AI KNOWLEDGE CENTRE

Understanding ‘harnessing’ in LLM working

Understanding ‘harnessing’ in LLM working Introduction People often say that an organisation should “harness AI”, meaning that it should put AI to…

Technical aspects of “AI existential dread”

Technical aspects of “AI existential dread” How to handle the torrent of scary news Introduction “AI existential dread” is not a recognised…

How LLMs learn continuously, or not

How LLMs learn continuously, or not What are training runs? What happens in-between? Introduction Large language models create an unusually convincing illusion…

Copyright tangles in LLM generations: Text versus Images

Copyright tangles in LLM generations: Text versus Images Models are different, so are the copyright claims Introduction Generative AI has made copyright…

Latest LLMs from leading vendors: September 2026 beginner’s guide

Latest LLMs from leading vendors: September 2026 beginner’s guide How LLM landscape is changing rapidly Introduction Following large language models has become…

AI Regulation and Compliance Global Landscape

AI Regulation and Compliance Global Landscape EU AI Act, US guidance, India’s AI policy direction, sector-specific compliance, and audit readiness. Introduction Artificial…

AI Governance Frameworks for Organizations

AI Governance Frameworks for Organizations Risk classification, accountability, documentation, model cards, human oversight, and responsible deployment. Introduction Artificial intelligence has moved rapidly…

Privacy-Preserving AI and Federated Learning

Privacy-Preserving AI and Federated Learning Federated learning, differential privacy, secure computation, confidential computing, and enterprise privacy. Introduction Artificial intelligence has historically been…

Guardrails, Policy Engines, and Safe AI Deployment

Guardrails, Policy Engines, and Safe AI Deployment AI Safety, LLMs, Enterprise AI; Content filters, permissioning, refusal design, data leakage prevention, audit trails,…

Prompt Engineering to Context Engineering

Prompt Engineering to Context Engineering Moving beyond prompts into context design, memory, tools, retrieval, guardrails, and workflow architecture. Introduction For the first…

Evaluation of LLM Applications in the real world

Evaluation of LLM Applications in the real world Task-based evaluation, human review, golden datasets, regression testing, red-teaming, business KPIs Introduction A large…

AI Observability and Monitoring

AI Observability and Monitoring Tracking hallucinations, latency, cost, safety, drift, user feedback, and model degradation Introduction Artificial intelligence systems do not behave…

MLOps and LLMOps Managing AI in Production

MLOps and LLMOps Managing AI in Production Model deployment, monitoring, versioning, rollback, observability, governance, and cost management Introduction Building an AI model…

Data-Centric AI Improving Models by Improving Data

Data-Centric AI Improving Models by Improving Data Label quality, dataset curation, data versioning, data drift, feedback loops, error analysis. Introduction For many…

Synthetic Data Generation for AI Training and Evaluation

Synthetic Data Generation for AI Training and Evaluation Synthetic text, images, tabular data, data augmentation, privacy, bias, and quality control Introduction Synthetic…

Knowledge Graphs for modern AI systems

Knowledge Graphs for modern AI systems Entities, relationships, ontologies, semantic search, Graph RAG, reasoning over structured knowledge. Introduction The AI landscape is…

RAG beyond basics – Graph RAG, Hybrid Search, and Enterprise Knowledge Systems

RAG beyond basics – Graph RAG, Hybrid Search, and Enterprise Knowledge Systems Combining vector search, keyword search, knowledge graphs, metadata, permissions, and…

Planning and Reasoning in AI Agents

Planning and Reasoning in AI Agents Chain-of-thought alternatives, tree search, task decomposition, self-correction, planning under uncertainty Introduction AI agents are becoming one…

Tool Use, Function Calling, and API-Oriented AI

Tool Use, Function Calling, and API-Oriented AI How LLMs interact with databases, APIs, calculators, CRMs, ERPs, search engines, and enterprise tools. Introduction…

AI Agents in Business Processes

AI Agents in Business Processes How agents can automate sales, support, finance, HR, operations, research, and compliance workflows. Introduction Artificial intelligence is…

Multi-Agent Systems and Agent Collaboration

Multi-Agent Systems and Agent Collaboration Agent teams, role specialization, negotiation, coordination, debate, swarm intelligence   Introduction Artificial intelligence is moving from single…

