Aishwarya Srinivasan

AI@Fireworks AI | Global Speaker(100+ talks)| EB1-A Recipient| 570k+ Followers| AI Engineer| GenAI/LLM Inference| LinkedIn Top Voice| Author-What's Your Worth?| Startup AI Advisor| Angel Investor| Ex-Google,Microsoft,IBM

𝗙𝗼𝗿 𝗽𝗮𝗿𝘁𝗻𝗲𝗿𝘀𝗵𝗶𝗽 𝗶𝗻𝗾𝘂𝗶𝗿𝗶𝗲𝘀, 𝗰𝗼𝗻𝘁𝗮𝗰𝘁 𝗺𝘆 𝘁𝗲𝗮𝗺 𝗮𝘁 💌 partnerships@illuminate-ai.org

I write about:
- AI engineering
- Applied machine learning
- Generative AI Applications
- LLM/ LLMOPs (Training, post-training, inferencing)
- Fundamentals of Data science/ ML/ AI
- ML roles interview tips
- Learning roadmaps (beginner to advanced)
- Importance of personal branding for career growth

**All the posts reflect my own views and do not represent my employer.**

AI Advisor || Responsible AI Advocate || LinkedIn Top Voice - Data & AI  || Top 10 AI Influencer || EB1-A Receipt


Bio: 
Aishwarya currently leads the Developer Relations - Growth at Fireworks AI. She was previously at Microsoft for Startups as a Senior AI Advisor to help Y Combinator startups build machine learning solutions, leveraging core Microsoft/ OpenAI products. Prior to this, Aishwarya was working as a Data Scientist in Google Cloud and before that an AI & ML Innovation Leader at IBM Data & AI, where she was working cross-functionally with the product team, data science team and sales to research AI use-cases for clients by conducting discovery workshops and building assets to showcase the business value of the technology. She holds a post-graduate in Data Science from Columbia University. 

Aishwarya has been awarded Trailblazer of the Year by Women in AI in 2022 and Women of Influence by Business Journal in 2022. She was awarded EB1A - extraordinary ability visa for her contributions in the AI field.

She is an advocate for open-source technologies, and was an open source Developer Advocate for Deepchecks and Lightning AI, and a contributor to Scikit Learn. 


She holds a Patent Award (2018) for developing a Reinforcement Learning model for Machine Trading.

She has over 500,000+ follower base on LinkedIn and actively organizes events and conferences to inspire budding data scientists. She has been spotlighted as a LinkedIn Top Voice 2020 for Data Science and AI, which features Top 10 Machine Learning influencers across the world. 

She is an ardent reader and has contributed to the scholastic community. To spread her knowledge in the space of data science, and to inspire budding Data Scientists, she actively writes blogs related to machine learning on LinkedIn and Instagram: www.instagram.com/the.datascience.gal

Besides being a data junkie, she is a fitness fanatic who is into martial arts (Krav Maga) and yoga.

Senior AI Advisor

If you're feeling overwhelmed with how fast AI is evolving, you're not alone.

Every day there’s a new paper, a new framework, a new agent loop, and it’s easy to feel like you’re falling behind.

But the good news is that you don’t need to learn everything all at once. What you need is structure.

So I put together a 10-level AI Agents Learning Roadmap that takes you from foundations to production, layering your learning in a way that’s actually doable.

💡My recommendation: spend 2–3 weeks on each level. Learn the concepts, implement small projects, and build your intuition.

If you're moving faster or slower based on time or experience, that’s okay too.
And when something new drops? That can be your Level 11. Don’t let “newness” derail your plan. Just start here.

👇 Here’s the roadmap:
🔖 Level 1: GenAI & Transformer Foundations
 Tokens, embeddings, transformers, decoding, and inference with open-weight models.

🔖 Level 2: Prompting & Language Model Behavior
 Prompt types (CoT, ReAct, ToT), decoding strategies, context design, and adversarial prompting.

🔖 Level 3: Retrieval-Augmented Generation (RAG)
 Chunking, embeddings, vector DBs, RAG pipelines, and RAG evaluation.

🔖 Level 4: LLMOps & Tools
 LangChain, LangGraph, Dust, CrewAI, tool use, function calling, and synthetic data.

🔖 Level 5: Agents & Agent Frameworks
 Agent types, memory, planning, LangChain agents, LangGraph loops, and evaluation.

🔖 Level 6: Memory, State & Orchestration
 Vector and symbolic memory, episodic vs persistent state, memory compression.

🔖 Level 7: Multi-Agent Systems
 Hub-and-spoke vs decentralized, message passing, collaborative agents, agent teams.

🔖 Level 8: Evaluation & Reinforcement Learning
 LLM-as-a-Judge, RLHF, RLVR, reward modeling, and self-correcting loops.

🔖 Level 9: Protocols & Safety
 MCP, A2A, safety alignment, guardrails, traceability, and autonomous policy updates.

🔖 Level 10: Build & Deploy
 FastAPI, Streamlit, GGUF, QLoRA, caching, monitoring with LangSmith, Arize, Trulens.

📌 Bookmark this.
🛠️ Build something after every level.

And if you're wondering what tools to explore along the way → Start with Hugging Face (to explore LLMs and SLMs), you can use Ollama (to run SLMs on your laptop, like Phi-4, TinyLlama), or Fireworks AI (to run LLMs via endpoint, like Qwen 3, Kimi K2, DeepSeek R1), then explore LangChain & LangGraph (these two tools will  teach you a lot), then you can move into learning Agentic frameworks like CrewAI, AutoGen. 

💻 Pro-tip: Start with cookbooks!

〰️〰️〰️
Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

Hundreds of AI minds gathered in Central Park on a chilly Saturday morning, it was incredible :)

The past weekend in New York felt like one of those moments where community completely surprises you in the best way.

I was traveling to the city for a work event, and my friend Pranjal and I had a thought- What if we host a small AI meetup on Saturday morning in Central Park? Nothing fancy, just a space for people to connect.

So we put up a quick Luma link and shared it on LinkedIn. We expected maybe a handful of people. Instead, the sign ups crossed 450+. 

We put a small table with coffee, muffins, cookies and some cool swag- thanks to Teachable. We weren’t sure how it would go. We just hoped people would show up.

What happened next honestly moved me.

We reached the park at 9.20am to set up for a 10am meetup, and people were already there. In the cold. On a Saturday morning. People had traveled from Boston, Maryland, upstate New York, New Jersey, even Philadelphia. I kept talking to folks thinking, you really came all this way… for this?

The meetup was meant to last two hours. People stayed until 3pm! Almost six hours of conversations about AI, careers, ideas, and everything in between. No microphones. No agenda. Just humans connecting because they care about the same things.

