Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering Topic-Wise Online Mock Tests & Free MCQ Practice
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Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering — Topic-Wise Mock Tests
Expand any module below to start a live timed MCQ exam across 172 Mock Test sets (max 2 free attempts per topic per day).
All Modules & Topics in Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering
How Computers & AI Software Execute, Python 3.12 Setup, VS Code, uv / venv & First Verified Programs
Purpose: To understand and apply How Computers & AI Software Execute, Python 3.12 Setup, VS Code, uv / venv & First Verified Programs in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Start from zero prior coding experience and write clean, well-structured Python programs from Day 1, so your code stays reliable, fast, and easy to maintain in production.
Linux Command Line Navigation, Shell Pipelines, SSH & Professional Git / GitHub PR Workflows
Purpose: To understand and apply Linux Command Line Navigation, Shell Pipelines, SSH & Professional Git / GitHub PR Workflows in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Operate Linux GPU/CPU cloud terminals and manage clean Git branches and pull requests like a pro, so your code stays reliable, fast, and easy to maintain in production.
Python Variables, Primitive Types, String Manipulation, Unicode/UTF-8 Text & Numeric Operators
Purpose: To understand and apply Python Variables, Primitive Types, String Manipulation, Unicode/UTF-8 Text & Numeric Operators in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Process raw text, numbers, and encodings cleanly — the fundamental building blocks of NLP and AI data, so your code stays reliable, fast, and easy to maintain in production.
Control Flow (if/elif/else, match-case), Loops (for/while), Trace Dry-Runs & Logical Problem Solving
Purpose: To understand and apply Control Flow (if/elif/else, match-case), Loops (for/while), Trace Dry-Runs & Logical Problem Solving in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Build rock-solid programming logic through 30+ progressive conditional, loop, and pattern challenges, so your code stays reliable, fast, and easy to maintain in production.
Lists, Tuples, Sets & Dictionaries: Hashing Internals, Slicing, Comprehensions & Memory Complexity
Purpose: To understand and apply Lists, Tuples, Sets & Dictionaries: Hashing Internals, Slicing, Comprehensions & Memory Complexity in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Manipulate nested JSON records, token vocabularies, and metadata maps with O(1) dictionary efficiency, so your code stays reliable, fast, and easy to maintain in production.
Modular Functions, *args / **kwargs, Closures, Decorators, Modules &CSV / JSON / JSONL Streaming I/O
Purpose: To understand and apply Modular Functions, *args / **kwargs, Closures, Decorators, Modules &CSV / JSON / JSONL Streaming I/O in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Read and transform million-line `.jsonl` AI training and evaluation datasets without RAM crashes, so your code stays reliable, fast, and easy to maintain in production.
Complete Object-Oriented Python: Classes, __init__, Properties, Inheritance, Dunder Methods & Dataclasses
Purpose: To understand and apply Complete Object-Oriented Python: Classes, __init__, Properties, Inheritance, Dunder Methods & Dataclasses in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Design clean model wrappers, document loaders, and vector store clients using modular OOP blueprints, so your code stays reliable, fast, and easy to maintain in production.
Exception Handling, Context Managers (with), SOLID Design Principles & Structured Diagnostic Logging
Purpose: To understand and apply Exception Handling, Context Managers (with), SOLID Design Principles & Structured Diagnostic Logging in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Ensure long-running AI ingestion and agent pipelines handle malformed inputs gracefully without crashing, so your code stays reliable, fast, and easy to maintain in production.
Iterators, Lazy Generators (yield), itertools & Memory-Bounded Streaming Data Pipelines
Purpose: To understand and apply Iterators, Lazy Generators (yield), itertools & Memory-Bounded Streaming Data Pipelines in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Stream large document corpora and live LLM token chunks in constant memory, so your code stays reliable, fast, and easy to maintain in production.
Static Type Annotations (typing / Generics / Protocols) & Rust-Powered Pydantic v2 Validation Schemas
Purpose: To understand and apply Static Type Annotations (typing / Generics / Protocols) & Rust-Powered Pydantic v2 Validation Schemas in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Master Pydantic v2 — the backbone of FastAPI, LangChain, LangGraph, MCP, and structured LLM outputs, so your code stays reliable, fast, and easy to maintain in production.
Python Concurrency Deep Dive: Threads vs Multiprocessing vs Asyncio Event Loop, Tasks & Semaphores
Purpose: To understand and apply Python Concurrency Deep Dive: Threads vs Multiprocessing vs Asyncio Event Loop, Tasks & Semaphores in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Fan out 100+ parallel embedding and LLM API calls safely with bounded rate-limit semaphores and timeouts, so your code stays reliable, fast, and easy to maintain in production.
