From foundation models, prompt engineering, and RAG to advanced fine-tuning, autonomous agents, distributed inference, safety guardrails, and enterprise platforms.
A complete foundation for Generative AI and Large Language Models, progressing from core concepts to advanced LLM capabilities, training stages, inference, limitations, multimodality, and practical application architecture.
A practical and detailed guide to designing reliable prompts, controlling LLM outputs, generating structured data, using schemas and tool calling, securing prompts, and evaluating prompt quality.
A beginner-friendly to advanced guide to Transformer architecture and the internal mechanics of modern Large Language Models, including attention, token embeddings, causal language modeling, Transformer blocks, and major model architectures.
A practical guide to building knowledge-grounded LLM applications with embeddings, vector search, document chunking, retrieval, reranking, query rewriting, evaluation, security, and production RAG architecture.
An advanced guide to designing high-quality retrieval and agent systems, covering hybrid and graph retrieval, reranking, query routing, agentic RAG, planning, memory, multi-agent architectures, workflow orchestration, evaluation, and production patterns.
A practical guide to orchestrating LLM applications with LangChain and LangGraph, covering chains, runnables, tools, agents, stateful workflows, routing, memory, human approval, multi-agent systems, evaluation, security, and production architecture.
Comprehensive guide on Advanced AI Agents & Computer Use.
A practical guide to multimodal Generative AI covering vision-language models, image understanding, OCR, document intelligence, audio and speech, video understanding, multimodal prompting, multimodal RAG, agents, evaluation, enterprise architectures, and security.
Comprehensive guide on Advanced Multimodal AI.
A practical beginner-to-advanced guide to adapting foundation models with supervised fine-tuning and parameter-efficient methods such as LoRA and QLoRA, including dataset preparation, training, evaluation, failure modes, model selection, deployment, and the tradeoffs between prompting, RAG, and fine-tuning.
Comprehensive guide on Advanced LLM Training.
Comprehensive guide on Post-Training & Alignment for Generative AI.
Comprehensive guide on Knowledge Distillation & Model Compression for Generative AI.
Comprehensive guide on Small Language Models & Edge AI.
Comprehensive guide on LLM Reasoning & Reasoning Models.
Comprehensive guide on Generative AI for Code.
Comprehensive guide on Synthetic Data & Dataset Generation for Generative AI.
A practical guide to evaluating and securing production LLM applications, covering evaluation datasets, quality metrics, hallucination and groundedness, RAG and agent evaluation, guardrails, prompt injection, PII protection, observability, regression testing, versioning, and reliability engineering.
A comprehensive advanced guide to evaluating and benchmarking Generative AI systems, covering evaluation design, golden datasets, automated metrics, LLM-as-a-Judge, human evaluation, RAG and agent evaluation, multimodal benchmarks, statistical analysis, regression testing, production evaluation, and continuous quality improvement.
A practical guide to securing production Generative AI systems, covering prompt injection, indirect attacks, data leakage, PII protection, model and supply-chain security, access control, tenant isolation, auditability, governance, responsible AI, red teaming, and educational-platform safety.
Comprehensive guide on AI Red Teaming & Security Testing.
A practical guide to taking LLM applications from prototype to reliable production systems, covering LLMOps, inference serving, GPU utilization, batching, KV-cache management, caching, routing, observability, deployment, CI/CD, reliability, cost optimization, and enterprise architecture.
A production-oriented guide to GPU architecture and distributed inference for large language models, covering memory planning, batching, KV cache, parallelism, scheduling, model serving, Kubernetes concepts, multimodal inference, profiling, and high-throughput system design.
Comprehensive guide on AI Reliability & SRE.
Comprehensive guide on AI FinOps & Cost Engineering.
A production-oriented guide to building data and evaluation infrastructure for Generative AI, covering ingestion, cleaning, multimodal processing, dataset versioning, data quality, synthetic data, evaluation datasets, automated benchmarking, feedback loops, lineage, governance, and continuous improvement.
A production-oriented guide to designing an AI platform that supports multiple models, RAG systems, agents, evaluation, observability, governance, and educational AI workloads.
Comprehensive guide on Enterprise Generative AI.
Comprehensive guide on AI Research & Production Engineering Patterns.
Comprehensive guide on Future & Research AI Architectures.
A portfolio-focused notebook that combines the Generative AI concepts learned so far into complete applications, including an educational AI tutor, document intelligence system, multimodal assistant, research agent, and enterprise knowledge assistant.
Comprehensive guide on Full Generative AI Capstone.