend analysis<\/strong>. Below are some <strong>key metadata categories<\/strong> related to weights and neural network usage.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"ember2960\">1. Model-Specific Metadata<\/h3>\n\n\n\n<p id=\"ember2961\">These fields describe the underlying LLM architecture, useful for <strong>hardware optimization, performance tracking, and statistical modeling<\/strong>.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"ember2962\">A. Model Weights and Training Metadata<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Weight Distribution<\/strong> \u2013 The statistical distribution of weights in each layer.<\/li>\n\n\n\n<li><strong>Weight Changes Over Time<\/strong> \u2013 Updates in weight values between training epochs.<\/li>\n\n\n\n<li><strong>Gradient Magnitude<\/strong> \u2013 The magnitude of gradient updates per layer.<\/li>\n\n\n\n<li><strong>Gradient Variance<\/strong> \u2013 How much the gradient changes between optimization steps.<\/li>\n\n\n\n<li><strong>Layer Activation Patterns<\/strong> \u2013 Which layers contribute most to specific outputs.<\/li>\n\n\n\n<li><strong>Neuron Sparsity<\/strong> \u2013 The percentage of inactive neurons in a layer (helps in pruning analysis).<\/li>\n\n\n\n<li><strong>Loss Function Values<\/strong> \u2013 Tracks how much the model is improving over training.<\/li>\n\n\n\n<li><strong>Optimizer Step Size<\/strong> \u2013 Learning rate adjustments for different training batches.<\/li>\n\n\n\n<li><strong>Batch Norm Statistics<\/strong> \u2013 Mean and variance of activations per batch.<\/li>\n\n\n\n<li><strong>Activation Histograms<\/strong> \u2013 Distributions of activations for each layer.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"ember2964\">B. Compute and Efficiency Metadata<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>FLOP Count (Floating Point Operations)<\/strong> \u2013 Measures the computational cost per forward pass.<\/li>\n\n\n\n<li><strong>Memory Utilization<\/strong> \u2013 GPU\/CPU memory usage at different model layers.<\/li>\n\n\n\n<li><strong>Inference Latency Per Layer<\/strong> \u2013 Breakdown of processing time across neural network layers.<\/li>\n\n\n\n<li><strong>Energy Consumption<\/strong> \u2013 Power used per inference or training step.<\/li>\n\n\n\n<li><strong>Layer-wise Execution Time<\/strong> \u2013 The time spent in each model layer during execution.<\/li>\n\n\n\n<li><strong>Parameter Efficiency<\/strong> \u2013 Ratio of model parameters to output complexity.<\/li>\n\n\n\n<li><strong>Quantization Levels<\/strong> \u2013 Whether weights are stored in FP16, INT8, etc.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"ember2966\">C. Attention and Transformer-Specific Metadata<\/h3>\n\n\n\n<p id=\"ember2967\">For transformer-based LLMs like GPT, BERT, etc.:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Attention Head Weights<\/strong> \u2013 How much each head contributes to different parts of input.<\/li>\n\n\n\n<li><strong>Self-Attention Entropy<\/strong> \u2013 Measures uncertainty in attention weight distribution.<\/li>\n\n\n\n<li><strong>Key-Value Pair Similarity<\/strong> \u2013 Overlap in keys\/values stored in attention mechanism.<\/li>\n\n\n\n<li><strong>Head-wise Attention Patterns<\/strong> \u2013 Tracks which attention heads are more active.<\/li>\n\n\n\n<li><strong>Cross-Layer Dependency<\/strong> \u2013 Interaction between different transformer layers.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"ember2969\">2. Statistical &amp; Time-Series Analysis on Model Weights<\/h3>\n\n\n\n<p id=\"ember2970\">These metadata fields can be used for various analytical techniques:<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"ember2971\">A. Descriptive Statistics<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Mean and Variance of Weights<\/strong> (Layer-wise stability analysis)<\/li>\n\n\n\n<li><strong>Skewness\/Kurtosis of Gradients<\/strong> (Helps in diagnosing exploding\/vanishing gradients)<\/li>\n\n\n\n<li><strong>Neuron Activation Sparsity Analysis<\/strong> (Checking efficiency of different model sections)<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"ember2973\">B. Time-Series Analysis<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Training Loss Convergence Over Time<\/strong><\/li>\n\n\n\n<li><strong>Gradient Evolution Across Batches<\/strong><\/li>\n\n\n\n<li><strong>Weight Drift (Tracking changes in weight distribution per training epoch)<\/strong><\/li>\n\n\n\n<li><strong>Exploding\/Vanishing Gradients Detection<\/strong> using variance over time.