🚀 LLM Specialist Roadmap

1
Junior LLM Practitioner
2-3 months | Goal: First Job

🔧 Tools

💡 Skills

📋 Projects:

API-based Chatbot
FAQ System
Document RAG
Telegram Bot
2
Middle LLM Engineer + Foundation
6-8 months | Goal: Deep Understanding

📊 Theory

🛠️ Practice

📋 Projects:

Model Fine-tuning
Multi-agent System
Prompt A/B Testing
Enterprise Integration
3
Senior LLM Engineer
8-12 months | Goal: Architectural Thinking

🏗️ Architecture

🎯 Alignment

📋 Projects:

Product Architecture Design
Custom Model Architectures
Performance Optimization Initiatives
Team Leadership & Mentoring
4
LLM Expert/Researcher
12+ months | Goal: Innovation and Research

🔬 Research

🌟 Leadership

📋 Projects:

Groundbreaking Scientific Publications
Leading Open Source Projects
Developing New Methodologies
Strategic AI Roadmapping
Practitioner (Junior)
Engineer with Foundation (Middle)
Architect (Senior)
Researcher (Expert)

📚 Detailed Skill Exploration

🐍 Python Basics Junior

Language Fundamentals

  • Variables, data types, operators
  • Conditionals (if/elif/else), loops (for/while)
  • Functions: definition, parameters, return values
  • Lists, dictionaries, tuples, sets
  • File operations (open, read, write)

For LLM Work

  • `requests` module for HTTP
  • JSON: loading, parsing, creation
  • Exception handling (try/except)
  • String formatting (f-strings)
  • Environment variables (`os.environ`)

Development & Deployment

  • Virtual environments: venv, conda
  • pip: package installation, `requirements.txt`
  • Poetry for dependency management
  • `.env` files for configuration
  • Project structure: `src/`, `tests/`, `docs/`
🛠️ Git & Docker Junior

Git Fundamentals

  • `git init, clone, add, commit, push, pull`
  • Branches: creation, switching, merging
  • `.gitignore`: excluding files (`.env`, `__pycache__`)
  • GitHub/GitLab: repositories, issues, pull requests
  • Semantic commit messages: `feat:`, `fix:`, `docs:`

Docker for Python

  • `Dockerfile`: `FROM python:3.11, COPY, RUN, CMD`
  • `docker build, run, exec, logs`
  • `docker-compose.yml` for multi-container setup
  • Volumes for data: `-v ./data:/app/data`
  • Environment variables in containers

Local Development

  • VS Code: extensions, debugging, terminals
  • Python virtual environments: `python -m venv`
  • Hot reload: `uvicorn --reload`, `streamlit run`
  • Environment management: development vs production
  • Local testing: pytest, unit tests
📁 Project Structure Junior

Code Organization

  • `src/` - main application code
  • `tests/` - unit and integration tests
  • `docs/` - project documentation
  • `config/` - configuration files
  • `data/` - datasets, data samples

Configuration Files

  • `requirements.txt` or `pyproject.toml`
  • `.env.example` - environment variable template
  • `Dockerfile` and `docker-compose.yml`
  • `README.md` with setup instructions
  • `.github/workflows/` for CI/CD

Best Practices

  • Modular architecture: separation of concerns
  • Config management: Pydantic settings
  • Logging: structlog, JSON logs
  • Error handling: custom exceptions
  • Code quality: black, flake8, mypy
🤖 OpenAI/Claude API Junior

OpenAI API

  • Registration, obtaining API key
  • `openai` library: installation, basic usage
  • ChatCompletion API: messages, roles, parameters
  • Parameters: `temperature`, `max_tokens`, `top_p`
  • Streaming responses for real-time

Anthropic Claude API

  • `anthropic` library
  • Messages API, system prompts
  • Tool use (function calling)
  • Model comparison: Sonnet, Opus, Haiku
  • Rate limiting and error handling
🌐 Streamlit/Gradio Junior

Streamlit

  • `st.write()`, `st.text_input()`, `st.button()`
  • `st.chat_message()`, `st.chat_input()` for chats
  • `st.session_state` for state management
  • `st.sidebar`, `st.columns` for layout
  • Deployment on Streamlit Cloud

Gradio

  • `gr.Interface()`: inputs, outputs, fn
  • `gr.ChatInterface()` for chatbots
  • Various input types: textbox, file, audio
  • Custom CSS and themes
  • Hugging Face Spaces deployment
🔗 LangChain Junior

Core Components

  • LLMs and ChatModels: OpenAI, Anthropic
  • PromptTemplates for structured prompts
  • Chains: LLMChain, SimpleSequentialChain
  • OutputParsers for response processing
  • Memory for conversation history