This experience reminded me of something I tend to forget- community doesn’t need perfect planning. It needs intention. It needs genuine people. And it needs a space where everyone feels welcome.

I’m grateful to every single person who showed up. You made this morning unforgettable, and you reminded me why I love building in this space.

Shout-out to Aditya Suresh and Pranam Daga for helping us with putting this together ❤️

There is a quiet moment every November when life slows down just enough for reflection. I never grew up celebrating Thanksgiving. Back home in India, I didn’t even know what it meant. But after moving to the US, watching my friends celebrate, watching sitcoms where families gathered around food and gratitude, I started understanding the deeper meaning behind it.

A moment to pause.
A moment to look around.
A moment to look back.

This year, gratitude feels a little different for me. A little heavier in the best possible way.

I feel grateful for a life where I get to wake up every day and do work I genuinely love. AI is chaotic and intense and fast. Yet it gives me energy. It gives me purpose. And I feel lucky to be part of this ecosystem during a time when everything is shifting.

I feel grateful for the people who hold me steady.
A mom who pushed me when the world expected her to play it safe. A mom who defied every norm to carve space for me to choose ambition without apology.

A husband who is involved in my life in the most beautiful way supportive is honestly an understatement. He is a true partner in every single thing I do. He listens to every idea, every frustration, every new plan I dream up and he backs me with a kind of belief that strengthens my own. Chinnu amma and Chinnu appa who patiently listen to my ten year entrepreneurial plans and cheer for me with the kind of warmth only family can give.

Mentors and colleagues who let me thrive creatively who challenge me in the right ways who make every room I walk into feel lighter and more possible.

I feel grateful for health. For a body I get to take care of. For the time I carve out to move, to eat well, to nurture myself. It took years to realize that taking care of your body is also taking care of your dreams.

And I feel grateful for the little girl I used to be the one who sat with a notebook and wrote her goals on every page the one who kept believing she would get there someday. If I could talk to her today, I would tell her that she is doing great and that her stubborn belief in herself was worth it.

Thanksgiving may not be something I grew up with, but it has become a reminder to pause and appreciate the rarest gift of all the good people around you. People who are genuinely supportive, genuinely positive, and genuinely rooting for you.

I am grateful for all of it.
And grateful for everyone who has been part of my journey so far.

If you are reading this, I hope you take a moment to celebrate the smallest wins, the unexpected joys, and the people who make your life feel expansive.

li_at

JSESSIONID

What an electrifying afternoon! Absolutely delighted to have joined the Mayfield AI List honorary lunch at the beautiful Golden Gate Club in San Francisco.

The room was filled with the brightest minds in the AI VC world. it was a privilege to meet and listen to inspiring leaders like Navin Chaddha, Aparna Chennapragada and Garry Tan. 

I had the opportunity to host a focused roundtable, diving deep into the current state of the industry. We tackled the crucial discussion around Perception vs. Reality of AI, analyzing current hype cycles, and zeroing in on investment opportunities that hold real long-term potential.

It’s moments and small-group settings like these, where leaders can candidly discuss where the real value lies beyond the buzz, that drive the industry forward.

Huge thanks to the Mayfield and Startup Grind team (special shoutout to James Gee and Fadia Frangie Doumat) for hosting and for the award ♥️

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At AWS Re:Invent this week!
It’s my first time at Re:Invent, and the energy here is unreal. I’m here with our team at Fireworks AI, and we’ve set up at booth #1558.

If you’re around, come say hi. Would love to meet builders, founders, and anyone exploring what’s next in AI.

#AWSReInvent

Wrapped up AI Dev 25 hosted by DeepLearning.AI

More than 1400 AI developers gathered in one space, and the energy was something you could feel instantly. Meeting so many builders and hearing what you’re creating reminded me why this community moves the needle the way it does.

Hearing Andrew Ng talk about the future of work was a standout moment. The way he encourages everyone on his team to code, regardless of background, is exactly the kind of mindset shift this era needs. AI literacy is becoming a baseline skill, and he articulated that perfectly.

Our panel with Michael Kearns, Hatice Ozen, Kay Zhu, and Josh Gordon covered how the ecosystem is evolving across the stack, from Groq , Google, and Amazon Web Services (AWS)  to Fireworks AI and how teams like Genspark are using RFT on Fireworks AI in production workflows. We also touched on multi-agent systems, practical best practices, and where developer tooling still needs to mature.

I also loved my conversation with Michael Kearns on responsible AI and how that thinking has evolved from classic ML days to the generative era. His perspective at the intersection of academia and industry always leaves me thinking differently.

Grateful for the conversations and connections today. If we met, thank you for stopping by. If we didn’t, I’m always happy to keep the discussion going. There’s a lot to build, and we’re still early.

Thank you DeepLearning.AI team for having me ❤️

When you’re an AI engineer, the worst bottleneck isn’t writing code. It’s navigating everything around the code.

Last week I tried out Rovo Dev from Atlassian and it really changed how I approach dev work, plus how I see AI agents changing the landscape of software engineering.

𝗪𝗵𝗮𝘁 𝗶𝘀 𝗥𝗼𝘃𝗼 𝗗𝗲𝘃?
RovoDev is an AI agent built to live inside your dev workflow, including your code, your issues (let’s say in Jira), your docs (let’s say in Confluence), your pull requests. 

It helps you move from idea → code → deployment with fewer context switches.

𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝗜 𝗻𝗼𝘁𝗶𝗰𝗲𝗱 𝗮𝗳𝘁𝗲𝗿 𝘂𝘀𝗶𝗻𝗴 𝗶𝘁:
→ I asked in plain English: “What does this module do? Where are its dependencies?” and got a clear breakdown, no digging through 3 dozen files.
→ I linked my GitHub/Bitbucket + Jira and was able to surface relevant issues, test failures and comments in one context instead of switching tools.
→ The CLI experience (yes, in my terminal) allowed me to trigger agents to draft tests, summarise PRs, or suggest refactoring ideas, without leaving my code. 

𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀?
What I realized after using Rovo Dev is that the real time-sink in engineering isn’t writing code, it’s in fact more about re-orienting yourself.

Jumping between Jira, Confluence, and your repo just to understand context.
Trying to figure out why something was built the way it was.
And then losing your train of thought before you can even start solving the problem.

Well, that's where Rovo Dev actually helps close that gap.

If you’ve ever joined a new project and spent hours just piecing together what’s going on, or if you constantly find yourself switching between tools to get one small thing done, you should try this out: fandf.co/42MTjr8 

Thank you Atlassian for partnering with me on this post, and giving me access to Rovo Dev to test it out :)

#AtlassianPartner

The two tools that are absolutely necessary for any AI engineer to get started are LangChain and LangGraph.

