AI-Engineer

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Agentic AI | Generative AI | LLMs | RAG | Agentic AI Frameworks 🌈 TOPICS detailed explanations

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𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝗟𝗮𝘆𝗲𝗿𝘀 𝗼𝗳 𝗔𝗜

AI agents roadmap divided into 3 levels

GenAI vs AI Agents vs Agentic AI vs ML vs Data Science vs LLM vs Cognitive architectures.

There are 3 AI workflows worth knowing:

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🚀 𝗧𝗵𝗲 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗲 — 𝗙𝗿𝗼𝗺 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲

AI Engineer

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Different Types of Retrieval in RAG System

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Understanding the Layers of Intelligence in Modern AI Systems

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Understanding the MCP Workflow: How AI + Tools Work Together Seamlessly

Building Agents with Model Context Protocol Full Workshop

NVIDIA Live with CEO Jensen Huang

AI 5 Layer Cake:

• 1. Energy
• 2. Chips
• 3. Infrastructure
• 4. Models
• 5. Applications

Stanford’s LLM lecture series

AI Periodic Table Explained: Mapping LLMs, RAG & AI Agent Frameworks

A RAG chatbot = PR + EM + VX + RG + LG + GR

An Agentic system = AG + FC + FW (looping until the goal is achieved)

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🚀 AI Periodic Table: A Simple Way to Understand Modern AI Systems

• AI systems are becoming increasingly complex — LLMs, RAG, agents, tools, guardrails, multimodal models… it’s easy to get lost.Just like the chemistry periodic table, it organizes AI into foundational elements, compositions, deployment patterns, and emerging capabilities.

🔹 Row 1 – Primitives (Foundations)

• Prompts (PR) – instructions that drive behavior
• Embeddings (EM) – semantic representations
• LLMs (LG) – core reasoning engines

🔹 Row 2 – Compositions (Where value starts)

• Function Calling (FC) – tool execution
• Vector Databases (VX) – semantic memory
• RAG (RG) – grounded generation
• Guardrails (GR) – safety & validation
• Multimodal Models (MM)

🔹 Row 3 – Deployment (Production AI)

• Agents (AG) – think → act → observe loops
• Fine-tuning (FT) – domain adaptation
• Frameworks (FW) – orchestration (LangChain, etc.)
• Red Teaming (RT) – adversarial testing
• Small Models (SM) – fast & cost-efficient

🔹 Row 4 – Emerging (Future direction)

• Multi-Agent Systems (MA)
• Synthetic Data (SY)
• Interpretability (IN)
• Thinking Models (TH)

⚗️ What’s powerful is how these elements combine into “reactions”:

• A RAG chatbot = PR + EM + VX + RG + LG + GR
• An Agentic system = AG + FC + FW (looping until the goal is achieved)

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Impact Building GenBI

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Build a Prompt Learning Loop

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Building durable Agents with Workflow DevKit & AI SDK

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Claude Agent SDK

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The Complete AI/LLM Ecosystem: A Developer’s Guide

📊 Understanding the Modern AI Stack

-Building AI applications today requires understanding multiple interconnected layers. Whether you’re working on Retrieval-Augmented Generation (RAG) or LLM-based systems, here are the 7 critical components you need to know:

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𝐀𝐈 𝐭𝐨𝐨𝐥𝐬 𝐚𝐫𝐞 𝐧𝐨 𝐥𝐨𝐧𝐠𝐞𝐫 𝐣𝐮𝐬𝐭 𝐛𝐮𝐳𝐳𝐰𝐨𝐫𝐝𝐬. 𝐓𝐡𝐞𝐲 𝐚𝐫𝐞 𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐢𝐧𝐠 𝐡𝐨𝐰 𝐰𝐞 𝐛𝐮𝐢𝐥𝐝, 𝐜𝐨𝐧𝐧𝐞𝐜𝐭, 𝐚𝐧𝐝 𝐬𝐜𝐚𝐥𝐞 𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐞𝐬. 𝐋𝐞𝐭’𝐬 𝐛𝐫𝐞𝐚𝐤 𝐝𝐨𝐰𝐧 𝐬𝐨𝐦𝐞 𝐨𝐟 𝐭𝐡𝐞 𝐤𝐞𝐲 𝐩𝐥𝐚𝐲𝐞𝐫𝐬 𝐬𝐡𝐚𝐩𝐢𝐧𝐠 𝐭𝐡𝐢𝐬 𝐞𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧:

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Everyone is talking about Agentic AI. Very few are talking about the architecture behind it. Lets do it!!!