RAG Components

  • Document loaders: TextLoader, PDFLoader
  • Text splitters: RecursiveCharacterTextSplitter
  • Vector stores: Chroma, FAISS
  • Embeddings: OpenAIEmbeddings
  • RetrievalQA chain
💡 Prompt Engineering Junior

Basic Techniques

  • Clear instructions: "Act as...", "Your task is..."
  • Few-shot examples: demonstrate desired format
  • Chain-of-Thought: "Let's think step by step"
  • Role prompting: system roles
  • Output formatting: JSON, XML, structured data

Advanced Methods

  • Tree of Thoughts for complex tasks
  • Self-consistency: multiple attempts
  • Constitutional prompting for safety
  • Meta-prompting: prompts about prompts
  • A/B testing prompts
🔍 RAG Systems Junior

RAG Fundamentals

  • Concept: Retrieval + Augmented + Generation
  • Text vector representations (embeddings)
  • Similarity search: cosine similarity
  • Chunking strategies: size, overlap
  • Context window and its limitations

Practical Implementation

  • ChromaDB: creating collections, adding documents
  • Sentence-transformers for embeddings
  • Query expansion and reranking
  • Hybrid search: keyword + vector
  • Evaluation: precision, recall for RAG
🌐 Web Interfaces Junior

Web Development Fundamentals

  • HTML: structure, tags, forms
  • CSS: styles, flexbox, grid
  • JavaScript: DOM manipulation, fetch API
  • HTTP: GET, POST, status codes
  • JSON: structure, parsing

Python Web Frameworks

  • Flask: routes, templates, request handling
  • FastAPI: async endpoints, automatic docs
  • Template engines: Jinja2
  • Static files: CSS, JS, images
  • CORS for frontend integration
🔌 API Integrations Junior

REST API

  • HTTP methods: GET, POST, PUT, DELETE
  • Headers: Authorization, Content-Type
  • Request/Response formats: JSON, form-data
  • Status codes: 200, 400, 401, 500
  • Rate limiting and retry logic

Popular Integrations

  • Telegram Bot API: webhooks, commands
  • Discord API: bots, slash commands
  • Slack API: apps, bot tokens
  • Google APIs: Drive, Sheets, Gmail
  • Webhook endpoints for notifications
📊 Linear Algebra Middle

Core Concepts

  • Vectors: addition, dot product
  • Matrices: multiplication, transpose
  • Eigenvectors and eigenvalues
  • Matrix factorization: SVD, PCA
  • Vector norms: L1, L2, cosine distance

Application in ML

  • Word embeddings as vectors
  • Similarity metrics for text
  • Dimensionality reduction
  • Gradient descent mathematics
  • Attention weights as matrices
🧠 Neural Networks Middle

Architecture

  • Perceptron: weights, bias, activation
  • Multilayer networks: hidden layers
  • Activation functions: ReLU, sigmoid, tanh
  • Forward pass: computing predictions
  • Backpropagation: updating weights

Optimization

  • Loss functions: MSE, cross-entropy
  • Optimizers: SGD, Adam, AdamW
  • Learning rate scheduling
  • Regularization: dropout, weight decay
  • Batch normalization
🔄 Transformer Architecture Middle

Core Components

  • Multi-head attention mechanism
  • Positional encoding for sequences
  • Feed-forward networks
  • Layer normalization and residual connections
  • Encoder-decoder vs decoder-only

Modern Architectures

  • BERT: bidirectional encoder
  • GPT: autoregressive decoder
  • T5: text-to-text transfer
  • Switch Transformer: sparse experts
  • Mixture of Experts (MoE) architectures
🎯 Attention Mechanism Middle

Basic Attention

  • Query, Key, Value matrices
  • Scaled dot-product attention
  • Attention scores and softmax
  • Context vectors
  • Visualizing attention weights

Multi-head Attention

  • Parallel attention heads
  • Different representation subspaces
  • Concatenation and linear projection
  • Self-attention vs cross-attention
  • Causal masking for decoders
🎨 Fine-tuning Middle

Fine-tuning Approaches

  • Full fine-tuning: updating all parameters
  • LoRA: Low-Rank Adaptation
  • QLoRA: Quantized LoRA
  • Adapter layers: additional modules
  • Prompt tuning: soft prompts

Practical Implementation

  • Dataset preparation: tokenization, formatting
  • Training loops: epochs, batches
  • Hyperparameters: learning rate, batch size
  • Gradient accumulation for large models
  • Evaluation during training
📊 Evaluation Metrics Middle

Automatic Metrics

  • BLEU: n-gram overlap for generation
  • ROUGE: recall for summarization
  • METEOR: semantic similarity
  • BERTScore: contextual embeddings
  • Perplexity: language model quality

Human Evaluation

  • Relevance, coherence, fluency
  • Helpfulness and harmlessness
  • Factual accuracy
  • Inter-annotator agreement
  • A/B testing with users
🔥 PyTorch/TensorFlow Middle