If you’re new to the AI engineering space, here is a quick TLDR.

LangChain takes you beyond basic prompting and gives you the building blocks to turn models into real applications with structure, tooling, retrieval, and clean orchestration.

LangGraph is where you unlock agentic workflows. It lets you define states and transitions so your systems behave predictably instead of guessing through tasks.

Arvind Narayanamurthy and I are hosting a hands-on workshop on December 20 to help you learn both. It’s beginner to intermediate friendly and fully focused on practical engineering patterns you can use immediately.

Holiday season or not, this is a great moment to sharpen your skills.

Slides, code, and the full recording is included and will be available after the session: https://lnkd.in/eW2WZdcf

This is one of the best resources for aspiring AI engineers, that covers all the basics - advanced in classroom teaching: https://lnkd.in/dgr5zEve

Highly recommend saving this so you can come back to it later.

Goldmine for aspiring AI Engineers

Lately, I’ve noticed something interesting in the AI startup world.

The ones growing the fastest aren’t building the next big model.

They’re building agentic systems.
AI that doesn’t just talk, it does.
Copilots that summarize meetings.

Agents that predict churn or handle internal ops automatically.

These aren’t demos anymore. They’re real products driving revenue.

What separates the ones that scale from the ones that stall comes down to one thing : Data.
Every great agent lives and dies by how it manages memory, retrieval, and reasoning speed.
And that’s exactly why so many founders I work with build on MongoDB Atlas.

It’s not just a database.
It’s the data layer that brings structured, unstructured, and vector data together so your AI can actually reason in real time.

If you’re a startup building AI that acts, not just talks, go check out the MongoDB Startup Program: https://fandf.co/493VsD1

#MongoDBPartner

🚨Context Engineering for GenAI Workshop happening tomorrow!

In this session, we’ll dive deep into:
→ What context really means in LLMs and how it shapes performance

→ The toolkit that helps you engineer it better: Summarization, Pruning, Query Rewrite, RAG, Context Offloading, Isolation, and Memory

→ How to optimize for accuracy, cost, and reliability across use cases

→ A live demo showing how these ideas come together in real time

This workshop is for every engineer who wants to move beyond prompt tuning and start thinking like a system designer.

🧩 Context Engineering
📅 November 8
🕐 1.5 hours
🎙️ with Arvind Narayanamurthy & me

🎟️ Save your spot: https://lnkd.in/dvFmMiRr

If you’re an AI engineer today, you should absolutely get familiar with Model Context Protocol (MCP) and Responses API. These two are becoming the backbone of modern agentic systems.

Let’s unpack why 👇

1️⃣ MCP: The Connector Layer

Model Context Protocol (MCP) standardizes how models connect to external tools, APIs, and live data.

Think of MCP as the wiring system that allows your LLM to talk to the outside world, like tools, databases, vector stores, or your company APIs.

When you build or host your own MCP server, you’re giving your model new abilities to reason, act, and fetch context beyond what’s in its prompt.
This is what turns a plain LLM into an agent.

If you’re working on production-grade AI systems, you’ll often need to:
→ Integrate custom tools or data sources
→ Maintain context across multiple steps
→ Ensure reliable tool invocation and streaming

MCP makes all that possible.

2️⃣ Fireworks AI Responses API

Once your MCP setup is ready, the Responses API is how you connect it to the model.

It’s a next-generation SDK that powers conversational AI. It supports:
→ Stateful conversations (previous_response_id)
→ Real-time streaming for interactive apps
→ Tool calling and safe execution

The Fireworks Responses API follows the same schema and format as OpenAI’s Responses endpoint.

So if you’ve been using OpenAI models, you can switch to open-source models on Fireworks AI with almost zero code change.

You’ll unlock faster inference, lower latency, and broader model choice, including open models like Qwen, Mistral, Llama, and Gemma hosted natively on Fireworks AI.

You can even mix and match:
✅ Use MCP to extend model capabilities
✅ Use Responses API to orchestrate reasoning, tool use, and streaming

That’s the magic combo powering many of today’s most advanced agentic systems.

3️⃣ Why This Matters

Agentic systems aren’t built with just a model call.
They’re built with context, memory, and actions. MCP and the Responses API make that orchestration reliable, stateful, and production-ready.

If you’re serious about building enterprise-grade AI apps, start experimenting with:
→ MCP Examples (connecting to MCP servers and invoking tools)
→ Conversation Chaining (using previous_response_id to continue dialogue)
→ Streaming Output (for responsive real-time interfaces)
→ Ephemeral Mode (for privacy-friendly, non-persistent interactions)

Happy Building 🚀
〰️〰️〰️
Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

If you’re an AI engineer or an AI leader looking to level up your skills in LangChain and LangGraph, this one is for you.

I’m teaming up with Arvind Narayanamurthy  for a beginner–intermediate, 1.5 hour hands-on workshop where you’ll learn how to move from simple prompts to real AI applications and agentic workflows.

Why this matters
- Most engineers know how to prompt. Few know how to orchestrate.
- LangChain and LangGraph are becoming the backbone of production AI systems.
- If you want to build or lead AI engineering teams, you need these mental models.

What you’ll learn
- Foundations of LLM orchestration and where LangChain fits
- Essential building blocks: chat models, prompting patterns, structured outputs, LCEL
- Tool calling with MCP adapters
- RAG basics end to end
- LangGraph workflows and agentic architectures
- Two live labs: a micro-app and a simple agent
- Quick dive into LangSmith for debugging

The workshop is recorded, and everyone gets the replay.

If you want practical, real-world skills to build modern AI systems, this is the one to sign up for.

Grab your seats: https://lnkd.in/eW2WZdcf

Are you wondering where the AI industry is actually headed? 
Every week brings a new framework, a new toolkit, a new acronym. But beneath all the noise, one truth is becoming clearer: the real moat isn’t the model, it’s the combination of data and AI. 
 
AI is the toolkit. 
Data is the substance. 
AI gives us reasoning power. 
Data gives us understanding. 

That connection came to life at Snowflake BUILD 2025, where the  focus wasn’t on hype, it was on grounding AI in reality. 
 
We saw how Snowflake Intelligence lets teams ask complex questions in natural language and get trusted answers from governed data. How the Cortex Agents API enables developers to build domain-specific agents that reason and collaborate. And how the Managed MCP Server securely bridges external AI systems to enterprise data, turning “agentic AI” from an idea into something production-ready. 
 
Even unstructured data, the 80 % of information hidden in PDFs, chats, and documents, is now first-class and accessible through simple AISQL queries. 
 