Test-Driven Engineering with Pytest: Fixtures, Parametrization, Asyncio Tests & Deterministic API Mocks
Purpose: To understand and apply Test-Driven Engineering with Pytest: Fixtures, Parametrization, Asyncio Tests & Deterministic API Mocks in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Test complex AI and backend pipelines deterministically in CI without burning live LLM API credits, so your code stays reliable, fast, and easy to maintain in production.
Big-O Time & Space Complexity, Two Pointers, Sliding Window, Prefix Sums & Binary Search
Purpose: To understand and apply Big-O Time & Space Complexity, Two Pointers, Sliding Window, Prefix Sums & Binary Search in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Clear Round-1 algorithmic coding screens at AI startups, unicorns, and global capability centers, so your code stays reliable, fast, and easy to maintain in production.
Linked Lists, Stacks, Deques, Hash Maps, Frequency Counters & Thread-Safe LRU / TTL Cache Design
Purpose: To understand and apply Linked Lists, Stacks, Deques, Hash Maps, Frequency Counters & Thread-Safe LRU / TTL Cache Design in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Implement custom token-window buffers and low-latency in-memory caches from scratch, so your code stays reliable, fast, and easy to maintain in production.
Trees, Binary Search Trees, Heaps (Top-K Beam/Vector Retrieval), Prefix Tries & Backtracking
Purpose: To understand and apply Trees, Binary Search Trees, Heaps (Top-K Beam/Vector Retrieval), Prefix Tries & Backtracking in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Understand the exact heap and trie data structures powering tokenizers, autocomplete, and top-K search, so your code stays reliable, fast, and easy to maintain in production.
Graph Algorithms (BFS, DFS, Topological Sort, Shortest Paths) & Dynamic Programming in Python
Purpose: To understand and apply Graph Algorithms (BFS, DFS, Topological Sort, Shortest Paths) & Dynamic Programming in Python in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Master DAG execution ordering (used directly in LangGraph!) and dynamic programming interview problems, so your code stays reliable, fast, and easy to maintain in production.
Linear Algebra, Vectors, Dot Products, Cosine Similarity, Probability & Gradient Descent Visualized
Purpose: To understand and apply Linear Algebra, Vectors, Dot Products, Cosine Similarity, Probability & Gradient Descent Visualized in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Gain intuitive visual mastery of vectors, matrices, embeddings, and loss optimization without dry proofs, so your code stays reliable, fast, and easy to maintain in production.
High-Speed Numerical Computing with NumPy: Broadcasting, Matrix Multiplication & Vectorized Top-K Search
Purpose: To understand and apply High-Speed Numerical Computing with NumPy: Broadcasting, Matrix Multiplication & Vectorized Top-K Search in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Build a 50,000-vector cosine-similarity search engine in pure vectorized NumPy with zero Python loops, so your code stays reliable, fast, and easy to maintain in production.
Data Wrangling & Exploratory Analytics with Pandas & Polars: Joins, GroupBy, Time-Series & Cleaning
Purpose: To understand and apply Data Wrangling & Exploratory Analytics with Pandas & Polars: Joins, GroupBy, Time-Series & Cleaning in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Clean messy enterprise CSV, Parquet, and SQL datasets into leak-free analytical and AI datasets, so your code stays reliable, fast, and easy to maintain in production.
Classical Machine Learning with Scikit-Learn: Feature Pipelines, Regression, Trees/XGBoost, Clustering & Metrics
Purpose: To understand and apply Classical Machine Learning with Scikit-Learn: Feature Pipelines, Regression, Trees/XGBoost, Clustering & Metrics in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Know when a 2ms XGBoost/classifier beats a 2-second LLM call — and evaluate precision, recall, ROC-AUC, and F1, so your code stays reliable, fast, and easy to maintain in production.
Relational Modeling From Scratch, Normalization & Core SQL (DDL, DML, Constraints, INNER/LEFT JOINs)
Purpose: To understand and apply Relational Modeling From Scratch, Normalization & Core SQL (DDL, DML, Constraints, INNER/LEFT JOINs) in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Master SQL fundamentals from zero so you can query any corporate data warehouse or transactional DB, so your code stays reliable, fast, and easy to maintain in production.
Advanced SQL: Aggregations, CTEs, Window Functions, JSONB Queries & ACID Transaction Isolation
Purpose: To understand and apply Advanced SQL: Aggregations, CTEs, Window Functions, JSONB Queries & ACID Transaction Isolation in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Combine structured SQL analytics with semi-structured JSONB agent metadata inside one database, so your code stays reliable, fast, and easy to maintain in production.