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E𝐧𝐝𝐥𝐞𝐬𝐬 𝐋𝐋𝐌 𝐨𝐮𝐭𝐩𝐮𝐭𝐬. 𝐅𝐫𝐮𝐬𝐭𝐫𝐚𝐭𝐞𝐝. 𝐈𝐧𝐜𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐭. 𝐓𝐡𝐞𝐧 𝐝𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐞𝐝 𝐭𝐡𝐞𝐬𝐞 9 𝐞𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥𝐬 𝐞𝐯𝐞𝐫𝐲𝐨𝐧𝐞 𝐢𝐠𝐧𝐨𝐫𝐞𝐬

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AI Agents, your Agentic RAG depends on your tech stack

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I broke this down to show what’s really happening inside a production-grade RAG system.

• Here’s how to understand each layer and why it exists:

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VECTOR DATABASE

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LLMs cheatsheet

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𝐇𝐨𝐰 𝐭𝐨 𝐈𝐦𝐩𝐫𝐨𝐯𝐞 𝐀𝐏𝐈 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞

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𝐋𝐚𝐧𝐠𝐆𝐫𝐚𝐩𝐡 𝐯𝐬 𝐂𝐫𝐞𝐰𝐀𝐈 𝐯𝐬 𝐀𝐮𝐭𝐨𝐆𝐞𝐧 𝐯𝐬 𝐌𝐞𝐭𝐚𝐆𝐏𝐓: 𝐐𝐮𝐢𝐜𝐤 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 𝐁𝐚𝐭𝐭𝐥𝐞

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𝐈𝐟 𝐈 𝐡𝐚𝐝 𝐭𝐨 𝐞𝐱𝐩𝐥𝐚𝐢𝐧 𝐑𝐀𝐆 𝐭𝐨 𝐚 𝐛𝐞𝐠𝐢𝐧𝐧𝐞𝐫 𝐢𝐧 𝐨𝐧𝐞 𝐥𝐢𝐧𝐞

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Master All 20 Agentic AI Design Patterns

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A Visual Taxonomy of Retrieval-Augmented Generation (RAG) Architectures:

• RAG has rapidly evolved from simple vector-based retrieval to agentic, multi-hop, graph-driven, and federated systems.

• This visual brings together 12 major RAG architectures—from Naïve RAG to Tool-Integrated and Federated RAG—highlighting how modern AI systems reason, retrieve, and adapt at scale.

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AI Agents, RAG has evolved to become an AI ecosystem

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Popular AI Agents Frameworks

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𝐘𝐨𝐮𝐫 𝐑𝐀𝐆 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞 𝐢𝐬𝐧’𝐭 𝐟𝐚𝐢𝐥𝐢𝐧𝐠 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐨𝐟 𝐭𝐡𝐞 𝐦𝐨𝐝𝐞𝐥, 𝐢𝐭’𝐬 𝐟𝐚𝐢𝐥𝐢𝐧𝐠 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐨𝐟 𝐛𝐚𝐝 𝐜𝐡𝐮𝐧𝐤𝐢𝐧𝐠.

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Master LLM Fine-Tuning

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Embeddings are the secret language of AI

MCP & A2A (Agent2Agent) protocol, explained visually!

• Agentic applications require both A2A and MCP.

• MCP provides agents with access to tools.

• A2A allows agents to connect with other agents and collaborate in teams.

• The visual below explains where exactly they fit into the Agent protocol stack.

What is A2A?

• A2A (Agent2Agent) enables multiple AI agents to work together on tasks without directly sharing their internal memory, thoughts, or tools.

Instead, they communicate by exchanging context, task updates, instructions, and data.

A2A <> MCP

• AI applications can model A2A agents as MCP resources, represented by their AgentCard (more about cards in next tweet).