This feels like a turning point. 
The next era of AI won’t be defined by bigger models, but by deeper integration between data and reasoning. 
 
AI without data is just potential. 
Data without AI is just storage. 
Together, they become intelligence. 
 
#SnowflakePartner #SnowflakeBUILD

If you’re an AI engineer trying to understand agentic systems, keep reading 👇

A single-agent system relies on one model to do everything: reasoning, planning, execution, and validation. It’s efficient for simple, sequential tasks but quickly breaks down as workflows grow more complex or interdependent.

A multi-agent system, on the other hand, distributes intelligence across specialized agents that collaborate. Each has its own role, memory, & context - and together, they solve problems that a single model can’t handle alone.

But here’s what most people miss: there’s no standard architecture.

A multi-agent system is like Lego blocks, you design it for your use case.

You can build:
→ a supervisor agent managing sub-agents
→ a hierarchical setup with layered control
→ or a hybrid model combining centralized planning and peer coordination
The challenge isn’t in building agents, it's in managing how they talk to each other.

Three reasons multi-agent systems often fail:

❌Memory fragments across agents.
Each agent keeps its own context. When one agent hands work to another, subtle details get lost or duplicated. Without a shared memory layer or context synchronization, agents start “forgetting” what the others already know.

❌Operational costs multiply.
What costs $0.10 for a single agent might cost $1 when ten agents coordinate. Every message, validation step, or context handoff adds latency, token cost, and complexity. At scale, coordination overhead often outweighs the benefit of specialization.

❌ Write conflicts cascade.
If multiple agents write to the same source of truth: a shared database, knowledge base, or document, their changes can collide. One agent updates a schema, another updates logic on top of the old version, and suddenly the system’s state fragments.

TL;DR: Not every workflow needs a multi-agent setup.

When tasks are independent, read-heavy, and parallelizable, multi-agent systems shine. But for sequential logic and tight dependencies, a single-agent pipeline is still faster, cheaper, and easier to maintain.

As always, Galileo has published a new phenomenal free handbook- Mastering Multi-Agent Systems.
 
If you want to go deeper into how these systems work, why they fail, how to evaluate them, explore different architectures, and even build LLMOps around them, this is one of the best reads out there: https://lnkd.in/diptdEC2

#GalileoPartner

Multi-Agent AI System Cheatsheet 

I learned Design Thinking on my first day as a data scientist at IBM, and it quietly became one of the most useful mindsets in my AI career. It taught me to slow down, understand the real problem, and build solutions that actually land with users.

Here’s how I still use it today:

→ Solve the right problem
Most AI teams jump into modeling too early. Design Thinking forces you to reframe, question assumptions, and check if the problem is even defined correctly.  

→ Diverge before you converge
Explore many possibilities, then narrow down. This prevents tunnel vision and leads to better architectures, better experiments, and better product decisions

→ Use the 2x2 grid
Am I exploring or deciding?
Am I thinking about the problem or the solution?
This simple check saves me from going down the wrong path.  

→ Observe behavior, not opinions
The real insights come from watching users, not asking them. The same applies when evaluating agents, prototypes, or internal tools.  

→ Prototype fast, assign ownership
Ideas die when nobody owns them. Execution is where Design Thinking turns into impact.  

This mindset shaped how I build AI systems then, and it still shapes how I build them now.

♻️Share this with your network

Design Thinking Framework

Last week I came home to a little surprise from OpenAI filled with their new books, 100 Chats for Studying, Career, and College Life. 

I flipped through them right away and loved how visual and genuinely interesting they’ve made each section. It feels approachable, practical, and fun in a way most tech books don’t even try for.

If you want to explore the prompts yourself, you can check them out at this link: https://lnkd.in/ddycaDAg

It’s a pretty cool resource to keep on your desk as you build, study, or just experiment with new ideas.

Here is my absolute fav one which I wish I could have used as a student 👇  
I want to understand [concept]. Explain it with an analogy to real-world items.

Let me know in the comments below which ones is your favorite?

If you’re getting started as an AI engineer, this one’s for you 👇

Here are 12 things you need to learn if you want to become a real AI engineer (or level up as one):

1️⃣ LoRA & qLoRA fine-tuning
Learn low-rank adaptation to fine-tune massive models while keeping GPU and memory usage in check.
https://lnkd.in/dxAfNJjP
https://lnkd.in/dcfhk4YB

2️⃣ Reinforcement Learning from Human Feedback (RLHF)
Understand how to align model behavior with human expectations using curated reward signals.
https://lnkd.in/da4qf5JV
https://lnkd.in/drEbQ4x2

3️⃣ Reinforcement Learning from Verifiable Rewards (RLVR)
Use simulated or programmatic reward models when human feedback is too expensive.
https://lnkd.in/dFBgDAyd

4️⃣ RAG Fundamentals
Learn how to build Retrieval-Augmented Generation systems- ingest documents, create embeddings, stitch context, and prompt LLMs.
https://lnkd.in/dFkgD6RQ
https://lnkd.in/d7v6DzEG

5️⃣ Advanced RAG Architectures
Master multi-hop retrieval, hybrid sparse-dense search, and complex reranking for multi-document use cases.
https://lnkd.in/dUrAz2D3

6️⃣ Embedding Models & Vector Databases
Benchmark sentence encoders, tune indexes (FAISS/Pinecone), and understand latency vs recall trade-offs.
https://lnkd.in/d8zeWbmn
https://learn.pinecone.io/

7️⃣ Prompt Engineering & Chain-of-Thought
Design prompts that guide reasoning step-by-step, manage templates, and dynamically inject context.
https://lnkd.in/djbJD578
https://lnkd.in/d9M75H_i

8️⃣ MCP & Agent-to-Agent Integration
Use Model Context Protocol (MCP) to coordinate LLMs with APIs and other agents for seamless A2A handoffs.
https://lnkd.in/dfjvxhvP
https://lnkd.in/dJ5NWuaK

9️⃣ Long-Context Window Strategies
Handle 100k+ token sequences using smart chunking, hierarchical retrieval, and memory-augmented architectures.
https://lnkd.in/djgkXGaZ

🔟 Model Efficiency: Quantization & Distillation
Shrink models with post-training quantization (4/8-bit), pruning, and knowledge distillation for faster, cheaper inference.
https://lnkd.in/d2iurEyv

1️⃣1️⃣ LLMOps & Model Monitoring
Build CI/CD pipelines for LLM deployment, track drift, monitor hallucinations, and set up alerting for cost or performance issues.
https://lnkd.in/dxik45e6

1️⃣2️⃣ Evaluation & Benchmarking
Measure with perplexity, ROUGE, F1, and human evaluations. Automate benchmarks to compare models at scale.
https://lnkd.in/dP_NJXg9

〰️〰️〰️〰️
♻️ Share this with your network 
🔔 Follow me (Aishwarya Srinivasan) for data & AI insights, and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

If you’re getting started with building AI agents, this Agentic AI Toolkit Cheatsheet will come in handy 👇

You’ll quickly realize it’s not just about choosing a model. You need orchestration, memory, retrieval, evaluation, and a solid deployment stack.

