Welcome to the LLM Engineering Lab – a comprehensive, structured repository designed to take you from LLM fundamentals to production-ready applications. Whether you're just starting with Large Language Models or looking to build sophisticated AI systems, this lab provides hands-on learning paths, practical implementations, and real-world projects to master the entire LLM engineering lifecycle.
It is important to understand the meaning and purpose of each section:
- 01-LLM-Fundamentals: Core LLM concepts, architectures, training strategies (fine-tuning, RLHF), and evaluation metrics. Understanding transformer models, pretraining objectives, and instruction tuning.
- 02-NLP-NLU-NLG-Comprehension: Foundations of natural language processing (tokenization, embeddings), understanding (intent extraction, entity recognition), and generation (coherent text output, decoding strategies).
- 03-Prompt-Engineering:: Advanced prompting strategies including zero-shot/one-shot/few-shot learning, chain-of-thought reasoning, prompt templates, optimization techniques, and comparison of prompt tuning vs prefix tuning methods.
- 04-RAG Pipeline:: Implementation of Retrieval-Augmented Generation techniques for context: Ingestion, Retrieval, Generation, Continuous Improvement.
- 05-Context-Management:: LLM context management, often referred to as context engineering, is the practice of strategically curating, organizing, and optimizing the information (context) provided to a Large Language Model within its limited "context window" to ensure relevant, accurate, and cost-effective responses.
- 06-Model-Context-Protocol:: Deep dive into Anthropic's Model Context Protocol (MCP) - the universal standard for connecting AI applications to data sources and tools. Covers MCP architecture, building custom servers (Python/TypeScript), security practices, and integration with orchestration frameworks.
- 07-LLM-Orchestration:: Tools and frameworks for orchestrating complex LLM workflows, including Model Context Protocol (MCP), Semantic Kernel, LangChain, LangGraph, LangSmith, LangFlow, and LangFuse for debugging and observability.
- 08-Agentic-AI-Systems:: Building autonomous AI agents with reasoning capabilities, tool integration (APIs, search, functions), planning strategies, and multi-agent collaboration patterns.
- 09-Evaluation-and-Benchmarks:: Comprehensive evaluation strategies including prompt testing, performance metrics (BLEU, ROUGE, perplexity), hallucination detection, latency/cost tracking, and distributed tracing with LangSmith.
- 10-MLOps-and-Production:: Productionizing LLM systems with MLOps best practices including experiment tracking with MLflow, model versioning and registry, continuous integration/deployment (CI/CD) pipelines, monitoring and logging in production, performance optimization, cost management, and integration with orchestration frameworks for robust, scalable deployments.
- 11-LLM-Data-Engineering:: Dataset lifecycle management including collection strategies, data cleaning and filtering techniques, formatting for model training, and synthetic data generation.
- 12-AI-IVR-Specifics:: Applying LLMs to Interactive Voice Response (IVR) systems, including speech-to-text/text-to-speech integration, dialogue management, and orchestration framework comparisons for voice applications.
- projects/: Practical and applied hands-on projects.
- notebooks/: Jupyter notebooks for experiments and demonstrations.
- scripts/: Utility scripts and helper functions.
