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🤖LLM EngineerIntermediate

LLM Engineer Path

Master production-ready LLM application development. From transformer fundamentals to deploying RAG systems, fine-tuning, and AI agents. Based on industry best practices from OpenAI, Anthropic, and leading AI labs.

16 weeks
12 milestones
0 items

Skills You Will Gain

Transformer ArchitecturePrompt EngineeringRAG SystemsFine-tuning (LoRA/QLoRA)AI AgentsLLM EvaluationProduction DeploymentVector DatabasesLangChain/LlamaIndex

Prerequisites

  • →Python programming (intermediate level)
  • →Basic understanding of APIs and web development
  • →Familiarity with machine learning concepts
  • →Linear algebra and probability basics

Learning Milestones

1

NLP & Transformer Foundations

Build a solid foundation in NLP concepts and the transformer architecture that powers all modern LLMs.

~20h•0 items

Learning Objectives

  • ✓Understand tokenization methods (BPE, WordPiece, SentencePiece)
  • ✓Master word embeddings and their evolution (Word2Vec → BERT → GPT)
  • ✓Explain self-attention and multi-head attention mechanisms
  • ✓Understand positional encoding and why transformers need it
  • ✓Compare encoder-only, decoder-only, and encoder-decoder architectures
  • ✓Trace data flow through a complete transformer block
Content coming soon
2

LLM Architectures Deep Dive

Understand the architecture and training of major LLM families: GPT, BERT, T5, LLaMA, and beyond.

~15h•0 items

Learning Objectives

  • ✓Compare GPT vs BERT vs T5 architectural differences
  • ✓Understand pre-training objectives (CLM, MLM, Span Corruption)
  • ✓Learn scaling laws and their implications for model design
  • ✓Explore open-source models: LLaMA, Mistral, Qwen, DeepSeek
  • ✓Understand model quantization (INT8, INT4, GPTQ, AWQ)
  • ✓Learn about mixture-of-experts (MoE) architectures
Content coming soon
3

Working with LLM APIs

Master the practical skills of working with LLM APIs from OpenAI, Anthropic, and open-source providers.

~12h•0 items

Learning Objectives

  • ✓Use OpenAI API effectively (chat completions, function calling)
  • ✓Work with Anthropic Claude API and understand its strengths
  • ✓Deploy and use open-source models via Hugging Face and Ollama
  • ✓Implement streaming responses for better UX
  • ✓Handle rate limits, retries, and error handling gracefully
  • ✓Optimize API costs through batching and caching
Content coming soon
4

Prompt Engineering Mastery

Master systematic prompt engineering techniques from basic to advanced.

~15h•0 items

Learning Objectives

  • ✓Apply zero-shot, few-shot, and many-shot prompting effectively
  • ✓Implement Chain-of-Thought (CoT) and Tree-of-Thought reasoning
  • ✓Design system prompts for consistent behavior
  • ✓Use structured outputs (JSON mode, function calling)
  • ✓Debug and iterate on prompt failures systematically
  • ✓Build prompt libraries and version control strategies
Content coming soon
5

Vector Databases & Embeddings

Master vector databases and embedding models - the foundation of RAG systems.

~18h•0 items

Learning Objectives

  • ✓Understand embedding models (OpenAI, Cohere, open-source alternatives)
  • ✓Compare vector database options (Pinecone, Weaviate, Chroma, Qdrant, pgvector)
  • ✓Implement efficient similarity search algorithms
  • ✓Design indexing strategies for large-scale data
  • ✓Handle multi-modal embeddings (text + images)
  • ✓Optimize embedding dimensions and quantization
Content coming soon
6

RAG Systems Architecture

Build production-ready Retrieval-Augmented Generation systems from scratch.

~25h•0 items

Learning Objectives

  • ✓Design document ingestion pipelines (PDF, HTML, Markdown)
  • ✓Implement effective chunking strategies (semantic, recursive, sentence)
  • ✓Build hybrid retrieval (vector + keyword BM25)
  • ✓Implement reranking for improved relevance
  • ✓Handle multi-hop reasoning and complex queries
  • ✓Build evaluation frameworks for RAG quality
Content coming soon
7

Advanced RAG Patterns

Master advanced RAG architectures: HyDE, CRAG, Agentic RAG, and GraphRAG.

~20h•0 items

Learning Objectives

  • ✓Implement HyDE (Hypothetical Document Embeddings)
  • ✓Build Corrective RAG (CRAG) with fallback mechanisms
  • ✓Design Adaptive RAG with query complexity routing
  • ✓Implement Agentic RAG with tool use
  • ✓Explore GraphRAG for knowledge graph integration
  • ✓Build multi-index RAG for heterogeneous data sources
Content coming soon
8

Fine-tuning LLMs

Learn parameter-efficient fine-tuning methods to customize LLMs for specific domains and tasks.

~20h•0 items

Learning Objectives

  • ✓Understand when to fine-tune vs when to use RAG
  • ✓Prepare and format training data (instruction tuning, chat format)
  • ✓Implement LoRA and QLoRA fine-tuning
  • ✓Use PEFT library for efficient fine-tuning
  • ✓Evaluate fine-tuned model performance
  • ✓Merge and deploy fine-tuned adapters
Content coming soon
9

AI Agents Fundamentals

Build autonomous AI agents that can reason, plan, and use tools to accomplish complex tasks.

~22h•0 items

Learning Objectives

  • ✓Understand ReAct pattern (Reasoning + Acting)
  • ✓Implement function calling and tool use
  • ✓Build agents with LangChain and LangGraph
  • ✓Design agent memory systems (short-term, long-term)
  • ✓Handle multi-turn conversations with context
  • ✓Implement agent guardrails and safety measures
Content coming soon
10

Multi-Agent Systems

Build sophisticated multi-agent systems for complex workflows.

~18h•0 items

Learning Objectives

  • ✓Design multi-agent architectures with CrewAI
  • ✓Implement agent collaboration patterns
  • ✓Build hierarchical agent systems
  • ✓Handle agent communication and coordination
  • ✓Implement human-in-the-loop workflows
  • ✓Debug and monitor multi-agent systems
Content coming soon
11

LLM Evaluation & Testing

Learn systematic approaches to evaluate and test LLM applications.

~15h•0 items

Learning Objectives

  • ✓Choose appropriate evaluation metrics for different tasks
  • ✓Build automated evaluation pipelines with LLM-as-judge
  • ✓Implement human evaluation workflows
  • ✓Create regression test suites for LLM apps
  • ✓Use evaluation frameworks (RAGAS, DeepEval)
  • ✓Build continuous evaluation in CI/CD
Content coming soon
12

Production Deployment

Deploy LLM applications at scale with reliability, observability, and cost efficiency.

~20h•0 items

Learning Objectives

  • ✓Optimize inference latency and throughput
  • ✓Implement caching strategies (semantic cache, exact match)
  • ✓Deploy with vLLM, TGI, or cloud providers
  • ✓Set up monitoring and observability (LangSmith, Langfuse)
  • ✓Manage costs and implement rate limiting
  • ✓Handle failover and graceful degradation
Content coming soon

Content Summary

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