Souvik Dey retweetledi
If I Had 6–12 Months to Master LLMs (Large Language Models) in the AI Era, I’d Do This
Stage 1 — AI & LLM Foundations
Start with clarity, not hype
• What AI, Machine Learning, and Deep Learning are
• Where LLMs fit in the ecosystem
• Tokens, context windows, and parameters
• Capabilities vs limitations of LLMs
• Using LLMs as tools, not magic
Stage 2 — Programming & Data Basics
Build the technical foundation
• Python fundamentals
• Working with text and JSON data
• APIs and HTTP basics
• Data preprocessing
• Using AI to understand unfamiliar code
Stage 3 — NLP Fundamentals
Understand language before models
• Tokenization
• Embeddings
• Text preprocessing
• Similarity search
• Classical NLP vs LLM-based NLP
• When to use (and not use) LLMs
Stage 4 — LLM Architecture (High-Level)
Know what’s happening under the hood
• Transformers and attention (conceptual)
• Encoder vs decoder models
• Pretraining vs fine-tuning
• Inference vs training
• Model size vs performance trade-offs
Stage 5 — Prompt Engineering
Learn to control model outputs
• Clear and structured prompts
• Few-shot and zero-shot prompting
• System vs user instructions
• Reducing hallucinations
• Prompt testing and iteration
• Prompt libraries
Stage 6 — Working with LLM APIs
Turn models into real applications
• API integration
• Token and cost management
• Streaming responses
• Error handling and retries
• Rate limiting
• AI-assisted debugging
Stage 7 — Embeddings, Vector DBs & RAG
Build knowledge-aware systems
• Embeddings and semantic search
• Vector databases (FAISS, Pinecone, Weaviate)
• Retrieval-Augmented Generation (RAG)
• Chunking and indexing
• Re-ranking results
• Evaluating retrieval quality
Stage 8 — Fine-Tuning & Custom Models
Customize model behavior
• When fine-tuning is needed
• Dataset preparation
• Instruction tuning
• LoRA (parameter-efficient tuning)
• Evaluation and testing
• Avoiding overfitting
Stage 9 — LLM System Design
Think like an AI engineer
• LLM application architecture
• Tool/function calling
• Agents (high-level concepts)
• Memory and context management
• Guardrails and safety systems
• Cost, latency, and reliability trade-offs
Stage 10 — Deployment & Production
Move from demo to real-world systems
• Backend integration
• Caching responses
• Logging prompts and outputs
• Monitoring usage and cost
• Fallback strategies
• Secure API key handling
Stage 11 — LLM Projects
Build real applications
• AI chatbot
• Document Q&A system
• Code assistant
• Knowledge-base search tool
• AI-powered support system
• Internal productivity tools
Stage 12 — Evaluation, Ethics & Career Growth
Be a responsible LLM developer
• Evaluating outputs
• Bias and hallucination awareness
• Data privacy and security
• Open-source vs proprietary models
• LLM interview preparation
• Staying updated with fast-moving AI trends
Grab the LLMS EBOOK
Large Language Models: From Foundations to Real-World Applications
codewithdhanian.gumroad.com/l/haeit
English