PostgreSQL 16 Internals, B-Tree / GIN Indexing, EXPLAIN ANALYZE Tuning & Async Connection Pooling
Purpose: To understand and apply PostgreSQL 16 Internals, B-Tree / GIN Indexing, EXPLAIN ANALYZE Tuning & Async Connection Pooling in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Optimize slow database queries and prevent async Python microservices from exhausting DB connections, so your code stays reliable, fast, and easy to maintain in production.
PostgreSQL pgvector Mastery: Vector Columns, Cosine/L2/Inner-Product Operators & HNSW / IVFFlat Indexes
Purpose: To understand and apply PostgreSQL pgvector Mastery: Vector Columns, Cosine/L2/Inner-Product Operators & HNSW / IVFFlat Indexes in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Turn PostgreSQL 16 into an ACID-compliant enterprise vector database unifying SQL RBAC + AI search, so your code stays reliable, fast, and easy to maintain in production.
Web Protocols (HTTP/HTTPS, REST, SSE, WebSockets, CORS), Semantic HTML5 & Accessible UI Landmarks
Purpose: To understand and apply Web Protocols (HTTP/HTTPS, REST, SSE, WebSockets, CORS), Semantic HTML5 & Accessible UI Landmarks in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Understand browser-to-AI-gateway network streaming protocols and semantic document structure, so your code stays reliable, fast, and easy to maintain in production.
Modern CSS3 Box Model, Flexbox, CSS Grid, Dark/Light Themes & Tailwind CSS Responsive Design
Purpose: To understand and apply Modern CSS3 Box Model, Flexbox, CSS Grid, Dark/Light Themes & Tailwind CSS Responsive Design in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Style sleek, executive-grade AI chat, document-copilot, and analytics interfaces effortlessly, so your code stays reliable, fast, and easy to maintain in production.
Core JavaScript From Zero: Variables, Closures, Arrays, Objects, ES Modules & Async Promises / Fetch
Purpose: To understand and apply Core JavaScript From Zero: Variables, Closures, Arrays, Objects, ES Modules & Async Promises / Fetch in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Gain complete JavaScript fluency to build interactive browser logic and consume streaming AI APIs, so your code stays reliable, fast, and easy to maintain in production.
DOM Events, ReadableStream SSE Parsing, Markdown/Code Syntax Rendering & Responsive Split-Pane Layouts
Purpose: To understand and apply DOM Events, ReadableStream SSE Parsing, Markdown/Code Syntax Rendering & Responsive Split-Pane Layouts in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Render token-by-token streaming markdown, tables, and cited code blocks smoothly at 60fps, so your code stays reliable, fast, and easy to maintain in production.
TypeScript 5 Fundamentals, Generics, Discriminated Unions (Message/Tool States) & Zod Validation
Purpose: To understand and apply TypeScript 5 Fundamentals, Generics, Discriminated Unions (Message/Tool States) & Zod Validation in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Model multi-turn chat, tool-invocation cards, and citation states with 100% compile-time type safety, so your code stays reliable, fast, and easy to maintain in production.
React 19 Core: Components, Hooks (useState, useEffect, useRef, useOptimistic), Forms & Custom Hooks
Purpose: To understand and apply React 19 Core: Components, Hooks (useState, useEffect, useRef, useOptimistic), Forms & Custom Hooks in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Build modular React 19 workspaces with clean abortable streaming hooks and zero render flicker, so your code stays reliable, fast, and easy to maintain in production.
Protected RBAC Routing, Zustand Workspace State & TanStack Query Server-State Synchronization
Purpose: To understand and apply Protected RBAC Routing, Zustand Workspace State & TanStack Query Server-State Synchronization in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Manage conversation histories, knowledge-base uploads, and multi-tenant admin dashboards cleanly, so your code stays reliable, fast, and easy to maintain in production.
Generative UI Components: Interactive Tool Approval Cards, PDF Citation Highlighters & Vitest Suites
Purpose: To understand and apply Generative UI Components: Interactive Tool Approval Cards, PDF Citation Highlighters & Vitest Suites in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Move beyond plain chat boxes by rendering interactive charts, diffs, and HITL approval widgets in React, so your code stays reliable, fast, and easy to maintain in production.
Django 5 Architecture, ORM Models, Zero-Downtime Migrations, select_related / prefetch_related & Admin
Purpose: To understand and apply Django 5 Architecture, ORM Models, Zero-Downtime Migrations, select_related / prefetch_related & Admin in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Build the rock-solid multi-tenant SaaS user, billing, and workspace management backbone in Django 5, so your code stays reliable, fast, and easy to maintain in production.