And if you’re not careful, your architecture turns into a mess of disconnected tools.

That’s why I put together this Agentic AI Toolkit Cheatsheet.

It organizes the full stack of what goes into building production-grade AI agents- from model hosting to feedback loops, so you can stop guessing what’s missing in your pipeline and start shipping actual systems.

Here’s what it covers:

🔧 Foundation Models & Hosting
🎛️ Model Customization (SFT, RFT, RLHF)
🤖 Agent Frameworks
🧩 Orchestration & Workflow
📥 Data Ingestion & Preprocessing
🧠 Embedding & Retrieval
🧷 Memory & Knowledge Storage
🔌 Tool Integration
🧪 Evaluation & Monitoring
🖥️ User Interfaces
🚀 Deployment & Hosting


💡 A few tips if you’re working on your first agentic stack:

→ Choose one tool per layer to start, depth over breadth.
→ Build iteratively and introduce evaluation as early as possible.
→ Use event-driven workflows to manage complexity.
→ Think about memory and feedback as first-class components, not afterthoughts.


📌 Bookmark this.
📚 Use it to audit your stack.
🛠️ Build with it.
📤 Share it with a teammate who’s lost in the sea of GenAI tooling.

〰️〰️〰️
Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

This Week of AI is here⚡️
Another packed week in the world of AI- here’s your quick TL;DR of everything that happened across the AI ecosystem this week 👇

- Apple Forges Billion-Dollar AI Alliance with Google for Siri Overhaul
- Anthropic Foils First Documented Large-Scale Autonomous AI Cyberattack
- World Labs Unveils Marble, A Multimodal World Model for Spatial Intelligence
- OpenAI Rolls Out GPT-5.1 with Adaptive Reasoning and Dual Personalities
- Microsoft Creates MAI Superintelligence Team Led by Mustafa Suleyman
- Google Launches ‘Agentic Checkout’ and AI Shopping Tools for Holiday Season
- IBM Joins OpenSearch Software Foundation as Premier Member

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🔔 Follow me (Aishwarya Srinivasan) for no BS data and AI updates and insights

This week, the AI landscape was defined by blockbuster partnerships, a major infrastructure upgrade, and a chilling preview of autonomous cyber warfar

Open article: This Week in AI: Week of 16th November 2025 by Aishwarya Srinivasan • 3 min read

This Week in AI: Week of 16th November 2025

Capco

AI Leadership & GTM Strategy | Driving Growth and Market Impact

Perplexity

- Selected for the Perplexity AI Business Fellowship, a competitive program for business leaders focused on AI adoption and innovation.

- Learn directly from industry leaders, including CEOs and founders from top tech companies.

- Engage in collaborative projects and case studies centered around real-world AI use cases and strategic implementation.

- Gain access to a global network of peers and experts in AI, technology, and business.

- Deepening expertise in AI, hands-on experience with emerging technologies, and developing strategies to drive AI-powered business solutions.

Perplexity Business Fellow

- Accelerated and augmented  through AI-powered frameworks integrating LLMs (OpenAI, Claude, Gemini) and tools 
- Secured multi-million-dollar deals by aligning AI and emerging tech with business objectives through high-impact sales pitches and strategic advisory.

- Boosted operational efficiency by enabling Fortune 500 clients to adopt automation, AI, and cloud solutions in transformative projects.

- Optimized IT investments and future-proofed infrastructures with AI adoption strategies and roadmaps for North America, UK/EMEA, and APAC, advising C-suite leaders.

- Enhanced system scalability and streamlined operations, leading cross-functional teams in rapid digital transformation initiatives.

- Bridged technical and business strategy to transform complex challenges into actionable solutions, collaborating with architects, engineers, and leadership.

Digital Transformation Leader & Applied Gen AI

PwC UK

- Built scalable cloud practices by architecting a digital customer strategy within Microsoft Azure, aligning resources and security to meet compliance, enabling a 30% reduction in time to deployment.

- Optimized digital transformation across EMEA by advising clients in Italy, Switzerland, Norway, and the UK, resulting in a 25% improvement in operational efficiency and streamlined workflows.

- Enhanced proposal development by collaborating with regional teams, crafting strategies that met business needs, and securing a 20% increase in successful project bids.

- Accelerated modernization by assessing legacy architectures, identifying automation opportunities, and delivering roadmaps that led to a 40% reduction in system downtime and a 15% boost in data-driven decisions.

- Ensured transparent project tracking by presenting regular updates to senior leadership, maintaining alignment with project goals and achieving 95% on-time delivery of transformation milestones.

Senior Manager

- Boosted client outcomes by refining business processes and implementing automation strategies, reducing response time by 25%.

- Improved operational performance through IT and application architecture assessments, identifying automation opportunities and enhancing process efficiency.

- Reduced project delays by 20% by maintaining strategic alignment and delivering consistent updates to C-suite leaders, ensuring milestones were met.

- Kept clients ahead of the curve by providing insights into AI, automation, and emerging technologies, driving improved digital strategy and execution.

- Delivered impactful solutions by collaborating with cross-functional teams, aligning technology strategies with client goals, and ensuring successful project delivery.

Ernst & Young LLP

- Ensured scalability and performance by designing and implementing enterprise solution architectures, enabling seamless integration across cloud and on-premise environments.

- Aligned IT investments with business objectives by developing technology roadmaps and modernization strategies that supported long-term growth.

- Optimized infrastructure, security, and application development by leading cross-functional teams in successful technology transformation projects.

- Enhanced data flow and interoperability by defining integration frameworks and system design principles across enterprise platforms.

- Drove operational efficiency by collaborating with business and IT leaders to assess technology needs and architect tailored solutions.

- Expanded leadership expertise in IT transformation by establishing a strong foundation in solution architecture, leading enterprise-scale digital and AI-driven modernization at PwC.

Digital Transformation Architect

- Built strong client relationships by understanding business goals and technical requirements, ensuring tailored solutions.

- Ensured project success by guiding the development of technical specs, blueprints, and implementation plans.

- Enabled long-term success by creating strategic technology roadmaps that improved operational efficiency.

