Ever wondered why sometimes you get misleading answers from generic LLMs? It's like trying to get directions from a confused stranger, right? This can happen for many reasons, some of them are that the LLM is trained on data that is out of date, it cannot do the math or the calculations or it is just hallucinated. That is where RAG comes in.

Traditional RAG systems, while foundational, often operate like a basic librarian - they fetch relevant documents and generate responses based on them. Agentic RAG, on the other hand, operates more like a research team with specialized experts. Let's dive deep into when and why you'd choose one over the other.

As a software developer in 2024, you've probably noticed that AI has fundamentally transformed the way we write code. Gone are the days of endlessly googling syntax or scrolling through Stack Overflow for basic implementations. AI coding assistants have emerged as indispensable tools in a developer's arsenal, promising to boost productivity and streamline the coding process.

But with so many options flooding the market, choosing the right AI coding assistant can feel overwhelming. Should you go with the popular GitHub Copilot, or explore newer alternatives? Is the free tier sufficient for your needs, or should you invest in a premium solution?

This blog is my attempt to explore the current landscape of AI coding assistants, helping you make an informed decision based on your specific needs and circumstances. I will say there are many more AI coding assistants out there, I am only covering a few more well known ones here.

I’m sharing with you the three most mind-blowing discoveries I've made about Microsoft so far. Trust me, some of these might just make you see this tech giant in a whole new light.

While the decision to work at a large corporation or join a startup is a personal one, the experience of navigating a big company ecosystem offers unique opportunities for professional growth. The skills developed in this environment – from mastering the corporate zoo and managing up and sideways, to building diverse networks and finding common ground amidst competing priorities – are invaluable skills that transcend any single job or company. These competencies not only contribute to our success within the organization but also enhance our overall professional toolkit.

The challenges of bureaucracy and complexity in large companies, often seen as drawbacks, can actually be catalysts for developing patience, persistence, and creative problem-solving skills. As you progress in your career, you'll find that the ability to navigate complex organizational structures, influence without direct authority, and align diverse interests are highly transferable skills, serving you well whether you stay in big business, venture into smaller companies, or even start your own enterprise. So, while it may sometimes feel like you're a small cog in a giant machine, remember that you're simultaneously honing a set of powerful, often overlooked skills that will propel your career forward, regardless of where your professional journey takes you next.

Have you ever wondered how Spotify manages to recommend the perfect song for your mood, or how Uber can connect you with a driver in minutes, anywhere in the world? These seamless experiences aren't just magic, and they're not solely the result of AI. They're powered by a powerful approach to infrastructure and applications called Platform Engineering. Let's dive into what Platform Engineering is, how it differs from DevOps, and why it's becoming the secret weapon to increase productivity in tech companies

What are AI Agents?

What is Gemini Flash?

Imagine a large language model (LLM) that's lightweight, super-fast, and cost-effective. That's exactly what Gemini Flash brings to the table. It boasts impressive features like:

Multimodal reasoning: Can handle text, audio, and even code!

Massive context window: Up to 1 million tokens, allowing you to process massive amounts of data (think hours of audio or thousands of lines of code).

Optimized for performance: Delivers high-quality results at a lower cost, perfect for enterprise use.

Over the past couple of years, I've had the privilege of building and launching Gemini Code Assist and Gemini for Google Cloud alongside Google’s talented product and engineering teams. Teaching is a passion of mine, and I've received countless requests to break down the fundamentals of Gen AI. So, I'm thrilled to share that I just put together a videos series "10 Days of Gen AI".

Whether you're a seasoned developer or just dipping your toes into the world of AI, this series will equip you with the knowledge and tools you need to harness the power of Gen AI. Let's embark on this exciting journey together!

Retrieval-Augmented Generation (RAG) leverages the strengths of Large Language Models (LLMs) and external knowledge bases to deliver more informative and accurate outputs. Here's a breakdown of the key components focusing on data chunking, embeddings, vector databases, and their interaction

Large language models are powerful tools, but ensuring their accuracy is essential. Retrieval-Augmented Generation (RAG) emerges as a game-changer, bridging the gap between raw LLM potential and reliable, factual outputs. By harnessing the power of external knowledge bases, RAG empowers LLMs to deliver more informative, contextually relevant, and up-to-date responses across various industries. From personalized e-commerce experiences to enhanced medical diagnosis assistance, the applications of RAG are vast and hold immense promise for the future of Generative AI.