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"ember2975\">C. Mathematical Modeling<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Eigenvalue Decomposition of Weights<\/strong> (Understanding layer redundancy)<\/li>\n\n\n\n<li><strong>Singular Value Decomposition (SVD)<\/strong> (Dimension reduction for weight matrices)<\/li>\n\n\n\n<li><strong>Fourier Analysis on Activation Patterns<\/strong> (Detecting oscillatory behaviors)<\/li>\n\n\n\n<li><strong>Fractal Dimension of Weight Updates<\/strong> (Measuring complexity in learning dynamics)<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"ember2977\">D. Machine Learning Applications<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Anomaly Detection in Weights<\/strong> \u2013 Finding unusual weight changes using clustering.<\/li>\n\n\n\n<li><strong>Autoencoder-Based Compression<\/strong> \u2013 Reducing parameter size while maintaining performance.<\/li>\n\n\n\n<li><strong>Reinforcement Learning for Model Optimization<\/strong> \u2013 Adjusting hyperparameters dynamically.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"ember2979\">3. Practical Use Cases<\/h3>\n\n\n\n<p id=\"ember2980\">These metadata fields can be used for:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Neural Network Pruning<\/strong> \u2013 Removing redundant neurons to improve efficiency.<\/li>\n\n\n\n<li><strong>Adaptive Learning Rate Adjustments<\/strong> \u2013 Optimizing gradient descent dynamically.<\/li>\n\n\n\n<li><strong>Low-Rank Factorization<\/strong> \u2013 Reducing large models&#8217; parameter counts while maintaining accuracy.<\/li>\n\n\n\n<li><strong>Efficient Transformer Training<\/strong> \u2013 Identifying under-utilized layers for compression.<\/li>\n\n\n\n<li><strong>Energy-Aware AI Models<\/strong> \u2013 Optimizing AI inference for green computing.<\/li>\n<\/ul>\n\n\n\n<p id=\"ember2982\">Would you like me to generate a <strong>sample dataset or visualization<\/strong> for any of these metadata categories? \ud83d\ude80<\/p>\n\n\n\n<p id=\"ember2983\">*****************<\/p>\n\n\n\n<p id=\"ember2984\">Is there any metadata for weights \/ neural network usage<\/p>\n\n\n\n<p id=\"ember2985\">What all can be used as public metadata from LLMs for running statistics, mathematics and time series analysis on that meta data?<\/p>\n\n\n\n<p id=\"ember2986\">When working with metadata from Large Language Models (LLMs) for running <strong>statistics, mathematics, and time series analysis<\/strong>, you can leverage various public metadata attributes. These metadata fields can be used for analysis depending on the specific application, such as performance evaluation, bias detection, trend analysis, or content summarization.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"ember2987\">1. General Metadata Fields<\/h3>\n\n\n\n<p id=\"ember2988\">These metadata fields can be collected while running an LLM and can be useful for statistical and time series analysis:<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"ember2989\">A. Interaction-Based Metadata<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Timestamps<\/strong> \u2013 When a query was made (useful for time-series analysis).<\/li>\n\n\n\n<li><strong>Response Time<\/strong> \u2013 The time taken by the model to generate a response.<\/li>\n\n\n\n<li><strong>Query Frequency<\/strong> \u2013 Number of queries per unit time (helps in analyzing usage trends).<\/li>\n\n\n\n<li><strong>Latency Metrics<\/strong> \u2013 The delay between request and response.<\/li>\n\n\n\n<li><strong>Session Length<\/strong> \u2013 Number of queries per user session.<\/li>\n\n\n\n<li><strong>Token Count<\/strong> \u2013 Number of tokens in the input and output (useful for computational cost analysis).