Welcome to the Language AI Engineering Lab — a comprehensive, structured repository designed to guide you from human language fundamentals and NLP through Transformers, Large Language Models, and into production-ready Language AI systems.
Whether you are starting from the basics or aiming to build scalable, real-world AI applications, this lab offers hands-on learning paths, practical implementations, and end-to-end projects that cover the entire Language AI engineering lifecycle — from text processing and model architectures to retrieval, agents, orchestration, evaluation, and deployment.
It is important to understand the meaning and purpose of each section:
Foundations of NLP, NLU, and NLG: tokenization, embeddings, intent extraction, entity recognition, and text generation.
Deep dive into transformers: attention, embeddings, positional encoding, feedforward layers, and why transformers work.
Core LLM concepts, architectures, training strategies (fine-tuning, RLHF), and evaluation foundations.
Zero-shot, one-shot, few-shot prompting, reasoning patterns, prompt templates, and optimization techniques.
Managing LLM context windows, conversation state, memory, truncation strategies, and structured outputs.
End-to-end Retrieval-Augmented Generation pipelines: indexing, chunking, embedding, retrieval, reranking, grounding, and response synthesis.
Designing context as a system: instruction hierarchies, memory fusion, grounding strategies, safety constraints, and cost-aware assembly.
Metrics, prompt testing, regression testing, hallucination measurement, latency, cost tracking, and tracing.
Failure modes, hallucination taxonomy, detection strategies, grounding techniques, and mitigation patterns.
Standardized tool and data access via MCP, custom servers, and secure integrations.
Workflow orchestration with LangChain, LangGraph, Semantic Kernel, LangFlow, LangSmith, and LangFuse.
Autonomous agents, planning, reasoning loops, tool use, and multi-agent collaboration.
Vision-language models, audio-text models, multimodal fusion, and cross-modal reasoning.
CI/CD, deployment, monitoring, observability, scaling, and cost optimization.
Dataset lifecycle, cleaning, versioning, labeling, and synthetic data generation.
Speech-to-text, text-to-speech, dialogue management, and real-time IVR orchestration.
Practical and applied hands-on projects.
Jupyter notebooks for experiments and demonstrations.
Utility scripts and helper functions.
By the End of This Lab, You Will Be Able To:
- Apply foundational NLP techniques to process, understand, and generate human language
- Implement tokenization, normalization, embeddings, intent classification, and entity recognition pipelines
- Differentiate between NLP, NLU, and NLG tasks and understand where each fits in modern LLM systems
- Understand transformer internals including self-attention, multi-head attention, and feed-forward layers
- Explain positional encoding, embeddings, and context length constraints
- Build a mini GPT-style language model from scratch to solidify architectural understanding
- Master essential LLM terminology and architectural trade-offs
- Understand pretraining objectives such as causal language modeling and masked language modeling
- Apply fine-tuning strategies including supervised fine-tuning, instruction tuning, and RLHF
- Evaluate how training choices affect model behavior, bias, and generalization
- Design effective zero-shot, one-shot, and few-shot prompts
- Apply reasoning-oriented prompting techniques such as chain-of-thought and decomposition
- Iterate and optimize prompts using templates, constraints, and systematic testing
- Optimize context windows to maximize information density within token limits
- Track conversation state and history for coherent multi-turn interactions
- Implement short-term and long-term memory patterns
- Structure model outputs using schemas such as JSON, XML, and function-call formats
- Understand the full RAG pipeline from ingestion to retrieval and generation
- Design chunking, embedding, indexing, and retrieval strategies
- Ground model responses in external knowledge to improve factuality and reliability
- Evaluate retrieval quality and generation faithfulness
- Design context as a system rather than a single prompt
- Compose system prompts, developer instructions, retrieved documents, memory, and user input coherently
- Apply hierarchical instruction models (system > developer > user)
- Rank, filter, and constrain context to reduce noise and hallucinations
- Optimize token usage for cost, latency, and relevance
- Build robust, production-ready context assembly pipelines
- Measure model quality using metrics such as BLEU, ROUGE, and perplexity
- Detect and categorize hallucinations (factual, contextual, structural)
- Implement grounding, verification, and evidence-first strategies
- Track latency, cost, and quality regressions over time
- Understand MCP as a standard interface between LLMs, tools, and data sources
- Build custom MCP servers for controlled tool and data access
- Secure and validate model-tool interactions
- Integrate MCP into orchestration and agent systems
- Orchestrate complex LLM workflows using LangChain, LangGraph, and Semantic Kernel
- Design stateful, multi-step pipelines with branching and retries
- Debug, trace, and observe systems using LangSmith, LangFlow, and LangFuse
- Build autonomous agents capable of reasoning, planning, and tool usage
- Integrate APIs, databases, search engines, and custom tools
- Design single-agent and multi-agent collaboration patterns
- Manage agent memory, goals, and execution loops
- Understand how transformers extend beyond text to vision, audio, and video
- Work with multimodal inputs such as text+image or speech+text
- Design cross-modal reasoning and generation workflows
- Deploy LLM systems using CI/CD pipelines and automated testing
- Track experiments, prompts, and evaluations using MLflow
- Monitor production systems for latency, cost, drift, and failures
- Optimize performance and reliability at scale
- Collect and curate high-quality datasets for training and fine-tuning
- Clean, filter, and deduplicate data to maintain quality standards
- Format and version datasets for reproducible training
- Generate synthetic data to address data scarcity or privacy constraints
- Apply LLM techniques to Interactive Voice Response (IVR) systems
- Integrate speech-to-text (STT) and text-to-speech (TTS) components
- Manage real-time dialogue state and orchestration for voice-based applications
This is a recommended progressive learning path:
START
↓
01-Human-Language-and-NLP
↓
02-Transformer-Architecture
↓
03-LLM-Fundamentals
↓
04-Prompt-Engineering
↓
05-Context-Management
↓
06-RAG-Pipeline
↓
07-Context-Engineering
↓
08-Evaluation-and-Benchmarks
↓
09-Hallucinations-and-Factuality
↓
10-Model-Context-Protocol
↓
11-LLM-Orchestration
↓
12-Agentic-AI-Systems
↓
13-Multimodal-Models
↓
14-MLOps-and-Production
↓
15-LLM-Data-Engineering
↓
16-AI-IVR-Specifics
The repository is organized into numbered folders to reflect a progressive learning path:
language-ai-engineering-lab/
├── 01-Human-Language-and-NLP/
├── 02-Transformer-Architecture/
├── 03-LLM-Fundamentals/
├── 04-Prompt-Engineering/
├── 05-Context-Management/
├── 06-RAG-Pipeline/
├── 07-Context-Engineering/
├── 08-Evaluation-and-Benchmarks/
├── 09-Hallucinations-and-Factuality/
├── 10-Model-Context-Protocol/
├── 11-LLM-Orchestration/
├── 12-Agentic-AI-Systems/
├── 13-Multimodal-Models/
├── 14-MLOps-and-Production/
├── 15-LLM-Data-Engineering/
├── 16-AI-IVR-Specifics/
├── projects/
├── notebooks/
└── scripts/