- Exceeded client expectations by designing scalable, secure, and high-performance solutions.

- Streamlined systems by developing comprehensive architectures for seamless integration across technologies.

- Guaranteed success by identifying and mitigating risks, ensuring smooth implementation.

- Captured insights by interviewing key business users to gather detailed information on processes and requirements.

Senior Consultant

Collabera

- Ensured scalable and secure enterprise applications by leading development efforts and seamlessly integrating with existing systems.

- Optimized business processes by architecting and implementing technology solutions that enhanced system performance.

- Delivered custom-built solutions by collaborating with cross-functional teams to meet specific business needs.

- Ensured best practices by conducting technical reviews and system assessments for high-quality software delivery.

- Improved operational efficiency by aligning technology solutions with business goals, resulting in enhanced customer satisfaction.

- Established a strong foundation in solution architecture which led to leadership roles in broader transformation and IT initiatives.

Technology Lead

Helping Leaders Adopt Gen AI with Clarity | AI Evangelist | AI x Transformation | Ex-Big 4 | Perplexity Fellow | 14K+ Community Builder

𝑻𝒐𝒎𝒐𝒓𝒓𝒐𝒘’𝒔 𝒍𝒆𝒂𝒅𝒆𝒓𝒔 𝒘𝒐𝒏’𝒕 𝒃𝒆 𝒓𝒆𝒑𝒍𝒂𝒄𝒆𝒅 𝒃𝒚 𝑨𝑰-𝒕𝒉𝒆𝒚’𝒍𝒍 𝒃𝒆 𝒅𝒆𝒇𝒊𝒏𝒆𝒅 𝒃𝒚 𝒉𝒐𝒘 𝒕𝒉𝒆𝒚 𝒉𝒂𝒓𝒏𝒆𝒔𝒔 𝒊𝒕.

Note: The postings on this site are my own and do not necessarily represent the position, strategy, or opinions of the company i work for and its brands.


Trusted advisor that guides businesses/ Leaders on their AI adoption journey and helping them turn visionary ideas into business impacts.

Blending my technical expertise with a strategic style, helping business and non technical leaders navigate the complexities of AI and modern technologies.

Throughout my career, I’ve had the privilege of partnering with executives to modernize enterprise operations, creating agile, scalable, and innovative solutions.

My focus has always been to align technology with business goals, ensuring that every decision empowers companies to be more efficient, forward-thinking, and data-driven.

From process automation to data-driven decision-making, my goal is to help businesses not just adapt, but thrive in the new AI-driven landscape.

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Google is building one of the most diverse and powerful AI ecosystems — covering everything from image generation and UI design to personal research assistants and AI-driven development tools.

Here's the breakdown of the key products shaping Google’s next wave of AI innovation and how each fits into the broader ecosystem.

Here’s what each tool brings to the table:

1. Gemini Gems:
Custom AI assistants fine-tuned for personal goals, workflows, and business needs.

2. NotebookLM:
An AI research assistant that organizes, summarizes, and queries your personal documents.

3. Google Pomelli:
AI-powered marketing tool that creates brand-consistent social media content for businesses.

4. Google Stitch:
A design-focused AI that converts text prompts into exportable UI layouts.

5. Google AI Studio:
A web-based IDE for prototyping apps using Google’s generative models — built for developers.

6. Google Opal:
A no-code tool to build and share lightweight AI-powered micro-apps instantly.

7. Google Whisk:
An experimental AI from Labs for image remixing and creative visual transformations.

8. Google Imagen 3:
Ultra-realistic text-to-image generation with exceptional typography and fine-detail rendering.

9. Nano Banana:
Google DeepMind’s model for high-quality image creation and editing.

10. Google Lumiere:
An advanced video generation model creating realistic, coherent footage directly from text input.

Google’s AI ecosystem isn’t just a collection of tools, it’s a full stack of creativity, productivity, research, and development capabilities. As these systems integrate more deeply, they will redefine how businesses build, design, create, and automate in the AI-first world.

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𝐀𝐈 𝐢𝐬 𝐚𝐧 𝐞𝐧𝐭𝐢𝐫𝐞 𝐬𝐩𝐞𝐜𝐭𝐫𝐮𝐦 𝐨𝐟 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞.
From simple rule-based systems to models that can reason, plan, and even feel - each type of AI represents a new step toward autonomy and awareness.

𝐇𝐞𝐫𝐞'𝐬 𝐚 𝐛𝐫𝐞𝐚𝐤𝐝𝐨𝐰𝐧 𝐨𝐟 𝐭𝐡𝐞 𝟖 𝐦𝐚𝐣𝐨𝐫 𝐭𝐲𝐩𝐞𝐬 𝐨𝐟 𝐀𝐈 𝐚𝐧𝐝 𝐰𝐡𝐞𝐫𝐞 𝐭𝐡𝐞𝐲 𝐬𝐭𝐚𝐧𝐝 𝐭𝐨𝐝𝐚𝐲:

𝟏. 𝐑𝐞𝐚𝐜𝐭𝐢𝐯𝐞 𝐀𝐈
The earliest form - responds to inputs using fixed rules. Think of IBM’s Deep Blue, the chess-playing AI.

𝟐. 𝐍𝐚𝐫𝐫𝐨𝐰 𝐀𝐈
Designed for specific tasks like translation or speech recognition. This is where Siri and Alexa live.

𝟑. 𝐂𝐨𝐠𝐧𝐢𝐭𝐢𝐯𝐞 𝐀𝐈
Learns, reasons, and adapts - mimicking aspects of human thinking. IBM Watson paved the way here.

𝟒. 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈
Creates new data - text, images, or code — from existing patterns. The power behind ChatGPT, Midjourney, and Gemini.

𝟓. 𝐄𝐦𝐨𝐭𝐢𝐨𝐧𝐚𝐥 𝐀𝐈
Detects and interprets human emotions through voice or expressions. Tools like Replika and Affectiva explore empathy in machines.

𝟔. 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈
The next big leap — AI that plans, acts, and adapts on its own. Frameworks like CrewAI and LangGraph are early examples.

𝟕. 𝐀𝐆𝐈 (𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐆𝐞𝐧𝐞𝐫𝐚𝐥 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞)
Theoretical human-level intelligence capable of learning any task. DeepMind’s Gato concept points in this direction.

𝟖. 𝐀𝐒𝐈 (𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐒𝐮𝐩𝐞𝐫 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞)
A hypothetical stage where AI surpasses human intellect and creativity entirely.

AI has come a long way - but the question remains:
Are we building smarter tools or the next form of intelligence?

Save this comparison for later and share your thoughts -
Which stage of AI do you think we’ll reach first in our lifetime?