<\/li>\n\n\n\n<li><strong>Response Length<\/strong> \u2013 Number of words\/characters in responses.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"ember2991\">B. Content-Based Metadata<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Prompt Complexity Score<\/strong> \u2013 Analyzing sentence structure or complexity of input.<\/li>\n\n\n\n<li><strong>Semantic Similarity<\/strong> \u2013 Clustering related queries using NLP techniques.<\/li>\n\n\n\n<li><strong>Sentiment Scores<\/strong> \u2013 Extracting sentiment polarity from responses.<\/li>\n\n\n\n<li><strong>Topic Classification<\/strong> \u2013 Assigning labels to content (useful for trend detection).<\/li>\n\n\n\n<li><strong>Language Distribution<\/strong> \u2013 Tracking language usage in queries.<\/li>\n\n\n\n<li><strong>Named Entity Recognition (NER)<\/strong> \u2013 Extracting proper nouns from responses.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"ember2993\">C. Statistical and Mathematical Metadata<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Query Entropy<\/strong> \u2013 A measure of information content in prompts.<\/li>\n\n\n\n<li><strong>Embedding Vectors<\/strong> \u2013 Vector representations of queries\/responses for clustering.<\/li>\n\n\n\n<li><strong>TF-IDF Scores<\/strong> \u2013 Measuring the importance of words in user inputs.<\/li>\n\n\n\n<li><strong>Lexical Diversity<\/strong> \u2013 Measuring vocabulary richness in inputs\/outputs.<\/li>\n\n\n\n<li><strong>Word Frequency Distribution<\/strong> \u2013 Common words appearing across interactions.<\/li>\n\n\n\n<li><strong>Probability Scores<\/strong> \u2013 Confidence level of model-generated responses.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"ember2995\">2. Statistical &amp; Mathematical Applications<\/h3>\n\n\n\n<p id=\"ember2996\">Using the metadata fields above, you can apply different mathematical techniques:<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"ember2997\">A. Descriptive Statistics<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Mean, Median, Mode (e.g., average token count, average response time)<\/li>\n\n\n\n<li>Variance and Standard Deviation (e.g., fluctuation in query frequency)<\/li>\n\n\n\n<li>Skewness and Kurtosis (e.g., analyzing sentiment distributions)<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"ember2999\">B. Time Series Analysis<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Trend Analysis<\/strong> (tracking token usage over time)<\/li>\n\n\n\n<li><strong>Seasonality Detection<\/strong> (daily\/weekly\/monthly cycles in usage patterns)<\/li>\n\n\n\n<li><strong>Anomaly Detection<\/strong> (spikes in response time or unusual query bursts)<\/li>\n\n\n\n<li><strong>Forecasting<\/strong> (predicting future query volumes)<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"ember3001\">C. Probability &amp; Machine Learning<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Markov Chains<\/strong> (predicting query transitions)<\/li>\n\n\n\n<li><strong>Bayesian Inference<\/strong> (assessing uncertainty in responses)<\/li>\n\n\n\n<li><strong>Regression Analysis<\/strong> (correlating query complexity with response time)<\/li>\n\n\n\n<li><strong>Clustering (K-Means, DBSCAN)<\/strong> (grouping similar queries)<\/li>\n\n\n\n<li><strong>Neural Networks<\/strong> (learning from interaction metadata for better performance)<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"ember3003\">D. Graph &amp; Network Analysis<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Co-Occurrence Networks<\/strong> (linking similar queries based on embeddings)<\/li>\n\n\n\n<li><strong>Graph Centrality Measures<\/strong> (identifying most frequent or influential topics)<\/li>\n\n\n\n<li><strong>Community Detection<\/strong> (finding clusters in user interactions)<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"ember3005\">3. Use Cases of Meta-Data Analytics<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Bias Detection<\/strong> \u2013 Finding disparities in model responses across different groups.<\/li>\n\n\n\n<li><strong>Performance Optimization<\/strong> \u2013 Reducing response time by analyzing slow queries.