• Using this, AI Agents connecting to an MCP server can discover new Agents to collaborate with and connect via the A2A protocol.

Agent Cards (ID cards for Agents)

• A2A-supporting Remote Agents must publish a JSON Agent Card detailing their capabilities and authentication.

• Clients use this to find and communicate with the best agent for a task.

What makes A2A powerful?

• Secure collaboration

• Task and state management

• UX negotiation

• Capability discovery

• Agents from different frameworks working together

• Additionally, it can integrate with MCP.

• If you want to learn MCPs from scratch (with projects), I have shared a free guidebook in the replies.

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LLM fine-tuning techniques I’d learn if I were to customize them:

• 1. LoRA
• 2. QLoRA
• 3. Prefix Tuning
• 4. Adapter Tuning
• 5. Instruction Tuning
• 6. P-Tuning
• 7. BitFit = 8. Soft Prompts
• 9. RLHF
• 10. RLAIF
• 11. DPO (Direct Preference Optimization)
• 12. GRPO (Group Relative Policy Optimization)
• 13. RLAIF (RL with AI Feedback)
• 14. Multi-Task Fine-Tuning
• 15. Federated Fine-Tuning

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LLMs hallucinate

• https://arxiv.org/pdf/2410.14262
• https://arxiv.org/abs/2509.18970
• https://arxiv.org/abs/2508.01781
• https://arxiv.org/abs/2409.05746
• https://www.mdpi.com/2227-7390/13/5/856
• https://arxiv.org/abs/2408.15533
• https://arxiv.org/abs/2508.03553
• https://openreview.net/forum?id=ztzZDzgfrh
• https://arxiv.org/abs/2402.10612
• https://arxiv.org/abs/2312.10997
• https://arxiv.org/abs/2506.00054
• https://arxiv.org/abs/2507.15903
• https://arxiv.org/abs/2501.13946
• https://www.mdpi.com/2078-2489/16/7/517
• https://arxiv.org/abs/2309.11495
• https://arxiv.org/abs/2203.11171
• https://arxiv.org/abs/2504.09440
• https://arxiv.org/abs/2510.11529
• https://arxiv.org/abs/2506.17088
• https://arxiv.org/abs/2409.11283
• https://arxiv.org/abs/2507.22915
• https://www.preprints.org/manuscript/202505.0456

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Fine-Tuning LLMs Without the Confusion

How SFT, RLHF, LoRA, QLoRA, and instruction tuning actually fit together LLM

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AI Engineering

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Claude Code 3-Phase strategy:

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12 Essential Generative AI Concepts

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AI Algorithms

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API Concepts

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Unpacking the LangChain Ecosystem

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𝐑𝐞𝐀𝐜𝐭: 𝐂𝐨𝐦𝐛𝐢𝐧𝐢𝐧𝐠 𝐑𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐀𝐜𝐭𝐢𝐧𝐠 𝐢𝐧 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥𝐬

Cornell University

• https://arxiv.org/abs/2210.03629
• https://react-lm.github.io/

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𝐓𝐡𝐞 𝐇𝐢𝐝𝐝𝐞𝐧 𝐏𝐨𝐰𝐞𝐫 𝐁𝐞𝐡𝐢𝐧𝐝 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬: 𝐌𝐞𝐦𝐨𝐫𝐲 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞𝐬 𝐔𝐧𝐯𝐞𝐢𝐥𝐞𝐝

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The role of Reinforcement Learning (RL)

• Reinforcement Learning

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Reasoning Models Generate Societies of Thought

• Reasoning in LLMs

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LangChain Components — understanding the engineering behind LLM systems

• LangChain Components

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The Smol Training Playbook

• Training LLMs

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Small Language Models for AI Agents

Small Language Models for AI Agents

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LLM Fine Tuningb Engineer Interview Questions and Answers

LLM Fine Tuningb Engineer Interview Questions and Answers

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RAG Meets LLMs

RAG is becoming essential for enterprise GenAI

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🔧 Mastering System Design: Essential Components for Success 🔧

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AI Agents Cheatsheet

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Build DeepSeek from Scratch

YouTube Playlist Link:

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𝐏𝐢𝐧𝐞𝐜𝐨𝐧𝐞: 𝐀 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐕𝐞𝐜𝐭𝐨𝐫 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐟𝐨𝐫 𝐒𝐞𝐦𝐚𝐧𝐭𝐢𝐜 𝐒𝐞𝐚𝐫𝐜𝐡

𝐏𝐢𝐧𝐞𝐜𝐨𝐧𝐞

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Build AI Agents with LLMs, RAG & Knowledge Graphs

Complete guide to building production-ready AI agents - systems that perceive, reason, and take autonomous action beyond simple chat

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🚀 LLM Architectures

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Learn Retrieval-Augmented Generation (RAG) from Scratch – Complete Video Series by LangChain

• Watch the full playlist here:

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🚀 Fine-Tuning Large Language Models for Domain-Specific Tasks

Fine-tuning Large Language Models is how generic LLMs turn into domain experts.

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Enterprise AI Agent System Architecture

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4 indexing strategies that separate good RAG from great RAG:

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Components of AI agents

Weaviate cheat sheet AI engineering roadmaps

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LLM APIs and only tweaking temperature

LLM APIs

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Hyperparameter Cheat Sheet

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6 Artifacts separate a $80k dev from a $300k architect

Build all 6. You’re hireable. Period.

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Prompt Repetition Improves Non-Reasoning LLMs

Duplicate your prompt!

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20 Essential LLM guardrails

LLM guardrails

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30 Claude prompts that 2X output quality

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97% of AI security is architecture.

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15 STRATEGIES TO REDUCE LLM COSTS

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Claude Code is not another AI assistant.

Claude

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AIGUARDRAILS

AIGOVERNANCE

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Speech-to-Text Model

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AI Engineer interview, you cannot ignore RAG (Retrieval-Augmented Generation).

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System Architecture for Agentic Large Language Models

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Vectorless Tree Retrieval for RAG

PDF → Chunk → Embed → Store → Retrieve → LLM → Answer

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Agentic RAG with MCP Architecture

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MIT literally packed 7 hours with everything:

• You need to know about GenAI for FREE. Here’s what you’ll learn:
• 💠 Stable-Diffusion & DALL·E
• 💠 Neural Networks
• 💠 Supervised Learning
• 💠 Representation & Unsupervised Learning
• 💠 Reinforcement Learning
• 💠 Generative AI
• 💠 Self-Supervised Learning
• 💠 Foundation Models
• 💠 GANs (adversarial)
• 💠 Contrastive Learning
• 💠 Auto-encoders
• 💠 Denoising & Diffusion
• https://github.com/analyticalrohit/llms-from-scratch
• https://awesomeneuron.substack.com/p/a-visual-guide-to-llms-part-1
• https://awesomeneuron.substack.com/p/a-visual-guide-to-ai-agents
• https://awesomeneuron.substack.com/p/how-rag-enhances-llms-a-step-by-step
• https://awesomeneuron.substack.com/p/a-visual-guide-to-agentic-rag
• https://awesomeneuron.substack.com/p/a-visual-guide-to-ai-agents
• https://www.youtube.com/playlist?list=PLXV9Vh2jYcjbnv67sXNDJiO8MWLA3ZJKR
• https://www.futureofai.mit.edu/

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Modern AI Runs on GPUs and TPUs Instead of CPUs

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Production-Grade AI Agent

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12 𝐄𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬

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14 Types of AI Hallucinations — and how to prevent them because most teams treat hallucination like a mystery

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Claude AI ➜ Thinks | Claude Code ➜ Builds | Claude Cowork ➜ Automates

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Types of Generative AI Models

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Master LLM Fine-Tuning

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The LLM Evaluation Guide

• Read the article here

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𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗜 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁: 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗥𝗲𝗹𝗶𝗮𝗯𝗹𝗲, 𝗖𝗼𝗻𝘁𝗲𝘅𝘁-𝗔𝘄𝗮𝗿𝗲 𝗦𝘆𝘀𝘁𝗲𝗺𝘀

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This visual captures 6 important categories:

1️⃣ 𝐆𝐏𝐓 (𝐆𝐞𝐧𝐞𝐫𝐚𝐥-𝐩𝐮𝐫𝐩𝐨𝐬𝐞 𝐦𝐨𝐝𝐞𝐥𝐬)

• Your default reasoning + generation layer.
• Great for writing, coding, and conversational tasks.