Django REST Framework (DRF), Serializers, ViewSets, Rotating JWT / OAuth2 SSO & Multi-Tenant RBAC
Purpose: To understand and apply Django REST Framework (DRF), Serializers, ViewSets, Rotating JWT / OAuth2 SSO & Multi-Tenant RBAC in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Enforce organization roles, API keys, and per-seat token quotas across enterprise customers, so your code stays reliable, fast, and easy to maintain in production.
Redis 7 Caching, Sliding-Window Rate Limiters & Distributed Celery Background Ingestion Pipelines
Purpose: To understand and apply Redis 7 Caching, Sliding-Window Rate Limiters & Distributed Celery Background Ingestion Pipelines in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Process 500-page PDF uploads, OCR, chunking, and embedding generation asynchronously via Celery + Redis, so your code stays reliable, fast, and easy to maintain in production.
Transactional Webhooks (Razorpay/Stripe Usage Billing), Audit Logs & pytest-django API Verification
Purpose: To understand and apply Transactional Webhooks (Razorpay/Stripe Usage Billing), Audit Logs & pytest-django API Verification in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Meter token/credit consumption accurately and verify every SaaS permission boundary with automated tests, so your code stays reliable, fast, and easy to maintain in production.
FastAPI Internals, ASGI Event Loop, Pydantic v2 Contracts, Dependency Injection & Lifespan Warmup
Purpose: To understand and apply FastAPI Internals, ASGI Event Loop, Pydantic v2 Contracts, Dependency Injection & Lifespan Warmup in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Serve ultra-low-latency async Python inference and RAG gateways capable of thousands of concurrent streams, so your code stays reliable, fast, and easy to maintain in production.
Async SQLAlchemy 2.0, Alembic Migrations, Non-Blocking pgvector Queries & Connection Pool Sizing
Purpose: To understand and apply Async SQLAlchemy 2.0, Alembic Migrations, Non-Blocking pgvector Queries & Connection Pool Sizing in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Execute async relational + vector queries without ever blocking the Uvicorn event loop, so your code stays reliable, fast, and easy to maintain in production.
Server-Sent Events (SSE) Token Streaming, Bidirectional WebSockets, Client Disconnect Cancellation & Backpressure
Purpose: To understand and apply Server-Sent Events (SSE) Token Streaming, Bidirectional WebSockets, Client Disconnect Cancellation & Backpressure in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Stop upstream LLM token generation immediately when a user clicks "Stop Generating" to save 40%+ compute, so your code stays reliable, fast, and easy to maintain in production.
API Key / JWT Auth Scopes, Per-Tenant Token Rate Limiting & Async Load Testing (httpx + Locust/k6)
Purpose: To understand and apply API Key / JWT Auth Scopes, Per-Tenant Token Rate Limiting & Async Load Testing (httpx + Locust/k6) in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Harden your AI gateway against abuse and benchmark concurrent streaming throughput scientifically, so your code stays reliable, fast, and easy to maintain in production.
Neural Networks From Scratch: Perceptrons, Activations (ReLU, GELU, SiLU), Loss Functions & Backpropagation
Purpose: To understand and apply Neural Networks From Scratch: Perceptrons, Activations (ReLU, GELU, SiLU), Loss Functions & Backpropagation in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Understand forward passes, gradients, overfitting, and regularization from first principles, so your code stays reliable, fast, and easy to maintain in production.
PyTorch Mastery: Tensors, GPU/CUDA Acceleration, Autograd, nn.Module, DataLoaders & Mixed-Precision Training
Purpose: To understand and apply PyTorch Mastery: Tensors, GPU/CUDA Acceleration, Autograd, nn.Module, DataLoaders & Mixed-Precision Training in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Write clean PyTorch training and evaluation loops on CPU and GPU hardware, so your code stays reliable, fast, and easy to maintain in production.
Tokenization (BPE / Tiktoken), Learned Embeddings, Positional Encoding (RoPE) & Self-Attention Math
Purpose: To understand and apply Tokenization (BPE / Tiktoken), Learned Embeddings, Positional Encoding (RoPE) & Self-Attention Math in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Implement Multi-Head Causal Self-Attention in PyTorch so LLMs are never a mysterious black box to you, so your code stays reliable, fast, and easy to maintain in production.
Decoder-Only vs Encoder Architectures, KV-Cache Memory Math, Context Windows, FlashAttention & MoE Models
Purpose: To understand and apply Decoder-Only vs Encoder Architectures, KV-Cache Memory Math, Context Windows, FlashAttention & MoE Models in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Calculate exact GPU VRAM requirements and understand why KV-cache governs long-context LLM latency, so your code stays reliable, fast, and easy to maintain in production.