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𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 𝐢𝐬 𝐞𝐱𝐜𝐢𝐭𝐢𝐧𝐠 - 𝐮𝐧𝐭𝐢𝐥 𝐲𝐨𝐮 𝐫𝐞𝐚𝐥𝐢𝐳𝐞 𝐡𝐨𝐰 𝐜𝐨𝐦𝐩𝐥𝐞𝐱 𝐚𝐮𝐭𝐨𝐧𝐨𝐦𝐲 𝐫𝐞𝐚𝐥𝐥𝐲 𝐢𝐬.
When systems begin to plan, reason, and act on their own, the challenge is not building them - it is controlling them.

𝐇𝐞𝐫𝐞 𝐚𝐫𝐞 𝐭𝐡𝐞 𝟕 𝐛𝐢𝐠𝐠𝐞𝐬𝐭 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬 𝐟𝐚𝐜𝐢𝐧𝐠 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐭𝐨𝐝𝐚𝐲:

𝟏. 𝐇𝐚𝐥𝐥𝐮𝐜𝐢𝐧𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐧𝐭𝐫𝐨𝐥
Ensuring agents don’t generate false or fabricated outputs due to poor grounding or reasoning gaps.

𝟐. 𝐓𝐚𝐬𝐤 𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧
Maintaining accuracy across dynamic, multi-step workflows with consistent goal tracking and sub-task verification.

𝟑. 𝐒𝐚𝐟𝐞𝐭𝐲 𝐀𝐥𝐢𝐠𝐧𝐦𝐞𝐧𝐭
Preventing harmful or unethical actions while ensuring agents adhere to safety and compliance boundaries.

𝟒. 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧
Balancing compute, APIs, and orchestration costs — especially in large multi-agent environments.

𝟓. 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐑𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠
Reducing decision latency, managing context-switching, and keeping adaptive reasoning under time pressure.

𝟔. 𝐓𝐨𝐨𝐥 𝐑𝐞𝐥𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲
Handling API failures, version mismatches, and tool inconsistencies that break execution flows.

𝟕. 𝐄𝐭𝐡𝐢𝐜𝐚𝐥 𝐎𝐯𝐞𝐫𝐬𝐢𝐠𝐡𝐭
Ensuring fairness, transparency, consent tracking, and accountability in agent decision-making.

As Agentic AI evolves, the goal isn’t just smarter agents - it is responsible autonomy.
Because the future of AI won’t be defined by how much it can do, but by how safely and ethically it does it.

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Context engineering is becoming one of the most important skillsets in the AI era — because the quality of an AI system’s output depends entirely on the quality of context it receives.

This framework breaks down the six pillars that shape how AI understands, reasons, retrieves, and responds with accuracy and relevance.

Here’s what each component contributes:

🔹 Prompt Techniques
- Tree of Thoughts (ToT): Helps the model explore multiple reasoning paths and choose the optimal answer.
- ReAct Prompting: Blends reasoning (“think”) with action (“act”), allowing the model to use tools, gather data, and refine responses iteratively.

🔹 Memory
- Short-Term Memory: The model’s immediate context window — everything currently “in view” that shapes its next step.
- Long-Term Memory (ReAct-based): External vector storage that allows AI to remember past interactions, facts, and patterns over time.

🔹 Retrieval
Retrieval pipelines break queries into chunks, embed them, fetch relevant knowledge from vector stores, and feed enriched context back into the LLM for more accurate generation.

🔹 Query Augmentation
LLMs rewrite vague or incomplete queries into precise, structured prompts — enabling better problem-solving and more accurate output.

🔹 Agents
AI agents reason step-by-step, use tools adaptively, access memory, decompose problems, and switch strategies dynamically when one approach fails.

🔹 Tools
External tools expand an AI system’s capabilities — enabling database queries, API calls, file operations, search, and multi-step workflows through structured integrations.

Context engineering is the hidden layer that transforms AI from a simple text generator into a reliable reasoning engine. When these six components work together — prompting, memory, retrieval, augmentation, agents, and tools — AI systems become dramatically smarter, more accurate, and far more capable of solving real-world problems.

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𝐖𝐞 𝐡𝐚𝐯𝐞 𝐭𝐚𝐮𝐠𝐡𝐭 𝐀𝐈 𝐭𝐨 𝐭𝐚𝐥𝐤, 𝐝𝐫𝐚𝐰, 𝐚𝐧𝐝 𝐜𝐨𝐝𝐞 - 𝐧𝐨𝐰 𝐰𝐞 𝐚𝐫𝐞 𝐭𝐞𝐚𝐜𝐡𝐢𝐧𝐠 𝐢𝐭 𝐭𝐨 𝐚𝐜𝐭.
That is the essence of Agentic AI - systems that do not just respond, but reason, plan, and execute tasks on their own.

𝐓𝐡𝐢𝐬 𝐜𝐡𝐞𝐚𝐭 𝐬𝐡𝐞𝐞𝐭 𝐛𝐫𝐞𝐚𝐤𝐬 𝐝𝐨𝐰𝐧 𝐡𝐨𝐰 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐰𝐨𝐫𝐤𝐬 𝐛𝐞𝐡𝐢𝐧𝐝 𝐭𝐡𝐞 𝐬𝐜𝐞𝐧𝐞𝐬:

𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬
They operate autonomously - Using reasoning, memory, and tools to achieve specific goals without human prompts.

𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 𝐓𝐲𝐩𝐞𝐬
- Reactive Agents respond instantly.
- Planning Agents reason and execute multi-step tasks.
- Collaborative Agents coordinate with other agents or humans.

𝐀𝐠𝐞𝐧𝐭 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞
Planner decides what to do, Executor performs actions, Memory tracks progress, and Interface connects everything to users or systems.

𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐏𝐚𝐭𝐭𝐞𝐫𝐧𝐬
ReAct, Plan–Execute, Reflection Loop, and Team Agents — each pattern defines how agents learn, adapt, and collaborate in real time.

𝐀𝐠𝐞𝐧𝐭 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞𝐬
From automating business workflows and analyzing data to building creative systems that plan, design, and optimize on their own.

𝐀𝐠𝐞𝐧𝐭 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬
LangChain, LlamaIndex, CrewAI, AutoGen, BabyAGI, Semantic Kernel - frameworks that turn simple prompts into multi-step autonomous workflows.

𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧 𝐌𝐞𝐭𝐫𝐢𝐜𝐬
Measured by reasoning success, planning efficiency, execution accuracy, and safety compliance.

𝐓𝐨𝐨𝐥𝐬 & 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦𝐬
From memory databases like Chroma to vector stores like Milvus and hosting with OpenAI or Hugging Face - these power the agent ecosystem.