Agentic AI System Design From Tools to Autonomous Workflows

Agentic AI System Design From Tools to Autonomous Workflows Tool use, planning, memory, reflection, multi-step execution, workflow orchestration. Introduction Agentic AI is…

Diffusion Models vs Autoregressive Models (Unified View)

Diffusion Models vs Autoregressive Models (Unified View) Score-based models, Generative paradigm comparison Introduction Generative AI in 2026 is no longer a story…

World Models & Latent Space Planning

World Models & Latent Space Planning MuZero, Dreamer, Simulation-based reasoning Introduction Artificial Intelligence becomes truly powerful when it can do more than…

State Space Models (S4, Mamba) & Sequence Alternatives

State Space Models (S4, Mamba) & Sequence Alternatives Linear-time sequence modelling; Transformer alternatives Introduction Modern artificial intelligence systems often need to understand…

Mixture of Experts (MoE) Architectures

Mixture of Experts (MoE) Architectures Sparse routing; Conditional computation Introduction Mixture of Experts, or MoE, is one of the most important architectural…

Quantization & Model Compression Techniques

Quantization & Model Compression Techniques INT8, 4-bit, Pruning, Distillation Introduction Modern AI models are powerful, but they are also heavy. Large language…

LLM Inference Optimization & Serving Systems

LLM Inference Optimization & Serving Systems KV cache, Batching, Latency vs Throughput Introduction Large Language Models have become the foundation of modern…

Memory Mechanisms in LLMs

Memory Mechanisms in LLMs Context windows vs external memory, Retrieval vs compression Introduction Large Language Models are often described as intelligent systems…

Alignment Algorithms RLHF, DPO, and Beyond

Alignment Algorithms RLHF, DPO, and Beyond Reward models, Preference optimization Introduction As large language models (LLMs) scale in capability, the central challenge…

Decoding Strategies in Generative Models

Decoding Strategies in Generative Models Top-k, top-p, beam search; Temperature scaling Introduction Generative models, especially modern large language models, produce text one…

In-Context Learning & Emergent Behaviour

In-Context Learning & Emergent Behaviour Few-shot learning, Emergence vs memorization Introduction Modern AI systems – especially large language models (LLMs) – have…

Training Stability & Gradient Dynamics

Training Stability & Gradient Dynamics Gradient explosion/vanishing, Initialization, normalization Introduction Training deep neural networks is not just about choosing the right architecture…

Self-Supervised Learning Paradigms

Self-Supervised Learning Paradigms Contrastive learning, Masked modelling Introduction Self-Supervised Learning (SSL) has emerged as one of the most powerful paradigms in modern…

Bayesian Machine Learning & Probabilistic AI

Bayesian Machine Learning & Probabilistic AI Bayesian inference, Uncertainty estimation Introduction In the evolving landscape of artificial intelligence, one of the most…

Scaling Laws & Compute-Optimal Training

Scaling Laws & Compute-Optimal Training Chinchilla scaling; Data vs model trade-offs Introduction Over the past decade, the rapid advancement of artificial intelligence…

Optimization Landscapes in Deep Learning

Optimization Landscapes in Deep Learning Loss surfaces, saddle points; Sharp vs flat minima Introduction Deep learning models have achieved remarkable success across…

Positional Encoding & Sequence Representation

Positional Encoding & Sequence Representation Absolute vs relative encoding; RoPE, ALiBi Introduction Modern transformer models, such as those used in natural language…

Attention Mechanisms Variants & Optimization

Attention Mechanisms Variants & Optimization Flash attention, sparse attention, memory-efficient attention Introduction The concept of attention mechanisms revolutionized modern AI, particularly after…

Embedding Spaces & Representation Geometry

Embedding Spaces & Representation Geometry Semantic structure of vector spaces, Anisotropy, clustering Introduction Modern AI systems – especially those built on deep…

Tokenization Algorithms & their impact on Learning

Tokenization Algorithms & their impact on Learning BPE, WordPiece, SentencePiece, Token boundaries vs reasoning Introduction In the world of modern AI and…

Mechanistic Interpretability in Transformers

Mechanistic Interpretability in Transformers Circuits, neurons, feature superposition & reverse-engineering model behaviour Introduction Modern AI systems – especially transformer-based models – can…

On-Device AI & Hardware-Aware Optimization Compilation techniques

On-Device AI & Hardware-Aware Optimization Compilation techniques TVMXLA, 4-bitNF4 quantization, and designing models specifically for NPU (Neural Processing Unit) architectures Introduction Artificial…