Decoding Strategies (Greedy, Temperature, Top-K, Top-P), Logprobs, System Prompts & Context Caching
Purpose: To understand and apply Decoding Strategies (Greedy, Temperature, Top-K, Top-P), Logprobs, System Prompts & Context Caching in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Control determinism vs creativity precisely and slash repeated system-prompt costs by up to 75%, so your code stays reliable, fast, and easy to maintain in production.
Production Prompt Engineering: Few-Shot Exemplars, Chain-of-Thought, Decomposition & XML/Delimiter Guards
Purpose: To understand and apply Production Prompt Engineering: Few-Shot Exemplars, Chain-of-Thought, Decomposition & XML/Delimiter Guards in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Author versioned, testable enterprise prompt templates resilient against ambiguous user inputs, so your code stays reliable, fast, and easy to maintain in production.
Constrained Decoding & Guaranteed Structured Outputs (JSON Schema / Pydantic / Instructor) for Extraction
Purpose: To understand and apply Constrained Decoding & Guaranteed Structured Outputs (JSON Schema / Pydantic / Instructor) for Extraction in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Extract 100% schema-valid invoices, resumes, medical entities, and SQL filters from unstructured text, so your code stays reliable, fast, and easy to maintain in production.
Multimodal Vision, Document Layout OCR & Speech-to-Text / Text-to-Speech Pipelines in Production
Purpose: To understand and apply Multimodal Vision, Document Layout OCR & Speech-to-Text / Text-to-Speech Pipelines in Production in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Build unified pipelines that reason over charts, scanned PDFs, screenshots, and spoken audio, so your code stays reliable, fast, and easy to maintain in production.
Dense vs Sparse Embedding Models, Dimensionality / Matryoshka Trade-Offs & Domain Embedding Selection
Purpose: To understand and apply Dense vs Sparse Embedding Models, Dimensionality / Matryoshka Trade-Offs & Domain Embedding Selection in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Benchmark open-weight vs commercial embedding models on recall, latency, and storage footprint, so your code stays reliable, fast, and easy to maintain in production.
Enterprise Ingestion: PDF/HTML/Code Parsing, Fixed vs Recursive vs Semantic vs Parent-Child Chunking
Purpose: To understand and apply Enterprise Ingestion: PDF/HTML/Code Parsing, Fixed vs Recursive vs Semantic vs Parent-Child Chunking in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Eliminate broken mid-sentence/mid-table chunks — the #1 silent cause of poor RAG answer quality, so your code stays reliable, fast, and easy to maintain in production.
Vector Databases in Depth: PostgreSQL pgvector & Qdrant Collections, HNSW Tuning, Payload Indexes & Quantization
Purpose: To understand and apply Vector Databases in Depth: PostgreSQL pgvector & Qdrant Collections, HNSW Tuning, Payload Indexes & Quantization in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Tune HNSW ef_search/m parameters and scalar/binary quantization for sub-15ms million-vector queries, so your code stays reliable, fast, and easy to maintain in production.
End-to-End Grounded RAG Pipeline: Metadata RBAC Pre-Filtering, Inline Source Citations & "I Don’t Know" Refusals
Purpose: To understand and apply End-to-End Grounded RAG Pipeline: Metadata RBAC Pre-Filtering, Inline Source Citations & "I Don’t Know" Refusals in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Guarantee every generated claim cites an authorized source chunk or cleanly abstains, so your code stays reliable, fast, and easy to maintain in production.
Hybrid Retrieval Architecture: BM25 / SPLADE Lexical Search + Dense Vectors with Reciprocal Rank Fusion (RRF)
Purpose: To understand and apply Hybrid Retrieval Architecture: BM25 / SPLADE Lexical Search + Dense Vectors with Reciprocal Rank Fusion (RRF) in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Combine exact part-number/code matching with semantic meaning for superior enterprise accuracy, so your code stays reliable, fast, and easy to maintain in production.
Query Transformation (Multi-Query, Step-Back, HyDE), Text-to-SQL Routing & Cross-Encoder Rerankers
Purpose: To understand and apply Query Transformation (Multi-Query, Step-Back, HyDE), Text-to-SQL Routing & Cross-Encoder Rerankers in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Re-score top-50 candidates down to the golden top-5 chunks using Cohere / BGE cross-encoders, so your code stays reliable, fast, and easy to maintain in production.