PyTorch Fundamentals

  • Tensors: creation, operations, device placement
  • Autograd: automatic differentiation
  • `nn.Module`: creating custom layers
  • Optimizers: `torch.optim`
  • DataLoaders: batch processing

For Transformers

  • `torch.nn.MultiheadAttention`
  • Positional embeddings
  • Layer normalization
  • Mixed precision training
  • Model checkpointing
🤗 Hugging Face Middle

Transformers Library

  • AutoModel, AutoTokenizer
  • Pipeline API: text-generation, classification
  • `Model.from_pretrained()`: loading models
  • `Tokenizer.encode(), decode()`
  • Trainer API for fine-tuning

Hub and Ecosystem

  • Model Hub: search, download models
  • Datasets library: `load_dataset()`
  • Spaces: deploy applications
  • Hub API: programmatic access
  • Model cards: model documentation
📈 Scaling Laws Senior

Theoretical Foundations

  • Kaplan et al. scaling laws
  • Compute-optimal training (Chinchilla)
  • Parameter count vs performance
  • Data scaling vs model scaling
  • Emergent abilities thresholds

Practical Applications

  • Resource planning for training
  • Trade-offs: quality vs speed
  • Optimal dataset sizes
  • Predicting performance before training
  • ROI analysis for large models
🌐 Distributed Training Senior

Parallelism

  • Data parallelism: DDP, FSDP
  • Model parallelism: tensor, pipeline
  • 3D parallelism: data + model + pipeline
  • Gradient accumulation strategies
  • Communication optimizations

Infrastructure

  • Multi-GPU setup: NCCL, CUDA
  • Kubernetes for ML workloads
  • Ray, Horovod for distributed computing
  • Storage: distributed filesystems
  • Monitoring: TensorBoard, Weights & Biases
⚡ Model Optimization Senior

Compression Techniques

  • Quantization: INT8, INT4, dynamic
  • Pruning: structured, unstructured
  • Knowledge distillation
  • Low-rank approximations
  • Sparse attention patterns

Inference Optimization

  • ONNX for cross-platform inference
  • TensorRT, TorchScript
  • Batching strategies
  • KV-cache optimization
  • Speculative decoding
🏗️ Infrastructure Design Senior

Cloud Architecture

  • AWS/GCP/Azure ML services
  • Serverless inference: Lambda, Cloud Functions
  • Load balancing for ML endpoints
  • Auto-scaling policies
  • Cost optimization strategies

MLOps Pipeline

  • CI/CD for ML: GitHub Actions, GitLab CI
  • Model registry: MLflow, Weights & Biases
  • Monitoring: performance, drift detection
  • A/B testing infrastructure
  • Rollback strategies
🎯 RLHF (Reinforcement Learning from Human Feedback) Senior

Theoretical Foundations

  • Reward modeling: human preferences
  • PPO (Proximal Policy Optimization)
  • Value functions and critic networks
  • KL-divergence regularization
  • Exploration vs exploitation

Practical Implementation

  • Human preference datasets
  • Reward model training
  • Policy optimization loops
  • Evaluation metrics for RLHF
  • Scaling human feedback
📜 Constitutional AI Senior

Principles

  • AI Constitution: set of principles
  • Self-supervision for alignment
  • Critiquing and revising responses
  • Iterative refinement
  • Reducing harmfulness

Implementation

  • Constitutional principles design
  • Red team testing
  • Automated safety evaluation
  • Robustness to adversarial prompts
  • Transparency and interpretability
🔒 Safety Techniques Senior

Alignment Methods

  • Value alignment: AI goals = human goals
  • Interpretability: understanding AI decisions
  • Robustness: resilience to errors
  • Corrigibility: ability to be corrected
  • Containment: limiting capabilities

Practical Techniques

  • Content filtering systems
  • Adversarial testing
  • Gradual capability disclosure
  • Human oversight loops
  • Fail-safe mechanisms
⚖️ Bias Mitigation Senior

Types of Bias

  • Training data bias
  • Representation bias
  • Evaluation bias
  • Confirmation bias
  • Demographic biases

Mitigation Methods

  • Bias detection in datasets
  • Debiasing techniques
  • Fairness metrics
  • Diverse evaluation sets
  • Inclusive design principles
🔬 Novel Architectures Expert

New Approaches

  • State Space Models (Mamba, S4)
  • Advanced Retrieval-Augmented Architectures
  • Mixture of Experts (MoE) Scaling & Optimization
  • Recursive & Self-Modifying Models
  • Neuro-Symbolic Integration

Research Directions

  • In-context learning mechanisms & theory
  • Long-context understanding & generation
  • Efficient & scalable training/inference
  • Reasoning & Planning in LLMs
  • World Models & Simulation with LLMs
✨ Emergent Abilities Expert

