Agentic AI | Generative AI | LLMs | RAG | Agentic AI Frameworks 🌈 TOPICS detailed explanations
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:
🚀 𝗧𝗵𝗲 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗲 — 𝗙𝗿𝗼𝗺 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲
AI Engineer
Different Types of Retrieval in RAG System
Understanding the Layers of Intelligence in Modern AI Systems
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:
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- Energy
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- Chips
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- Infrastructure
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- Models
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- 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)
🚀 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)
Impact Building GenBI
Build a Prompt Learning Loop
Building durable Agents with Workflow DevKit & AI SDK
Claude Agent SDK
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:
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:
Everyone is talking about Agentic AI. Very few are talking about the architecture behind it. Lets do it!!!!
E𝐧𝐝𝐥𝐞𝐬𝐬 𝐋𝐋𝐌 𝐨𝐮𝐭𝐩𝐮𝐭𝐬. 𝐅𝐫𝐮𝐬𝐭𝐫𝐚𝐭𝐞𝐝. 𝐈𝐧𝐜𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐭. 𝐓𝐡𝐞𝐧 𝐝𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐞𝐝 𝐭𝐡𝐞𝐬𝐞 9 𝐞𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥𝐬 𝐞𝐯𝐞𝐫𝐲𝐨𝐧𝐞 𝐢𝐠𝐧𝐨𝐫𝐞𝐬
AI Agents, your Agentic RAG depends on your tech stack
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:
VECTOR DATABASE
LLMs cheatsheet
𝐇𝐨𝐰 𝐭𝐨 𝐈𝐦𝐩𝐫𝐨𝐯𝐞 𝐀𝐏𝐈 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞
𝐋𝐚𝐧𝐠𝐆𝐫𝐚𝐩𝐡 𝐯𝐬 𝐂𝐫𝐞𝐰𝐀𝐈 𝐯𝐬 𝐀𝐮𝐭𝐨𝐆𝐞𝐧 𝐯𝐬 𝐌𝐞𝐭𝐚𝐆𝐏𝐓: 𝐐𝐮𝐢𝐜𝐤 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 𝐁𝐚𝐭𝐭𝐥𝐞
𝐈𝐟 𝐈 𝐡𝐚𝐝 𝐭𝐨 𝐞𝐱𝐩𝐥𝐚𝐢𝐧 𝐑𝐀𝐆 𝐭𝐨 𝐚 𝐛𝐞𝐠𝐢𝐧𝐧𝐞𝐫 𝐢𝐧 𝐨𝐧𝐞 𝐥𝐢𝐧𝐞
Master All 20 Agentic AI Design Patterns
A Visual Taxonomy of Retrieval-Augmented Generation (RAG) Architectures:
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RAG has rapidly evolved from simple vector-based retrieval to agentic, multi-hop, graph-driven, and federated systems.
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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.
AI Agents, RAG has evolved to become an AI ecosystem
Popular AI Agents Frameworks
𝐘𝐨𝐮𝐫 𝐑𝐀𝐆 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞 𝐢𝐬𝐧’𝐭 𝐟𝐚𝐢𝐥𝐢𝐧𝐠 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐨𝐟 𝐭𝐡𝐞 𝐦𝐨𝐝𝐞𝐥, 𝐢𝐭’𝐬 𝐟𝐚𝐢𝐥𝐢𝐧𝐠 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐨𝐟 𝐛𝐚𝐝 𝐜𝐡𝐮𝐧𝐤𝐢𝐧𝐠.
Master LLM Fine-Tuning
Embeddings are the secret language of AI
MCP & A2A (Agent2Agent) protocol, explained visually!
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Agentic applications require both A2A and MCP.
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MCP provides agents with access to tools.
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A2A allows agents to connect with other agents and collaborate in teams.
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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
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AI applications can model A2A agents as MCP resources, represented by their AgentCard (more about cards in next tweet).
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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)
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A2A-supporting Remote Agents must publish a JSON Agent Card detailing their capabilities and authentication.
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Clients use this to find and communicate with the best agent for a task.
What makes A2A powerful?
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Secure collaboration
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Task and state management
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UX negotiation
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Capability discovery
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Agents from different frameworks working together
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Additionally, it can integrate with MCP.
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If you want to learn MCPs from scratch (with projects), I have shared a free guidebook in the replies.
LLM fine-tuning techniques I'd learn if I were to customize them:
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- LoRA
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- QLoRA
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- Prefix Tuning
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- Adapter Tuning
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- Instruction Tuning
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- P-Tuning
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- BitFit = 8. Soft Prompts
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- RLHF
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- RLAIF
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- DPO (Direct Preference Optimization)
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- GRPO (Group Relative Policy Optimization)
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- RLAIF (RL with AI Feedback)
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- Multi-Task Fine-Tuning
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- Federated Fine-Tuning
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
Fine-Tuning LLMs Without the Confusion
How SFT, RLHF, LoRA, QLoRA, and instruction tuning actually fit together LLM
AI Engineering
Claude Code 3-Phase strategy:
12 Essential Generative AI Concepts
AI Algorithms
API Concepts
Unpacking the LangChain Ecosystem
𝐑𝐞𝐀𝐜𝐭: 𝐂𝐨𝐦𝐛𝐢𝐧𝐢𝐧𝐠 𝐑𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐀𝐜𝐭𝐢𝐧𝐠 𝐢𝐧 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥𝐬
Cornell University
𝐓𝐡𝐞 𝐇𝐢𝐝𝐝𝐞𝐧 𝐏𝐨𝐰𝐞𝐫 𝐁𝐞𝐡𝐢𝐧𝐝 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬: 𝐌𝐞𝐦𝐨𝐫𝐲 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞𝐬 𝐔𝐧𝐯𝐞𝐢𝐥𝐞𝐝
The role of Reinforcement Learning (RL)
Reasoning Models Generate Societies of Thought
LangChain Components — understanding the engineering behind LLM systems
The Smol Training Playbook
Small Language Models for AI Agents
Small Language Models for AI Agents
LLM Fine Tuningb Engineer Interview Questions and Answers
LLM Fine Tuningb Engineer Interview Questions and Answers
RAG Meets LLMs
RAG is becoming essential for enterprise GenAI
🔧 Mastering System Design: Essential Components for Success 🔧
AI Agents Cheatsheet
Build DeepSeek from Scratch
YouTube Playlist Link:
𝐏𝐢𝐧𝐞𝐜𝐨𝐧𝐞: 𝐀 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐕𝐞𝐜𝐭𝐨𝐫 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐟𝐨𝐫 𝐒𝐞𝐦𝐚𝐧𝐭𝐢𝐜 𝐒𝐞𝐚𝐫𝐜𝐡
𝐏𝐢𝐧𝐞𝐜𝐨𝐧𝐞
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
🚀 LLM Architectures
Learn Retrieval-Augmented Generation (RAG) from Scratch – Complete Video Series by LangChain
🚀 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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