<\/li>\n\n\n\n<li><strong>Anomaly &amp; Fraud Detection<\/strong> \u2013 Detecting bot-generated queries or spam attacks.<\/li>\n\n\n\n<li><strong>Personalization &amp; Adaptive Learning<\/strong> \u2013 Using embeddings to tailor responses.<\/li>\n\n\n\n<li><strong>Knowledge Graphs<\/strong> \u2013 Building structured representations of recurring queries.<\/li>\n<\/ul>\n\n\n\n<p id=\"ember3007\">Would you like me to generate a sample dataset or visualization based on any of these metadata fields? \ud83d\ude80<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Email me: <a href=\"mailto:Neil@HarwaniSytems.in\">Neil@HarwaniSytems.in<\/a><\/li>\n\n\n\n<li>Website: <a href=\"http:\/\/www.harwanisystems.in\/\">www.HarwaniSystems.in<\/a><\/li>\n\n\n\n<li>Blog: <a href=\"http:\/\/www.techandtrain.com\/blog\">www.TechAndTrain.com\/blog<\/a><\/li>\n\n\n\n<li>LinkedIn: <a href=\"https:\/\/www.linkedin.com\/in\/neil27\/\">Neil Harwani | LinkedIn<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Following up on my thoughts here: https:\/\/www.linkedin.com\/posts\/activity-7296196592175321089-jk1B &#8211; I asked ChatGPT on metadata to achieve my goals as in the [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":1418,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"nf_dc_page":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[144,271],"tags":[432,281,433,415,431],"class_list":["post-1417","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-aiml","category-generative-ai","tag-analysis-of-llm-metadata","tag-gai","tag-legal-accountability","tag-llm","tag-metadata-for-llms-gai"],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/www.techandtrain.com\/blog\/wp-content\/uploads\/2025\/02\/LLMs.jpg?fit=1280%2C853&ssl=1","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p7do02-mR","jetpack-related-posts":[{"id":1383,"url":"https:\/\/www.techandtrain.com\/blog\/2024\/11\/how-can-portals-commerce-have-intelligence-via-llms-gai-chatgpt-gemini-etc-part-1\/","url_meta":{"origin":1417,"position":0},"title":"How can portals &amp; commerce have intelligence via LLMs\/GAI\/ChatGPT\/Gemini\/etc. &#8211; Part 1","author":"Neil Harwani","date":"November 26, 2024","format":false,"excerpt":"Here is my solution template for having intelligence from AI\/GAI\/LLMs in Portals & Commerce - Part 1: Assumptions: Java\/PHP\/Dot Net\/SharePoint\/Liferay\/WordPress\/Drupal type of a portal and \/ or commerce Just like we have HTML - HEAD \/ BODY \/ FOOTER \/ HEADER \/ META TAGS \/ BACKEND LOGIC in Java \/\u2026","rel":"","context":"In &quot;AIML&quot;","block_context":{"text":"AIML","link":"https:\/\/www.techandtrain.com\/blog\/category\/aiml\/"},"img":{"alt_text":"Credits: www.Pixabay.com","src":"https:\/\/i0.wp.com\/www.techandtrain.com\/blog\/wp-content\/uploads\/2024\/11\/web.jpg?fit=1200%2C800&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/www.techandtrain.com\/blog\/wp-content\/uploads\/2024\/11\/web.jpg?fit=1200%2C800&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/www.techandtrain.com\/blog\/wp-content\/uploads\/2024\/11\/web.jpg?fit=1200%2C800&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/www.techandtrain.com\/blog\/wp-content\/uploads\/2024\/11\/web.jpg?fit=1200%2C800&ssl=1&resize=700%2C400 2x, https:\/\/i0.wp.com\/www.techandtrain.com\/blog\/wp-content\/uploads\/2024\/11\/web.jpg?fit=1200%2C800&ssl=1&resize=1050%2C600 3x"},"classes":[]},{"id":1541,"url":"https:\/\/www.techandtrain.com\/blog\/2026\/04\/dimensions-for-artificial-intelligence-genai-llms-deep-learning-neural-networks-data-science-to-ponder-on-part-1-assisted-by-ai-chatgpt\/","url_meta":{"origin":1417,"position":1},"title":"Dimensions for Artificial Intelligence \/ GenAI \/ LLMs \/ Deep Learning \/ Neural Networks \/ Data Science to ponder on &#8211; Part 1-Assisted by AI &#8211; ChatGPT","author":"Neil Harwani","date":"April 17, 2026","format":false,"excerpt":"\ud83e\udde0 1. Model Performance & Quality Beyond accuracy: Precision \/ Recall \/ F1-score ROC-AUC Calibration (probability correctness) Generalization ability Robustness (noise, adversarial inputs) Stability (variance