By the End of This Lab, You Will Be Able To:
- Foundation & Core Concepts
- Understand the architecture and mechanics of Large Language Models, from transformers to attention mechanisms
- Build a miniature GPT model from scratch to deeply understand the underlying principles
- Master key terminology and concepts that form the language of modern LLM engineering
- Natural Language Processing
- Apply foundational NLP, NLU, and NLG techniques to process, understand, and generate human language
- Implement tokenization, embeddings, intent extraction, and entity recognition pipelines
- Evaluate different model architectures and select appropriate fine-tuning strategies (supervised, RLHF, instruction tuning)
- Retrieval-Augmented Generation (RAG)
- Implement end-to-end RAG pipelines from document ingestion to context-aware generation
- Work with vector databases including FAISS and Weaviate for efficient semantic search
- Evaluate and improve retrieval quality through continuous iteration and testing
- Context Management
- Optimize context windows to maximize information density within token limits
- Track conversation state and history for coherent multi-turn dialogues
- Implement memory systems for long-term information retention across sessions
- Structure outputs in specific formats (JSON, XML, function calls) for downstream processing
- Model Context Protocol (MCP)
- Understand MCP as the universal standard for connecting AI applications to external tools and data
- Build custom MCP servers in Python and TypeScript for your specific use cases
- Integrate MCP with orchestration frameworks to create flexible, modular AI systems
- Orchestration & Workflows
- Orchestrate complex LLM workflows using LangChain, LangGraph, and Semantic Kernel
- Debug and trace applications with LangSmith for complete observability
- Visualize workflows with LangFlow and monitor production systems with LangFuse
- Agentic Systems
- Build autonomous AI agents with reasoning, planning, and tool-use capabilities
- Integrate external APIs, search engines, and custom functions as agent tools
- Design multi-agent systems with collaboration patterns and shared objectives
- Evaluation & Quality Assurance
- Monitor model performance using industry-standard metrics (BLEU, ROUGE, perplexity)
- Detect hallucinations and implement quality gates in your pipelines
- Track latency, cost, and resource utilization for production optimization
- Trace distributed systems with comprehensive logging and debugging tools
- Production & MLOps
- Deploy LLM systems to production with CI/CD pipelines and automated testing
- Track experiments and manage model versions using MLflow
- Monitor production systems with logging, alerts, and performance dashboards
- Optimize costs and performance for scalable, enterprise-grade deployments
- Data Engineering
- Collect and curate high-quality datasets for training and fine-tuning
- Clean and filter data to ensure consistency and relevance
- Generate synthetic data for scenarios where real data is scarce or sensitive
- Dataset lifecycle management
- Collect and curate high-quality datasets from diverse sources for training and fine-tuning
- Clean and filter data to ensure consistency, remove duplicates, and maintain quality standards
- Format datasets according to specific model requirements and training objectives
- Generate synthetic data to augment training sets and address data scarcity challenges
- Domain Applications
- Apply LLM techniques to specialized domains like Interactive Voice Response (IVR) systems
- Integrates speech-to-text and text-to-speech for voice-enabled applications
- Manage complex dialogues in real-time conversational systems
This is a recommended progressive learning path::
START HERE
↓
[01] LLM Fundamentals ← Must understand basics first
↓
[02] NLP/NLU/NLG ← Language processing concepts
↓
[03] Prompt Engineering ← How to talk to LLMs
↓
[04] RAG Pipeline ← Connecting LLMs to knowledge
↓
[05] Context Management ← Managing conversations
↓
[06] MCP ← Universal tool/data layer
↓
[07] Orchestration ← Using MCP with frameworks
↓
[08] Agentic Systems ← Building autonomous agents
↓
[09] Evaluation ← Testing everything
↓
[10] MLOps ← Production deployment
↓
[11] Data Engineering ← Training data pipelines
↓
[12] IVR Specifics ← Domain application
The repository is organized into numbered folders to reflect a progressive learning path:
llm-engineering-lab/
│
├── README.md # Introduction, objectives, repo overview, usage instructions
│
├── 01-LLM-Fundamentals/ # LLM Path
│ ├── README.md # Overview LLM
│ ├── 01-1-What-is-an-LLM.md # Intro: simple explanation of LLMs and their importance
│ ├── 01-2-Key-Terms.md # Glossary of essential terms (token, prompt, inference, etc.)