2️⃣ 𝐌𝐨𝐄 (𝐌𝐢𝐱𝐭𝐮𝐫𝐞 𝐨𝐟 𝐄𝐱𝐩𝐞𝐫𝐭𝐬)

• Instead of using the full model every time, it routes inputs to specialized sub-networks.
• Better efficiency + scalability at large scale.

3️⃣ 𝐕𝐋𝐌 (𝐕𝐢𝐬𝐢𝐨𝐧-𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥𝐬)

• Handles multimodal inputs.
• Agents can now read screenshots, interpret diagrams, understand images + text together

4️⃣ 𝐋𝐑𝐌 (𝐋𝐚𝐫𝐠𝐞 𝐑𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠 𝐌𝐨𝐝𝐞𝐥𝐬)

• Focused on structured thinking.
• Less about fluent text, more about multi-step reasoning, decision-making

5️⃣ 𝐒𝐋𝐌 (𝐒𝐦𝐚𝐥𝐥 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥𝐬)

• Optimized for low latency, on-device inference and cost efficiency
• Useful for edge AI and real-time systems.

6️⃣ 𝐋𝐀𝐌 (𝐋𝐚𝐫𝐠𝐞 𝐀𝐜𝐭𝐢𝐨𝐧 𝐌𝐨𝐝𝐞𝐥𝐬)

• This is where agents become agents.
• Not just generating text, but calling tools, executing actions, interacting with environments

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AI Anatomy” to help you actually understand what’s going on:

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CLAUDE CODE COMMAND

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Most AI agent failures in production are NOT model problems.

• They’re guardrail failures.

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Production is where the real architecture begins.

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𝐀𝐈 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐈𝐬𝐧’𝐭 𝐎𝐧𝐞 𝐂𝐨𝐧𝐭𝐫𝐨𝐥.

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Rate Limiting Explained!

• Your API can handle 1,000 requests per second.But what happens when one user sends 10,000 requests in just a few seconds?Without rate limiting, your servers could slow down, crash, or become unnecessarily expensive.That’s why rate limiting is one of the most important concepts in system design.

Here are 6 concepts every developer should understand:

-1. Why Rate Limiting? Controls how many requests a user, API key, or IP can send within a specific period to prevent abuse and server overload.

-2. Token Bucket Uses tokens that refill at a fixed rate, allowing short bursts of traffic while maintaining a controlled average request rate.

-3. Leaky Bucket Places incoming requests into a queue and processes them at a steady rate, preventing sudden traffic spikes from overwhelming servers.

-4. Fixed vs. Sliding Window Fixed windows reset request counters at regular intervals, while sliding windows track requests over a rolling period for more accurate limits.

-5. Distributed Rate Limiting Uses shared counters, often stored in Redis, to enforce consistent request limits across multiple API servers.

-6. Responding to Limits Returns HTTP 429 when limits are exceeded and uses Retry-After headers to guide clients on when to retry safely.

-The important lesson?

-Rate limiting isn’t just about blocking excessive requests.

-It’s about balancing performance, fairness, reliability, and infrastructure costs.

• Get Your Copy anywhere internationally:
• US: https://www.amazon.com/System-Design-Behavioral-Intelligence-Engineering/dp/1808344057/
• UK: https://www.amazon.co.uk/System-Design-Behavioral-Intelligence-Engineering/dp/1808344057/r
• India : https://www.amazon.com/System-Design-Behavioral-Intelligence-Engineering/dp/1808344057

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📜 License

Licensed under the MIT License - Feel free to fork and build upon this innovation! 🚀

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📞 CONTACT & NETWORKING 📞

💼 Professional Networks

🚀 AI/ML & Data Science

💻 Competitive Programming (Including all coding plateform’s 5000+ Problems/Questions solved )

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📊 GitHub Stats & Metrics 📊

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