Production AI

The questions GenAI interviewers actually ask — with handwritten explanations, diagrams and code for every answer.

End-to-end retrieval-augmented generation: chunking strategies, embeddings, hybrid search, reranking and evaluation — with full pipeline diagrams.

Master LLM fundamentals, Prompt Engineering and API integration with 14 pages of structured handwritten revision notes. Topics Covered: • LLM Inference & Tokenization • Prompt Engineering Framework • Zero-shot, Few-shot & Advanced Prompting • System Instructions & Context Management • Temperature, Top-P & Generation Parameters • LLM API Architecture & Python Integration • Structured Outputs & JSON • Tool Calling & Function Integration • API Reliability, Retries & Error Handling • Security & Prompt Injection • LLM Evaluation, Cost & Optimization Includes: Visual diagrams, practical Python examples, API workflows, important concepts and rapid revision cheat sheets. Ideal For: AI Engineers, Python Developers, LLM Developers and Generative AI Learners. Format: PDF | 14 Pages | English

Everything a working developer needs in Python for AI: environments, NumPy, data pipelines, notebook workflows and the libraries the AI stack actually uses.

Tool use, planning loops, memory and multi-agent orchestration — a developer's field guide to building reliable agentic systems.

Learn embeddings, vector search and vector databases through clear handwritten notes. Understand similarity metrics, HNSW, IVF, hybrid search, reranking and production retrieval workflows. Ideal for AI Engineers, RAG Developers and LLM learners.

Explore Transformer Architecture through clear and well-structured handwritten notes. Understand self-attention, multi-head attention, transformer blocks and tensor shapes with step-by-step explanations and visual diagrams. Ideal for Deep Learning students, LLM developers and aspiring AI Engineers.

Build a strong foundation in Artificial Intelligence and Machine Learning with clear, structured handwritten notes. Learn core concepts, ML algorithms, model evaluation, Deep Learning, LLMs and practical AI Engineering workflows.

Master AI Agents, Tool Calling and Agentic Design Patterns through structured handwritten notes. Learn ReAct, planning, multi-agent systems, MCP, memory, guardrails and production reliability. Designed for AI Engineers, LLM Developers and Generative AI learners.

Learn to build stateful AI workflows and intelligent agents using LangGraph. Explore graph architecture, state management, conditional routing, agent loops, multi-agent systems, memory and production workflows. Ideal for AI Engineers, LangChain Developers and LLM Application Developers.

Master Natural Language Processing with structured handwritten notes covering text processing, linguistics, machine learning, Transformers, LLMs and practical NLP applications. Ideal for AI/ML students, NLP developers and aspiring AI Engineers.

Learn how to build, evaluate, secure and deploy reliable AI systems in production. Covers LLM evaluation, AI security, observability, deployment architecture, MLOps, scaling and production reliability. Designed for AI Engineers, LLM Developers and Production AI learners.

Master Neural Networks and Deep Learning fundamentals with 19 pages of structured handwritten revision notes. Topics Covered: • Neural Network Architecture & Forward Propagation • Activation Functions & Loss Functions • Backpropagation & Gradient Descent • Optimizers & Weight Initialization • Batch Normalization & Regularization • Overfitting & Training Diagnostics • Neural Network Architectures (CNN, RNN, LSTM, Transformer) • PyTorch Implementation • Model Evaluation & Error Analysis • Scaling, Efficiency & Deployment • Advanced Interview Questions & Revision Includes: Visual diagrams, important formulas, practical examples, PyTorch code, interview questions and revision cheat sheets. Ideal For: AI/ML Students, Deep Learning Learners, Python Developers and Aspiring AI Engineers. Format: PDF | Pages: 19 | Language: English

The transformer block, self-attention and multi-head attention drawn and annotated page by page — the clearest way to internalise the architecture behind modern LLMs.

Prepare for Applied AI Engineer interviews with structured handwritten notes covering Python, ML, Deep Learning, LLMs, RAG, AI Agents, Production AI and System Design. Includes technical interview questions, coding concepts, real-world engineering scenarios and rapid revision.

Master Advanced Retrieval-Augmented Generation (RAG) concepts with 16 pages of structured handwritten AI Engineering notes. Topics Covered: • Advanced RAG Architecture & Mental Models • Document Ingestion, Chunking & Indexing • Embeddings, Vector Search & HNSW • Hybrid Retrieval & Reranking • Query Transformation & Routing • Context Assembly & Prompt Grounding • Generation Quality & Hallucination Control • Multi-Hop, Graph, Agentic & Iterative RAG • RAG Evaluation & Debugging • Performance Engineering & Optimization • Security, Governance & Production RAG Includes: Architecture diagrams, workflows, evaluation metrics, practical engineering examples, interview questions and rapid revision sheets. Ideal For: AI Engineers, LLM Developers, Python Developers and learners with basic RAG knowledge. Format: PDF | Pages: 16 | Language: English

Learn Retrieval-Augmented Generation (RAG) from scratch with 12 pages of structured handwritten notes. Topics Covered: • RAG Fundamentals & Architecture • Document Loading & Chunking • Embeddings & Vector Storage • Retrieval & Ranking • Prompt Construction & Generation • Citations, Hallucinations & Abstention • Basic RAG Implementation • RAG Evaluation & Debugging • RAG Security & Production Includes: Visual diagrams, workflows, practical AI engineering examples and a rapid revision cheat sheet. Ideal For: Beginners, Python developers, AI/ML students and aspiring AI Engineers. Format: PDF | Pages: 12 | Language: English

Master Transformer Architecture and Attention Mechanisms with 16 pages of handwritten advanced revision notes. Topics Covered: • Transformer Architecture & Mental Model • Tokens, Embeddings & Positional Encoding • Query, Key & Value (QKV) • Scaled Dot-Product Attention • KV Cache & Inference Optimization • Multi-Head Attention (MHA) • Transformer Blocks, Residuals & FFN • Encoder, Decoder & Model Families • Attention Masks & Causal Attention • Training vs Generation • Advanced Attention Variants (MHA, MQA, GQA) • Complexity, Performance & FlashAttention • Technical Interview Questions Includes: Visual diagrams, key formulas, practical examples, interview preparation and rapid revision cheat sheets. Format: PDF | 16 Pages | English

Master Python programming for AI Engineering with these 17 pages of handwritten revision notes. Topics Covered: • Python Fundamentals & Data Structures • Functions, OOP & Exception Handling • Iterators, Generators & Decorators • NumPy & Pandas • APIs, Async Python & AI Integration • Testing, Debugging & Best Practices Includes: Practical code examples, visual diagrams, quick revision sheet and final learning checklist. Perfect for: Python learners, AI/ML students and aspiring AI Engineers. Format: PDF | Pages: 17 | Language: English