│ ├── 01-3-Environment-Setup.md # Setting up Python env, APIs, dependencies
│ ├── 01-4-LLM-Architectures.md # Transformer-based models like GPT, LLaMA
│ ├── 01-5-Pretraining-and-Objectives.md # Pretraining tasks and objectives
│ ├── 01-6-Fine-tuning.md # Strategies for fine-tuning LLMs
│ ├── 01-7-Instruction-Tuning.md # Fine-tuning for following instructions
│ ├── 01-8-RLHF.md # Reinforcement learning with human feedback
│ └── 01-9-LLM-Evaluation.md # Metrics and best practices for evaluation
│
├── 02-NLP-NLU-NLG-Comprehension/ # NLP-NLU-NLG Path
│ ├── README.md # NLP-NLU-NLG Overview
│ ├── 02-1_NLP_Processing_Language/ # Text preparation and transformation into machine-readable form
│ ├── 02-2_NLU_Understanding_Meaning/ # Extracting intent, entities, and semantic relationships
│ ├── 02-3_NLG_Generating_Text/ # Producing coherent natural language output
│ └── 02-4_Evaluation_Refining_the_System/ # Measuring, optimizing, and improving model performance
│
├── 03-Prompt-Engineering/ # Crafting effective prompts
│ ├── README.md # Prompt Engineering Overview
│ ├── 03-1-Zero-Shot-One-Shot-Few-Shot.md # Different prompting strategies
│ ├── 03-2-Chain-of-Thought.md # Step-by-step reasoning
│ ├── 03-3-Prompt-Templates.md # Reusable patterns
│ ├── 03-4-Prompt-Optimization.md # Testing & iteration
│ └── 03-5-Prompt-Tuning-vs-Prefix-Tuning.md # Lightweight tuning methods
│
├── 04-RAG-Pipeline/ # Retrieval-Augmented Generation
│ ├── README.md # RAG Overview
│ ├── 04-1-Document-Ingestion.md # Loading, preprocessing, chunking documents
│ ├── 04-2-Retrieval-Strategies.md # Semantic search, vector databases
│ ├── 04-3-Generation-and-Synthesis.md # Combining retrieval with generation
│ └── 04-4-RAG-Evaluation.md # Continuous improvement and metrics
│
├── 05-Context-Management/ # Managing LLM context
│ ├── README.md # Context Management Overview
│ ├── 05-1-Context-Window-Management.md # Token limits, truncation strategies
│ ├── 05-2-State-and-History.md # Conversation state tracking
│ ├── 05-3-Memory-Systems.md # Long-term memory patterns
│ └── 05-4-Structured-Outputs.md # JSON, XML, function call formatting
│
├── 06-Model-Context-Protocol/ # Universal AI integration standard
│ ├── README.md # MCP Overview
│ ├── 06-1-MCP-Introduction.md # What is MCP, why it matters
│ ├── 06-2-MCP-Architecture.md # Hosts, clients, servers, transports
│ ├── 06-3-Using-MCP-Servers.md # Installing & configuring existing servers
│ ├── 06-4-Building-MCP-Servers-Python.md # Complete Python tutorial
│ ├── 06-5-Building-MCP-Servers-TypeScript.md # TypeScript/Node.js version
│ ├── 06-6-MCP-Security-Best-Practices.md # Security, sandboxing, validation
│ └── 06-7-MCP-Advanced-Patterns.md # Code execution, optimization
│
├── 07-LLM-Orchestration/ # Orchestrating LLM workflows
│ ├── README.md # LLM Orchestration Overview
│ ├── 07-1-MCP-in-Orchestration.md # How MCP fits in
│ ├── 07-2-Semantic-Kernel.md # Microsoft's Semantic Kernel
│ ├── 07-3-LangChain-Concepts.md # Chains, agents, retrievers, memory
│ ├── 07-4-LangGraph-Workflows.md # Stateful workflows with LangGraph
│ ├── 07-5-LangSmith-Debugging.md # Debugging, tracing, evaluation
│ ├── 07-6-LangFlow-Visual-Builder.md # Visual builder for LangChain workflows
│ ├── 07-7-LangFuse-Observability.md # Monitoring & analytics in production
│ └── 07-8-Orchestration-Comparison.md # When to use each tool
│
├── 08-Agentic-AI-Systems/ # Autonomous AI agents
│ ├── README.md # Agentic AI Systems Overview
│ ├── 08-1-What-Are-AI-Agents.md # Clear definition and concepts
│ ├── 08-2-Agent-Architectures.md # ReAct, Plan-Execute, etc.