Understanding Emergence

  • Defining and detecting emergent abilities
  • Phase transitions in model capabilities
  • Relationship with scale (data, params, compute)
  • Predicting future emergent abilities
  • Unintended capabilities and risks

Harnessing & Guiding Emergence

  • Techniques to elicit specific abilities
  • Controlling and aligning emergent behaviors
  • Evaluating complex, multi-step reasoning
  • Ethical implications of powerful emergent skills
  • Theories of why emergence occurs
🖼️ Multimodal Systems Expert

Core Concepts

  • Fusing different data modalities (text, image, audio, video)
  • Cross-modal attention mechanisms
  • Joint embedding spaces
  • Generative multimodal models (e.g., text-to-image, image-to-text)
  • Multimodal grounding and reasoning

Advanced Research

  • Scaling multimodal models
  • Zero-shot and few-shot multimodal learning
  • Multimodal instruction following
  • Applications in robotics, HCI, creative AI
  • Evaluation of multimodal understanding and generation
🔗 AGI Alignment Expert

Fundamental Problems

  • Defining and specifying human values
  • Outer vs. Inner alignment
  • Scalable oversight and reward misspecification
  • Corrigibility and avoiding power-seeking behavior
  • Long-term safety of superintelligent systems

Research Approaches

  • Interpretability for highly capable models
  • Formal verification of AI safety properties
  • Debate, amplification, and iterated distillation
  • AI safety via debate or recursive reward modeling
  • Cooperative AI and multi-agent safety
🔍 XAI & Interpretability Expert

Core Techniques

  • Feature attribution methods (SHAP, LIME, Integrated Gradients)
  • Concept-based explanations
  • Mechanistic interpretability: circuits in transformers
  • Probing and diagnostic classifiers
  • Generating natural language explanations

Advanced Research & Application

  • Developing inherently interpretable models
  • Auditing models for bias and fairness using XAI
  • Improving model robustness and debugging
  • Building trust and understanding in AI systems
  • Evaluating the faithfulness and usefulness of explanations
🧭 Setting Research Direction Expert

Strategic Vision

  • Identifying impactful research questions
  • Forecasting technological trends and breakthroughs
  • Balancing foundational research with applied innovation
  • Developing long-term research roadmaps
  • Assessing societal impact and ethical considerations

Execution & Leadership

  • Securing funding and resources
  • Building and mentoring research teams
  • Fostering a collaborative and innovative research culture
  • Managing complex, multi-year research projects
  • Communicating research vision to stakeholders
📜 High-Impact Publications Expert

Crafting Quality Papers

  • Novelty and significance of contributions
  • Rigorous methodology and experimentation
  • Clear, concise, and compelling writing
  • Reproducibility and open-sourcing code/data
  • Addressing reviewer feedback constructively

Dissemination & Impact

  • Targeting top-tier conferences (NeurIPS, ICML, ICLR, ACL, CVPR)
  • Journal publications for archival work
  • Presenting research effectively (talks, posters)
  • Building citations and academic influence
  • Translating research into real-world applications
🌍 Open Source Leadership Expert

Project Initiation & Management

  • Identifying needs for new open-source tools/models
  • Designing scalable and maintainable architectures
  • Establishing contribution guidelines and code of conduct
  • Managing community contributions and pull requests
  • Roadmapping project features and releases

Community Building & Advocacy

  • Fostering an inclusive and active community
  • Creating high-quality documentation and tutorials
  • Promoting the project through talks and articles
  • Collaborating with other open-source projects
  • Ensuring long-term sustainability of the project
🏛️ Defining Industry Standards Expert

Technical Standards

  • Benchmarking and evaluation protocols for LLMs
  • Standardized data formats and APIs
  • Best practices for responsible AI development
  • Interoperability between LLM systems
  • Security standards for LLM deployment

Policy & Governance

  • Contributing to ethical guidelines and frameworks
  • Participating in standards bodies and consortia
  • Advising on regulatory approaches for AI
  • Promoting transparency and accountability
  • Shaping public discourse on AI's societal impact
⚖️ Advanced AI Ethics Expert

Deep Ethical Considerations

  • Fairness, accountability, and transparency (FAT/FAccT) in complex systems
  • Long-term societal impacts of LLMs (e.g., job displacement, misinformation)
  • Philosophical underpinnings of AI value alignment
  • Dual-use concerns and misuse potential of advanced AI
  • Ethical frameworks for AGI development and deployment

Practical Implementation & Research

  • Developing and implementing robust ethics review processes
  • Researching novel techniques for bias detection and mitigation at scale
  • Designing AI systems for contestability and redress
  • Cross-cultural perspectives on AI ethics
  • Public engagement and education on AI ethics