MASTERING PLAN FOR MODERN LLM ENGINEERING.STEP
1: UNDERSTAND THE BASICS OF MACHINE LEARNING
→ Learn core ML concepts (models, training, inference)
→ Understand supervised vs unsupervised learning
→ Learn gradient descent, loss functions, optimization
→ Explore neural networks fundamentals
STEP 2: LEARN PYTHON AND ML LIBRARIES
→ Master Python basics and advanced features
→ Use NumPy, Pandas for data handling
→ Learn plotting libraries (Matplotlib, Seaborn)
→ Work with ML frameworks (TensorFlow, PyTorch)
STEP 3: UNDERSTAND NATURAL LANGUAGE PROCESSING (NLP)
→ Tokenization, embeddings, attention
→ Text preprocessing and normalization
→ Language model basics
→ Sequence models and transformers
→ Evaluation metrics for NLP
STEP 4: GET DEEP INTO TRANSFORMER ARCHITECTURE
→ Learn self-attention mechanisms
→ Understand positional encoding
→ Explore encoder vs decoder vs encoder-decoder models
→ Study scaling laws and performance trade-offs
STEP 5: EXPLORE LARGE LANGUAGE MODELS (LLMs)
→ Understand what LLMs are and how they work
→ Study popular models (GPT series, Claude, LLaMA, etc.)
→ Learn about token limits, context windows, latency, inference costs
→ Explore zero-shot, few-shot, and fine-tuning paradigms
STEP 6: PRACTICAL LLM USAGE VIA APIS
→ Learn how to use APIs (OpenAI, Cohere, Anthropic)
→ Understand request/response patterns
→ Manage rate limits, keys, security
→ Handle streaming responses
→ Log, handle errors, timeouts, and retries
STEP 7: PROMPT ENGINEERING – DESIGN AND OPTIMIZATION
→ Learn effective prompting techniques
→ Use prompts for tasks (classification, summarization, Q&A, extraction)
→ Chain-of-thought and step-by-step strategies
→ Prompt templates, context packing
→ Use system instructions and persona prompts
STEP 8: BUILD LLM APPLICATIONS
→ Chatbots with memory and context
→ Intelligent search systems
→ Summarization pipelines
→ Question-answering systems
→ Code assistants and task automation
→ LLM-augmented analytics tools
STEP 9: FINE-TUNING AND CUSTOMIZATION
→ Learn when to fine-tune vs use embeddings
→ Create fine-tuning datasets
→ Train and validate tuned models
→ Monitor overfitting, underfitting, evaluation metrics
→ Deploy and test fine-tuned models
STEP 10: EMBEDDINGS AND SEMANTIC SEARCH
→ Understand vector embeddings
→ Use vector databases (Pinecone, Milvus, Weaviate)
→ Build semantic search systems
→ Connect embeddings with retrieval systems
→ Use RAG (Retrieval-Augmented Generation) workflows
STEP 11: SCALABILITY, PERFORMANCE, AND COST OPTIMIZATION
→ Architect systems for scale
→ Use batching, caching, and request queuing
→ Optimize token usage and context placement
→ Manage cloud costs and inference budgets
→ Monitor performance and latency
STEP 12: LLM SAFETY, ETHICS, AND BIAS MITIGATION
→ Understand risks (hallucination, bias, misuse)
→ Learn mitigation techniques (input validation, guardrails)
→ Interpretability basics
→ Privacy and security best practices
STEP 13: DEVOPS FOR LLM SYSTEMS
→ Containerization (Docker)
→ CI/CD pipelines
→ Logging and monitoring (OpenTelemetry, ELK)
→ Model versioning and experiment tracking
→ Deployment strategies (serverless, microservices)
STEP 14: SYSTEM DESIGN AND ARCHITECTURE
→ Design LLM-enabled production systems
→ Plan data pipelines, storage, caching
→ Integrate APIs, databases, event streams
→ Ensure resiliency and fault tolerance
STEP 15: PORTFOLIO AND REAL-WORLD PRACTICE
→ Build real LLM projects
→ Document workflows
→ Write case studies
→ Prepare technically for interviews
MODERN LLM ENGINEERING HANDBOOK
Get the complete Modern LLM Engineering Handbook for deep insights, practical templates, code examples, real world workflows, and scalable architectures:
codewithdhanian.gumroad.com/l/haeit
Dhanian