✨ From GenAI to RAG – Personalizing the Power of Language Models
“GenAI isn’t just about what AI can generate — it’s about what humans can create with it.”
🎤 AI, Creativity & Real-World Relevance
Last weekend’s workshop — split into “Powering Creativity with GenAI” and “LLMs and RAG” — wasn’t just a tech talk. It was a deep exploration of how AI is revolutionizing the way we create, communicate, and solve problems.
Hosted by MLSA community leads Keerthi AJ and Kalyanasundaram V, this event took attendees from the foundations of Generative AI to one of its most powerful real-world evolutions: Retrieval-Augmented Generation (RAG).
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🎨 Part 1: Fueling Imagination with GenAI
We began by breaking down some big questions:
- Who sparked the GenAI wave?
- Why does it matter more than traditional AI?
- And — what can we actually build with it?
✨ GenAI: From Answers to Artistry
Generative AI is not your typical rules-based system. It creates — poems, essays, summaries, code, designs, even videos.
Think ChatGPT, DALL·E, Sora, GitHub Copilot, or Stable Diffusion — not tools that respond, but tools that collaborate with us.
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🧠 From Statistical Models to Transformers
We walked through the evolution of AI thinking:
- Statistical ML
- Neural Networks
- Recurrent Neural Networks (RNNs)
- The game-changing Transformer architecture
This leap enabled tools like BERT, GPT, and Copilot to process language, code, and context in ways humans intuitively understand.
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🔍 Part 2: The Missing Piece — Context
LLMs are amazing — but not perfect. They hallucinate. They forget. They give brilliant answers... that sometimes make no sense.
So how do we ground them in our world, our documents, and our needs?
🤖 Enter RAG: Retrieval-Augmented Generation
RAG bridges the gap between general AI knowledge and personalized, contextual answers.
Imagine asking a chatbot for vegetarian diabetic meal suggestions — and it suggests honey chicken. That’s an LLM without context.
With RAG, you feed the model relevant, real data at the moment of response — like giving a student cheat notes during an exam.
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🧰 How RAG Works – A Step-by-Step Breakdown
Vector Embeddings
Your documents and query are converted into number-based vectors that reflect meaning.Semantic Search
These vectors are stored in tools like FAISS or Pinecone for intelligent search.Retrieval + Prompt
The top-matching chunks are injected into the LLM’s prompt — producing relevant, grounded responses.
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🛠️ Real Use Cases Where RAG Shines
- 🧑🏫 Education: Summarize lecture slides, generate quizzes, explain topics using your syllabus.
- 🏥 Healthcare: Respond based on dietary restrictions or medical history.
- 🧑💼 Enterprise Support: Internal document-based responses, not vague guesses.
- 🧑⚖️ Legal & Policy: Accurate, private insights drawn from confidential case files.
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⚖️ RAG vs Fine-Tuning — Which One When?
| ✅ RAG | 🔧 Fine-Tuning |
|---|---|
| Perfect for changing, private data | Ideal for tone/stylistic consistency |
| No retraining required | Retraining needed (costly) |
| Dynamic and real-time | Static behavior baked into the model |
| Example: Personalized chatbot | Example: Support bot with empathetic tone |
💡 From Tools to Transformation
This workshop wasn’t just about how AI works — it was a call to build smarter:
“The future of AI isn’t just what it knows. It’s what it can remember, personalize, and co-create with you.”
Whether you're a developer, creator, student, or founder — this is the future you're helping shape.
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📸 A Note of Thanks
A heartfelt thank-you to all attendees, the MLSA community, and our speakers for making this session engaging, hands-on, and vision-driven.
Let’s keep learning, experimenting, and imagining — together.
🎤 Presented by:
- Keerthi AJ – Beta Microsoft Student Ambassador
- Kalyanasundaram V – Gold Microsoft Student Ambassador
✍️ Edited by:
- Deepthi Balasubramanian - Gold Microsoft Student Ambassador
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