across runs) Overfitting \/ Underfitting control Latency (response time) Throughput (requests per second) \u2696\ufe0f 2. Responsible AI \/ Ethics Along with fairness, bias, explainability, interpretability:\u2026","rel":"","context":"In &quot;AIML&quot;","block_context":{"text":"AIML","link":"https:\/\/www.techandtrain.com\/blog\/category\/aiml\/"},"img":{"alt_text":"Image credit: www.Pixabay.com","src":"https:\/\/i0.wp.com\/www.techandtrain.com\/blog\/wp-content\/uploads\/2026\/04\/Dimensions.jpg?fit=640%2C360&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/www.techandtrain.com\/blog\/wp-content\/uploads\/2026\/04\/Dimensions.jpg?fit=640%2C360&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/www.techandtrain.com\/blog\/wp-content\/uploads\/2026\/04\/Dimensions.jpg?fit=640%2C360&ssl=1&resize=525%2C300 1.5x"},"classes":[]},{"id":1223,"url":"https:\/\/www.techandtrain.com\/blog\/2024\/02\/notes-on-prompt-engineering-part-1-generated-by-chatgpt-bard\/","url_meta":{"origin":1417,"position":2},"title":"Notes on prompt engineering &#8211; Part 1 &#8211; Generated by ChatGPT &amp; BARD","author":"Neil Harwani","date":"February 7, 2024","format":false,"excerpt":"Prompt engineering involves crafting inputs (prompts) to effectively communicate with AI models like GPT-4 to achieve desired outputs. Here are some best practices: 1. Be Specific and Clear: Clearly define what you want from the model. Specific prompts lead to more accurate and relevant responses. For example, instead of asking\u2026","rel":"","context":"In &quot;Prompt Engineering&quot;","block_context":{"text":"Prompt Engineering","link":"https:\/\/www.techandtrain.com\/blog\/category\/prompt-engineering\/"},"img":{"alt_text":"Credits: www.Pixabay.com","src":"https:\/\/i0.wp.com\/www.techandtrain.com\/blog\/wp-content\/uploads\/2024\/02\/ChatGPT.jpg?fit=1200%2C603&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/www.techandtrain.com\/blog\/wp-content\/uploads\/2024\/02\/ChatGPT.jpg?fit=1200%2C603&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/www.techandtrain.com\/blog\/wp-content\/uploads\/2024\/02\/ChatGPT.jpg?fit=1200%2C603&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/www.techandtrain.com\/blog\/wp-content\/uploads\/2024\/02\/ChatGPT.jpg?fit=1200%2C603&ssl=1&resize=700%2C400 2x, https:\/\/i0.wp.com\/www.techandtrain.com\/blog\/wp-content\/uploads\/2024\/02\/ChatGPT.jpg?fit=1200%2C603&ssl=1&resize=1050%2C600 3x"},"classes":[]},{"id":1065,"url":"https:\/\/www.techandtrain.com\/blog\/2023\/05\/what-points-can-be-part-of-fair-use-disclaimer-for-generative-ai-part-1\/","url_meta":{"origin":1417,"position":3},"title":"What points can be part of fair use disclaimer for Generative AI? &#8211; Part 1","author":"Neil Harwani","date":"May 14, 2023","format":false,"excerpt":"As per me, 0.1 version (draft) for What points can be part of fair use disclaimer for Generative AI? - Part 1 would be: Reference that it was taken from GAI Prompt\/s and prompt sequence used for the solution. Which GAI system was used? Was it real time or non-real\u2026","rel":"","context":"In &quot;Generative AI&quot;","block_context":{"text":"Generative AI","link":"https:\/\/www.techandtrain.com\/blog\/category\/generative-ai\/"},"img":{"alt_text":"Credits: www.Pixabay.com","src":"https:\/\/i0.wp.com\/www.techandtrain.com\/blog\/wp-content\/uploads\/2023\/05\/StoryTelling.jpg?fit=1200%2C675&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/www.techandtrain.com\/blog\/wp-content\/uploads\/2023\/05\/StoryTelling.jpg?fit=1200%2C675&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/www.techandtrain.com\/blog\/wp-content\/uploads\/2023\/05\/StoryTelling.jpg?fit=1200%2C675&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/www.techandtrain.com\/blog\/wp-content\/uploads\/2023\/05\/StoryTelling.jpg?fit=1200%2C675&ssl=1&resize=700%2C400 2x, https:\/\/i0.wp.com\/www.techandtrain.com\/blog\/wp-content\/uploads\/2023\/05\/StoryTelling.jpg?fit=1200%2C675&ssl=1&resize=1050%2C600 3x"},"classes":[]},{"id":1080,"url":"https:\/\/www.techandtrain.com\/blog\/2023\/06\/liferay-aiml-generative-ai-part-1\/","url_meta":{"origin":1417,"position":4},"title":"Liferay &amp; AIML \/ Generative AI &#8211; Part 1","author":"Neil Harwani","date":"June 16, 2023","format":false,"excerpt":"What all is possible with Liferay & AIML \/ Generative AI? - Part 1. Liferay being open source we can integrate GAI \/ AIML with Liferay for: Generating content & images in web content section Generating automated blogs, tags, categories and UIUX flows Assistant which could redirect us to relevant\u2026","rel":"","context":"In &quot;AIML&quot;","block_context":{"text":"AIML","link":"https:\/\/www.techandtrain.com\/blog\/category\/aiml\/"},"img":{"alt_text":"Credits: www.Pixabay.com","src":"https:\/\/i0.wp.com\/www.techandtrain.com\/blog\/wp-content\/uploads\/2023\/06\/binary-1536646_1920.jpg?fit=1200%2C800&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/www.techandtrain.com\/blog\/wp-content\/uploads\/2023\/06\/binary-1536646_1920.jpg?fit=1200%2C800&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/www.techandtrain.com\/blog\/wp-content\/uploads\/2023\/06\/binary-1536646_1920.jpg?fit=1200%2C800&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/www.techandtrain.com\/blog\/wp-content\/uploads\/2023\/06\/binary-1536646_1920.jpg?fit=1200%2C800&ssl=1&resize=700%2C400 2x, https:\/\/i0.wp.com\/www.techandtrain.com\/blog\/wp-content\/uploads\/2023\/06\/binary-1536646_1920.jpg?fit=1200%2C800&ssl=1&resize=1050%2C600 3x"},"classes":[]},{"id":1376,"url":"https:\/\/www.techandtrain.com\/blog\/2024\/11\/possibilities-for-ai-powered-features-in-liferay-part-1-via-chatgpt-gemini\/","url_meta":{"origin":1417,"position":5},"title":"Possibilities for AI powered features in Liferay &#8211; Part 1 via ChatGPT &amp; Gemini","author":"Neil Harwani","date":"November 17, 2024","format":false,"excerpt":"A good list on possibilities for AI powered features in Liferay - Part 1 from GAI\/LLMs. Sharing here for everyone's benefit. Came across during talking to ChatGPT and Gemini, very good ideas - some are inbuilt, some need customizations. Possibilities are endless. ******************************** Liferay, a digital experience platform (DXP), integrates\u2026","rel":"","context":"In &quot;AIML&quot;","block_context":{"text":"AIML","link":"https:\/\/www.techandtrain.com\/blog\/category\/aiml\/"},"img":{"alt_text":"Credits: www.Pixabay.com","src":"https:\/\/i0.wp.com\/www.techandtrain.com\/blog\/wp-content\/uploads\/2024\/11\/Mountain.png?fit=1200%2C872&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/www.techandtrain.com\/blog\/wp-content\/uploads\/2024\/11\/Mountain.png?fit=1200%2C872&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/www.techandtrain.com\/blog\/wp-content\/uploads\/2024\/11\/Mountain.png?fit=1200%2C872&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/www.techandtrain.com\/blog\/wp-content\/uploads\/2024\/11\/Mountain.png?fit=1200%2C872&ssl=1&resize=700%2C400 2x, https:\/\/i0.wp.com\/www.techandtrain.com\/blog\/wp-content\/uploads\/2024\/11\/Mountain.png?fit=1200%2C872&ssl=1&resize=1050%2C600 3x"},"classes":[]}],"jetpack_likes_enabled":true,"_links":{"self":[{"href":"https:\/\/www.techandtrain.com\/blog\/wp-json\/wp\/v2\/posts\/1417","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.techandtrain.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.techandtrain.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.techandtrain.com\/blog\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.techandtrain.com\/blog\/wp-json\/wp\/v2\/comments?post=1417"}],"version-history":[{"count":1,"href":"https:\/\/www.techandtrain.com\/blog\/wp-json\/wp\/v2\/posts\/1417\/revisions"}],"predecessor-version":[{"id":1419,"href":"https:\/\/www.techandtrain.com\/blog\/wp-json\/wp\/v2\/posts\/1417\/revisions\/1419"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.techandtrain.com\/blog\/wp-json\/wp\/v2\/media\/1418"}],"wp:attachment":[{"href":"https:\/\/www.techandtrain.com\/blog\/wp-json\/wp\/v2\/media?parent=1417"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.techandtrain.com\/blog\/wp-json\/wp\/v2\/categories?post=1417"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.techandtrain.com\/blog\/wp-json\/wp\/v2\/tags?post=1417"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}