│ ├── 08-3-Tool-Integration.md # APIs, search, databases, functions
│ ├── 08-4-MCP-Tools-in-Agents.md # MCP as tool layer
│ ├── 08-5-Agent-Memory-and-Planning.md # Long-term memory and reasoning
│ └── 08-6-Multi-Agent-Systems.md # Agent collaboration patterns
│
├── 09-Evaluation-and-Benchmarks/ # Testing and evaluating LLMs
│ ├── README.md # Evaluation Overview
│ ├── 09-1-Evaluation-Overview.md # Testing philosophy and strategies
│ ├── 09-2-Prompt-Evaluation.md # Assessing prompt quality
│ ├── 09-3-Metrics-and-Benchmarks.md # Accuracy, BLEU, ROUGE, perplexity
│ ├── 09-4-Hallucination-Detection.md # Detecting and preventing hallucinations
│ ├── 09-5-Latency-and-Cost-Tracking.md # Runtime, token usage, cost tracking
│ └── 09-6-LangSmith-Tracing.md # Distributed tracing with LangSmith
│
├── 10-MLOps-and-Production/ # Productionizing LLM systems
│ ├── README.md # MLOps and Production Overview
│ ├── 10-1-Production-Readiness.md # Deployment considerations
│ ├── 10-2-MLflow-Basics.md # Logging and tracking experiments
│ ├── 10-3-Experiment-Tracking.md # MLflow for prompt + retrieval stats
│ ├── 10-4-LangChain-MLflow-Integration.md # MLflow integration with LangChain
│ ├── 10-5-Monitoring-and-Logging.md # Production monitoring and alerts
│ └── 10-6-CI-CD-for-LLM-Systems.md # Deployment pipelines
│
├── 11-LLM-Data-Engineering/ # Dataset lifecycle for training
│ ├── README.md # LLM Data Engineering Overview
│ ├── 11-1-Data-Engineering-Overview.md # Data lifecycle and principles
│ ├── 11-2-Dataset-Collection.md # Gathering high-quality training data
│ ├── 11-3-Data-Cleaning-and-Filtering.md # Cleaning and filtering for consistency
│ ├── 11-4-Dataset-Formatting.md # Formatting datasets for model training
│ └── 11-5-Synthetic-Data-Generation.md # Creating training data
│
├── 12-AI-IVR-Specifics/ # LLMs in IVR systems
│ ├── README.md # AI-IVR-Specifics Overview
│ ├── 12-1-IVR-System-Overview.md # Interactive Voice Response systems
│ ├── 12-2-Speech-to-Text-and-TTS.md # Speech-to-text and TTS integration
│ ├── 12-3-Dialogue-Management.md # Managing conversations with LLMs
│ ├── 12-4-MCP-in-IVR-Systems.md # MCP for telephony integration
│ └── 12-5-Orchestration-for-IVR.md # LangChain vs Semantic Kernel in IVR
│
├── projects/ # Hands-on projects
│ ├── 01-basic-rag/ # Simple RAG implementation
│ ├── 02-weaviate-rag/ # RAG with Weaviate vector DB
│ ├── 03-weather-mcp-server/ # Custom MCP server example
│ ├── 04-langchain-agent/ # Agent with LangChain
│ ├── 05-semantic-kernel-bot/ # Bot with Semantic Kernel
│ └── 06-multi-agent-system/ # Multi-agent collaboration
│
├── notebooks/ # Jupyter notebooks for experiments
│ ├── 01-LLM-Fundamentals/
│ │ ├── 01-mini-gpt-char.ipynb # Minimal character-level GPT
│ │ ├── 02-genesis-transformer.ipynb # More advanced Transformer LM
│ │ ├── README.md # Explains both steps
│ │ └── requirements.txt
│ ├── 02-NLP-NLU-NLG/ # NLP (NLU and NLG) examples
│ ├── 03-Prompt-Engineering/ # Prompt engineering experiments
│ ├── 04-RAG-Pipeline/ # RAG examples
│ │ └── rag_from_scratch_1_to_4/
│ ├── 05-Context-Management/ # Context management examples
│ ├── 06-MCP/ # MCP experiments
│ │ ├── 01-mcp-basics.ipynb
│ │ ├── 02-build-simple-server.ipynb
│ │ └── 03-mcp-with-langchain.ipynb
│ ├── 07-LLM-Orchestration/ # LLM Orchestration examples
│ └── 08-Agentic-Systems/ # Agent building examples
│
└── scripts/ # Utility scripts for loaders, embeddings, etc.
├── utils.py # General utility functions
├── loaders.py # Document loaders
├── embeddings.py # Embedding generators
└── mcp_utils.py # MCP helper functions