Continual and lifelong learning solving catastrophic forgetting

Continual and lifelong learning solving catastrophic forgetting How to update a model with new information without losing previously learned skills Introduction Modern…

AI System Security & Adversarial Machine Learning

AI System Security & Adversarial Machine Learning Technical deep-dives into prompt injection, model inversion attacks, and defense mechanisms like adversarial training. Artificial…

Diffusion Models & Generative Modeling Theory

Diffusion Models & Generative Modeling Theory Underlying physics and stochastic differential equations powering image, video, and audio generation Diffusion models have emerged…

Formal Methods and Verification in AI

Formal Methods and Verification in AI Using mathematical proofs to guarantee a model will not violate specific safety constraints Designing powerful AI…

Neural Architecture Search (NAS) & Hyperparameter Optimization

Neural Architecture Search (NAS) & Hyperparameter Optimization Automating the design of neural networks using reinforcement learning and Bayesian optimization. Designing high-performance neural…

Long-Context Engineering & Retrieval-Augmented Generation (RAG)

Long-Context Engineering & Retrieval-Augmented Generation (RAG) Technical implementation of vector databases, semantic chunking, and architectural modifications for infinite context windows Modern LLMs…

Distributed Training & Large-Scale Systems

Distributed Training & Large-Scale Systems The engineering of 3D parallelism (Data, Pipeline, and Tensor), InfiniBand networking, and GPU memory optimization (ZeRODeepSpeed) Modern…

State Space Models (SSMs) & Alternatives to Transformers: Deep diving into the math behind Mamba and S4 architectures

Modern deep learning models for language, vision, and time-series have been dominated by Transformers. Attention mechanisms enabled models to capture global dependencies…

Geometric Deep Learning & Graph Neural Networks (GNNs) – Extending deep learning to non-Euclidean data

Modern deep learning models were built for grids: images, audio spectrograms, and text sequences. These Euclidean structures made convolution and attention powerful…

Human-in-the-Loop Learning and Feedback Systems

Modern AI systems do not learn in isolation. Behind every effective intelligent system lies an ongoing interaction between machines and humans –…

Knowledge Representation and Symbolic Reasoning basics

Knowledge representation and symbolic reasoning sit at the roots of artificial intelligence. Long before large datasets and deep neural networks dominated the…

Robustness Uncertainty and Reliability basics

Robustness, uncertainty, and reliability form the backbone of trustworthy artificial intelligence. As AI systems move from controlled research settings into real-world environments,…

Multimodal learning – basics

Multimodal learning represents a fundamental shift in how artificial intelligence systems perceive and understand the world. Humans naturally integrate information from multiple…

Evaluation, Benchmarks, and Metrics in AI Systems – basics

Evaluation is the backbone of progress and accountability in artificial intelligence. As AI systems grow more complex and influential, understanding how they…

100 AI FAQs

Artificial General Intelligence – basics

1. What AGI and ASI mean – and why they matter Artificial intelligence today is powerful but narrow. Most systems excel at…

RL basics

1. What is reinforcement learning and why it matters Reinforcement Learning (RL) is a branch of machine learning in which an agent…

Robotics – basics

1. What is robotics and why it matters Robotics is the field of designing, building, and operating machines that can perform physical…

AI Agents

1. What are AI Agents and why they matter AI Agents are everywhere: popular social media, mainstream media and public imagination. So…

NLP to LLMs

1. What is NLP and why it matters Natural Language Processing, or NLP, is the field of artificial intelligence focused on enabling…

GENERATIVE AI basics

1. What is Generative AI – and why is it different Generative AI refers to a class of artificial intelligence (AI) systems…

DATA BASICS

DATA BASICS The Foundation of AI, Machine Learning, and Intelligent Systems 1. Why Data is the true starting point of AI There…

DL basics

1. Introduction to Deep Learning (DL): What it is and why it matters Deep Learning (DL) is a specialized branch of machine…

ML basics

1. Introduction to Machine Learning: Definition, Origins, and Why It Matters Machine Learning (ML) is the field of study in which computers…

AI basics

1. Understanding Artificial Intelligence: Definition, Scope, and Evolution Artificial Intelligence (AI) refers to computer systems capable of performing tasks that traditionally require…