Agentic AI is not about replacing humans - it is about building digital teammates that think, plan, and act alongside us.

What is your take, are autonomous AI agents the future of work or a step too far?

Save this for later and share your thoughts below.

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𝐄𝐯𝐞𝐫 𝐰𝐨𝐧𝐝𝐞𝐫𝐞𝐝 𝐰𝐡𝐚𝐭 𝐭𝐫𝐮𝐥𝐲 𝐩𝐨𝐰𝐞𝐫𝐬 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐭 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐥𝐢𝐤𝐞 𝐆𝐏𝐓, 𝐂𝐥𝐚𝐮𝐝𝐞, 𝐨𝐫 𝐆𝐞𝐦𝐢𝐧𝐢?

Behind every smart AI conversation lies a network of powerful concepts - from how models understand language and context to how they learn, remember, and reason like humans.

𝐇𝐞𝐫𝐞’𝐬 𝐰𝐡𝐚𝐭 𝐢𝐭 𝐜𝐨𝐯𝐞𝐫𝐬:
🔹 Encoders & Decoders — Encoders grasp meaning from input data, while decoders turn those insights into coherent, structured responses.
🔹 Transformers Pre-training — The backbone of LLMs, using attention mechanisms to process entire sequences efficiently and understand contextual relationships.
🔹 BERT — A model trained to read in both directions, helping AI comprehend context deeply and produce better language understanding.
🔹 Transformer-Decoder Architecture — Powers models like GPT by predicting next words using prior context and attention mechanisms.
🔹 RLHF (Reinforcement Learning from Human Feedback) — Teaches AI to align with human preferences, improving the quality and reliability of responses.
🔹 Attention Types — Enable models to focus on the most relevant words, enhancing comprehension and factual precision.
🔹 Memory — Allows LLMs to store and recall context, ensuring smoother multi-turn conversations and better continuity.
🔹 RAG (Retrieval-Augmented Generation) — Combines external knowledge retrieval with generation for more accurate and up-to-date answers.
🔹 Embedding — Converts text into vector space, letting AI understand meaning, similarity, and relationships between words.

Each concept is a building block - together they form the foundation of modern AI as we know it.

Here's a guide simplifies the core building blocks of Large Language Models (LLMs) - helping you understand how today’s AI learns, thinks, and responds in the real world 👇

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𝐀𝐠𝐞𝐧𝐭-𝐭𝐨-𝐀𝐠𝐞𝐧𝐭 (𝐀𝟐𝐀) 𝐜𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐭𝐡𝐞 𝐌𝐨𝐝𝐞𝐥 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐏𝐫𝐨𝐭𝐨𝐜𝐨𝐥 (𝐌𝐂𝐏) 𝐚𝐫𝐞 𝐪𝐮𝐢𝐜𝐤𝐥𝐲 𝐛𝐞𝐜𝐨𝐦𝐢𝐧𝐠 𝐭𝐡𝐞 𝐛𝐚𝐜𝐤𝐛𝐨𝐧𝐞 𝐨𝐟 𝐡𝐨𝐰 𝐦𝐨𝐝𝐞𝐫𝐧 𝐀𝐈 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐜𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐞 — 𝐚𝐧𝐝 𝐭𝐡𝐢𝐬 𝐝𝐢𝐚𝐠𝐫𝐚𝐦 𝐬𝐡𝐨𝐰𝐬 𝐞𝐱𝐚𝐜𝐭𝐥𝐲 𝐡𝐨𝐰 𝐭𝐡𝐞 𝐭𝐰𝐨 𝐰𝐨𝐫𝐤 𝐭𝐨𝐠𝐞𝐭𝐡𝐞𝐫.
Instead of one AI model trying to do everything, A2A and MCP allow multiple specialized agents to coordinate, share context, and execute tasks using the right tools at the right time.

𝐇𝐞𝐫𝐞’𝐬 𝐭𝐡𝐞 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰 𝐛𝐫𝐨𝐤𝐞𝐧 𝐝𝐨𝐰𝐧:

𝟏. 𝐓𝐡𝐞 𝐔𝐬𝐞𝐫 𝐓𝐚𝐥𝐤𝐬 𝐭𝐨 𝐚 𝐑𝐨𝐮𝐭𝐞𝐫 𝐀𝐠𝐞𝐧𝐭
The user interacts with a single Client/Router Agent, which interprets the request and decides which specialized agent should handle it.

𝟐. 𝐒𝐩𝐞𝐜𝐢𝐚𝐥𝐢𝐳𝐞𝐝 𝐀𝐠𝐞𝐧𝐭𝐬 (𝐀𝟐𝐀) 𝐓𝐚𝐤𝐞 𝐎𝐯𝐞𝐫
The router forwards the task to the right expert agent — each designed for a specific job.
These A2A agents communicate seamlessly, passing context and coordinating actions without the user managing anything manually.

𝟑. 𝐀𝐠𝐞𝐧𝐭𝐬 𝐔𝐬𝐞 𝐓𝐨𝐨𝐥𝐬 𝐯𝐢𝐚 𝐌𝐂𝐏
Each specialized agent connects to the appropriate MCP server, gaining access to structured tools, APIs, and capabilities such as:
- Tool X, Tool Y (from API Hub Z)
- Tool X, Tool Y (from MCP Server X)
- Tool 1, Tool 2 (from MCP Server Y)
MCP standardizes how tools are discovered, connected, and executed — making it easy for agents to find the right capability instantly.

𝟒. 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬 𝐋𝐢𝐤𝐞 𝐜𝐫𝐞𝐰𝐀𝐈, 𝐋𝐚𝐧𝐠𝐂𝐡𝐚𝐢𝐧, 𝐚𝐧𝐝 𝐎𝐭𝐡𝐞𝐫𝐬 𝐏𝐨𝐰𝐞𝐫 𝐒𝐩𝐞𝐜𝐢𝐚𝐥𝐢𝐳𝐞𝐝 𝐀𝐠𝐞𝐧𝐭𝐬
- crewAI → Specialized Agent A
- LangChain → Specialized Agent B
- Other frameworks → Specialized Agent C
This lets teams mix and match tools, agent frameworks, and servers freely.

The Big Picture
- A2A handles intelligent collaboration between agents.
- MCP handles standardized access to tools and APIs.

Together, they create a powerful AI ecosystem where:
- The user makes one request
- The router assigns tasks
- Specialized agents solve problems
- MCP connects them to the tools needed
- The system delivers accurate, multi-step results

This is the future of agent orchestration - modular, interoperable, and tool-aware AI systems that work together like a digital team.

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