Corrective RAG (CRAG), Self-RAG & Knowledge-Graph Enhanced Retrieval (GraphRAG) for Multi-Hop Questions
Purpose: To understand and apply Corrective RAG (CRAG), Self-RAG & Knowledge-Graph Enhanced Retrieval (GraphRAG) for Multi-Hop Questions in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Answer complex cross-document synthesis questions that single-chunk vector search misses, so your code stays reliable, fast, and easy to maintain in production.
Golden Evaluation Datasets, LLM-as-a-Judge & Automated RAGAS CI Gates (Faithfulness, Recall, Precision)
Purpose: To understand and apply Golden Evaluation Datasets, LLM-as-a-Judge & Automated RAGAS CI Gates (Faithfulness, Recall, Precision) in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Fail GitHub pull requests automatically if a prompt or chunker change drops RAG faithfulness below 92%, so your code stays reliable, fast, and easy to maintain in production.
Agentic Design Patterns From Scratch: ReAct, Plan-and-Execute, Reflection, Routing & Parallel Fan-Out
Purpose: To understand and apply Agentic Design Patterns From Scratch: ReAct, Plan-and-Execute, Reflection, Routing & Parallel Fan-Out in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Know when a deterministic chain suffices versus when an autonomous tool-using agent loop is warranted, so your code stays reliable, fast, and easy to maintain in production.
Safe Tool / Function Calling: Strict Pydantic Tool Schemas, Sandboxed Code Execution & Error Self-Correction
Purpose: To understand and apply Safe Tool / Function Calling: Strict Pydantic Tool Schemas, Sandboxed Code Execution & Error Self-Correction in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Let agents query SQL/APIs, recover from tool errors, and enforce max-iteration and dollar-budget caps, so your code stays reliable, fast, and easy to maintain in production.
LangGraph Mastery: Typed StateGraphs, Conditional Edges, Subgraphs, Streaming Events & Postgres Checkpointers
Purpose: To understand and apply LangGraph Mastery: Typed StateGraphs, Conditional Edges, Subgraphs, Streaming Events & Postgres Checkpointers in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Build production-grade cyclic, resumable, time-travel-debuggable agent workflows in LangGraph, so your code stays reliable, fast, and easy to maintain in production.
Multi-Agent Orchestration (Supervisor, Hierarchical & CrewAI Role Teams) + Long-Term Episodic/Semantic Memory
Purpose: To understand and apply Multi-Agent Orchestration (Supervisor, Hierarchical & CrewAI Role Teams) + Long-Term Episodic/Semantic Memory in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Coordinate specialized Researcher, Coder, Analyst, and Reviewer agents with persistent user memory, so your code stays reliable, fast, and easy to maintain in production.
Model Context Protocol (MCP) Architecture: Hosts, Clients, Servers, Tools, Resources & Stdio/SSE Transports
Purpose: To understand and apply Model Context Protocol (MCP) Architecture: Hosts, Clients, Servers, Tools, Resources & Stdio/SSE Transports in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Build standardized MCP servers in Python/TypeScript that plug cleanly into enterprise AI copilots, so your code stays reliable, fast, and easy to maintain in production.
Enterprise MCP Connectors for PostgreSQL, GitHub, Slack & Internal REST APIs with OAuth2 / RBAC Scopes
Purpose: To understand and apply Enterprise MCP Connectors for PostgreSQL, GitHub, Slack & Internal REST APIs with OAuth2 / RBAC Scopes in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Expose company tools to AI agents safely with per-user authorization and full audit logging, so your code stays reliable, fast, and easy to maintain in production.
Human-in-the-Loop (HITL) Interrupts: LangGraph interrupt(), Diff Preview Cards, State Editing & Resume Flows
Purpose: To understand and apply Human-in-the-Loop (HITL) Interrupts: LangGraph interrupt(), Diff Preview Cards, State Editing & Resume Flows in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Require explicit human sign-off before any agent executes refunds, emails, or production writes, so your code stays reliable, fast, and easy to maintain in production.
Low-Latency Spoken Voice AI Agents: VAD, Streaming STT -> Agent -> Streaming TTS & Barge-In Interruption
Purpose: To understand and apply Low-Latency Spoken Voice AI Agents: VAD, Streaming STT -> Agent -> Streaming TTS & Barge-In Interruption in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Engineer natural sub-600ms conversational voice agents for customer support and technical interviews, so your code stays reliable, fast, and easy to maintain in production.
RAG vs Prompting vs Fine-Tuning Decision Matrix & Curating High-Signal Instruction / ShareGPT Datasets
Purpose: To understand and apply RAG vs Prompting vs Fine-Tuning Decision Matrix & Curating High-Signal Instruction / ShareGPT Datasets in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Avoid wasting weeks fine-tuning for facts (use RAG) — fine-tune for domain style, format, and latency!, so your code stays reliable, fast, and easy to maintain in production.
