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If you are trying to *Switch your career to GenAI*, I have created the most practical roadmap after working as an AI / ML Engineer in Generative AI for the past 5 years. Step 1 – Strengthen ML Fundamentals → Know the basics of: ✅ Neural networks ✅ Loss functions and optimization ✅ Overfitting vs generalization ✅ Model evaluation metrics → Even if you won’t train huge models yourself, understanding how they work is crucial. Step 2 – Learn How LLMs Work Dive deeper into: ✅ Transformers (self-attention, positional encoding) ✅ Tokenization and embeddings ✅ Differences between encoder, decoder, and encoder-decoder architectures ✅ Pre-training vs fine-tuning Start with resources like: → Illustrated Transformer blog posts → Papers like “Attention Is All You Need” → YouTube explainers for intuitive understanding Step 3 – Practice Prompt Engineering LLMs are powerful because of good prompts. Learn to: ✅ Design zero-shot, one-shot, and few-shot prompts ✅ Control output style and format (e.g. JSON) ✅ Reduce hallucinations with better prompt wording ✅ Create “chain-of-thought” prompts for reasoning tasks Great playgrounds: OpenAI Playground, Anthropic Console, Gemini Pro UI. Step 4 – Build Something Small Apply what you’re learning. Start tiny: → A text summarizer → A Q&A bot for your documentation → An email re-writer → A chatbot for internal tools Tools to explore: ✅ LangChain ✅ LlamaIndex ✅ Pinecone (for vector search) ✅ Gradio / Streamlit for frontends Step 5 – Understand RAG Systems Retrieval-Augmented Generation (RAG) is everywhere in real-world GenAI apps. ✅ What embeddings are and how they’re stored ✅ How vector databases (e.g. Pinecone, Weaviate, Chroma) work ✅ How to combine retrieval results with an LLM ✅ Pros and cons of RAG vs Fine-tuning Step 6 – Explore Fine-Tuning & Model Customization Companies often want models specialized for their data. ✅ Fine-tuning vs prompt engineering ✅ Parameter-efficient fine-tuning (LoRA, QLoRA, PEFT) ✅ Trade-offs between cost, speed, and accuracy ✅ Tools like Hugging Face and open-source models Step 7 – Think About Deployment & Cost Real-world GenAI = business constraints. Learn about: ✅ Token costs (and how to reduce them) ✅ Latency considerations ✅ Privacy and compliance risks ✅ Caching strategies to lower API calls Step 8 – Stay Current Generative AI changes FAST. Keep learning: → Follow research papers (e.g. arXiv) → Join communities / Follow good writers → Read newsletters → Play with new APIs and open-source releases *Double Tap ❤️ For More*

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