Generative AI & LLM Development Roadmap

Transformer Architecture

Understand attention mechanisms, self-attention, and multi-head attention

NLP Fundamentals

Tokenization, embeddings, language modeling basics

Deep Learning Concepts

Neural networks, optimization, loss functions

Probability & Statistics

Statistical modeling, probability distributions, sampling

Hugging Face Ecosystem

Transformers, datasets, tokenizers libraries

Prompt Engineering

Advanced prompting techniques, chain-of-thought, few-shot learning

Model Fine-tuning

PEFT, LoRA, QLoRA, instruction fine-tuning

Model Evaluation

Metrics, benchmarks, evaluation frameworks

LangChain & LlamaIndex

Building complex LLM applications and chains

OpenAI API Integration

API usage, best practices, token optimization

Retrieval Augmented Generation

Vector databases, embedding, semantic search

AI Agents Development

Autonomous agents, tools, planning systems

Model Optimization

Quantization, pruning, distillation

Deployment Strategies

Model serving, API development, scaling

Monitoring & Logging

Performance tracking, drift detection

Security & Safety

Prompt injection, output filtering, safety measures

Multimodal Models

Text-to-image, vision-language models

Model Training

Pre-training, distributed training, optimization

Research Papers

Keep up with latest papers and implementations

Custom Architectures

Building specialized model architectures