Synthetic Data Generation, Decontamination, Quality Filtering & Formatting Chat Templates Correctly
Purpose: To understand and apply Synthetic Data Generation, Decontamination, Quality Filtering & Formatting Chat Templates Correctly in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Distill compact 3B–8B specialist models from frontier teacher outputs cleanly without test leakage, so your code stays reliable, fast, and easy to maintain in production.
Parameter-Efficient Fine-Tuning (PEFT): LoRA & 4-Bit QLoRA with HuggingFace, TRL, Unsloth & Weights/Trackers
Purpose: To understand and apply Parameter-Efficient Fine-Tuning (PEFT): LoRA & 4-Bit QLoRA with HuggingFace, TRL, Unsloth & Weights/Trackers in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Fine-tune open-weight Llama/Qwen/Mistral models on a single affordable GPU with 70% less VRAM, so your code stays reliable, fast, and easy to maintain in production.
Preference Alignment (DPO), Adapter Merging & Post-Training Quantization (AWQ, GPTQ, GGUF FP8/INT4)
Purpose: To understand and apply Preference Alignment (DPO), Adapter Merging & Post-Training Quantization (AWQ, GPTQ, GGUF FP8/INT4) in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Align model tone/safety and compress weights for 3x faster, cheaper production inference, so your code stays reliable, fast, and easy to maintain in production.
Production Model Serving Internals: Continuous Batching, PagedAttention & Deploying vLLM / TGI OpenAI-Compatible APIs
Purpose: To understand and apply Production Model Serving Internals: Continuous Batching, PagedAttention & Deploying vLLM / TGI OpenAI-Compatible APIs in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Achieve 5x–10x higher token throughput per GPU compared to naive HuggingFace generate() loops, so your code stays reliable, fast, and easy to maintain in production.
Multi-Model Routing Gateways, Fallback Chains, Redis Vector Semantic Caching & Token Unit-Economics FinOps
Purpose: To understand and apply Multi-Model Routing Gateways, Fallback Chains, Redis Vector Semantic Caching & Token Unit-Economics FinOps in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Route simple intents to fast small models, cache frequent answers, and guarantee 99.9% AI uptime, so your code stays reliable, fast, and easy to maintain in production.
Input / Output Guardrails: PII Redaction (PAN/Aadhaar/Cards), Toxicity Filters, Topic Rails & Schema Validators
Purpose: To understand and apply Input / Output Guardrails: PII Redaction (PAN/Aadhaar/Cards), Toxicity Filters, Topic Rails & Schema Validators in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Ensure enterprise AI responses never leak PII, discuss competitors, or violate compliance rules, so your code stays reliable, fast, and easy to maintain in production.
OWASP Top-10 for LLM Applications: Indirect Prompt Injection Defense, Tool Privilege Escalation & Automated Red-Teaming
Purpose: To understand and apply OWASP Top-10 for LLM Applications: Indirect Prompt Injection Defense, Tool Privilege Escalation & Automated Red-Teaming in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Harden RAG and agentic tool pipelines against adversarial jailbreaks and poisoned documents, so your code stays reliable, fast, and easy to maintain in production.
Containerizing Full-Stack AI Platforms: Multi-Stage Python/React Dockerfiles, GPU Runtime Containers & Compose
Purpose: To understand and apply Containerizing Full-Stack AI Platforms: Multi-Stage Python/React Dockerfiles, GPU Runtime Containers & Compose in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Package FastAPI, Django, Celery, React, pgvector, and Qdrant cleanly for cloud deployment, so your code stays reliable, fast, and easy to maintain in production.
Kubernetes Orchestration for AI: Deployments, Ingress TLS, Secrets, Queue-Driven HPA & GPU Node Scheduling
Purpose: To understand and apply Kubernetes Orchestration for AI: Deployments, Ingress TLS, Secrets, Queue-Driven HPA & GPU Node Scheduling in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Autoscale stateless AI gateways and background ingestion workers smoothly on Kubernetes (EKS), so your code stays reliable, fast, and easy to maintain in production.
AWS Cloud & Enterprise AI Infrastructure: Multi-AZ VPC, RDS pgvector, S3, SQS, AWS Bedrock & Terraform IaC
Purpose: To understand and apply AWS Cloud & Enterprise AI Infrastructure: Multi-AZ VPC, RDS pgvector, S3, SQS, AWS Bedrock & Terraform IaC in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Provision compliant, VPC-isolated AWS cloud + Bedrock enterprise AI stacks via Terraform HCL, so your code stays reliable, fast, and easy to maintain in production.
GitHub Actions CI/CD + AI Eval Gates & Full-Stack LLMOps Observability (Langfuse, OpenTelemetry & Prometheus)
Purpose: To understand and apply GitHub Actions CI/CD + AI Eval Gates & Full-Stack LLMOps Observability (Langfuse, OpenTelemetry & Prometheus) in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Trace every prompt version, tool span, token cost, user thumbs-up/down score, and latency regression live, so your code stays reliable, fast, and easy to maintain in production.
GenAI & Full-Stack System Design Interviews: Designing Perplexity Search, GitHub Copilot, Voice Agents & Multi-Tenant RAG
Purpose: To understand and apply GenAI & Full-Stack System Design Interviews: Designing Perplexity Search, GitHub Copilot, Voice Agents & Multi-Tenant RAG in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Ace frontier AI Engineer and Senior Full-Stack system design rounds with concrete latency/cost math, so your code stays reliable, fast, and easy to maintain in production.
End-to-End Delivery of NexusAgent Multi-Agent LangGraph + MCP + Hybrid RAG + Voice + React 19 SaaS Cloud
Purpose: To understand and apply End-to-End Delivery of NexusAgent Multi-Agent LangGraph + MCP + Hybrid RAG + Voice + React 19 SaaS Cloud in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Ship our flagship full-stack Agentic AI platform with HITL approvals, citations, and LLMOps tracing, so your code stays reliable, fast, and easy to maintain in production.
Red-Team Security Audit, RAGAS >95% Accuracy Gate, Live AWS HTTPS Cutover & 20-Repo GitHub Portfolio
Purpose: To understand and apply Red-Team Security Audit, RAGAS >95% Accuracy Gate, Live AWS HTTPS Cutover & 20-Repo GitHub Portfolio in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Deploy to live cloud production, polish your ATS GenAI resume, and earn your Verified Internship Letter, so your code stays reliable, fast, and easy to maintain in production.
Timed Python DSA + GenAI Mocks, Spoken MockAttempt AI Bar-Raiser Vivas & ≥90% Placement Guarantee Activation
Purpose: To understand and apply Timed Python DSA + GenAI Mocks, Spoken MockAttempt AI Bar-Raiser Vivas & ≥90% Placement Guarantee Activation in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Clear all ≥90% topic mocks and AI vivas (unlimited free re-attempts) to unlock 100 job openings in 6 months, so your code stays reliable, fast, and easy to maintain in production.
Agentic Design Patterns: Planner-Executor, Reflection, Tool Sandboxing & State Persistence
Purpose: To understand and apply Agentic Design Patterns: Planner-Executor, Reflection, Tool Sandboxing & State Persistence in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Engineer reliable multi-step AI agents with bounded retries, timeouts, and human approval gates, so your code stays reliable, fast, and easy to maintain in production.
Advanced Hybrid Multimodal RAG, Re-Ranking & Automated Faithfulness/Safety Evals
Purpose: To understand and apply Advanced Hybrid Multimodal RAG, Re-Ranking & Automated Faithfulness/Safety Evals in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Eliminate hallucinations on complex PDFs, tables, and enterprise knowledge bases, so your code stays reliable, fast, and easy to maintain in production.
Prompt-Injection Defense, PII Redaction, Output Schema Enforcement & Audit Logging
Purpose: To understand and apply Prompt-Injection Defense, PII Redaction, Output Schema Enforcement & Audit Logging in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Meet enterprise security and compliance standards before exposing LLM endpoints to users, so your code stays reliable, fast, and easy to maintain in production.
Semantic Caching, Model Routing, Quantization/LoRA Trade-Offs & Token FinOps
Purpose: To understand and apply Semantic Caching, Model Routing, Quantization/LoRA Trade-Offs & Token FinOps in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Deliver sub-second time-to-first-token while cutting production LLM inference costs, so your code stays reliable, fast, and easy to maintain in production.
Full-Stack React 19 + TypeScript Streaming Interfaces, Citations & Feedback Loops
Purpose: To understand and apply Full-Stack React 19 + TypeScript Streaming Interfaces, Citations & Feedback Loops in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Build intuitive AI product UX with live tool-status indicators and thumbs-up/down eval capture, so your code stays reliable, fast, and easy to maintain in production.
End-to-End GenAI & LLM System Design Defense for Top AI Engineering Roles
Purpose: To understand and apply End-to-End GenAI & LLM System Design Defense for Top AI Engineering Roles in real Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering systems — specifically: Architect enterprise copilots, agent platforms, and evaluation pipelines in final interviews, so your code stays reliable, fast, and easy to maintain in production.