LLM Engineering and Deployment: Architecting, Training & Scaling Generative AI Systems

Introduction to LLM Engineering

• Evolution of NLP to modern foundation models
• Enterprise adoption patterns of Generative AI
• Model benchmarking landscape
• Comparative analysis of major LLM families (GPT, Claude, Gemini, Llama)
• Understanding parameters, tokens, and scaling laws
• Context windows and memory constraints
• Cost vs capability trade-offs

Architecture discussion:
● Centralized vs distributed LLM services
● Build vs buy decisions in enterprises

Hands-on:
● Evaluate model responses across providers
● Analyze latency, token usage, and cost metrics

Transformer Deep Dive

• Transformer architecture fundamentals
  ● Self-attention and multi-head attention
  ● Positional encoding strategies
  ● Decoder-only vs encoder-decoder architectures
  ● Tokenization strategies and vocabulary design
  ● KV cache and inference optimization
  ● Limitations and bottlenecks

Hands-on:
● Visualize attention maps
● Experiment with context window limits

Multimodal LLM Architectures

• Expanding from text to image and audio
• Cross-modal embeddings
• Vision-language models overview
• Audio-text interaction models
• Multimodal pipeline design patterns

Hands-on:
● Build multimodal assistant using text + image APIs
● Integrate image generation and text summarization

Fine-Tuning & Optimization Techniques

• Training lifecycle: Pre-training vs Fine-tuning
  ● Domain adaptation strategies
  ● Dataset curation and cleaning for enterprise domains
  ● Parameter-efficient fine-tuning (LoRA)
  ● QLoRA for memory-efficient training
  ● Hyperparameter tuning strategies
  ● Quantization (8-bit, 4-bit)
  ● Trade-offs between accuracy and inference speed

Hands-on:
● Implement LoRA fine-tuning pipeline
● Apply quantization for optimized inference

LLM Deployment Strategies

• End-to-end deployment pipeline
• API gateway design
• Cloud-native vs on-premise vs hybrid
• Serverless inference architectures
• GPU provisioning strategies
• Scaling strategies and load balancing
• Secure model access and API authentication

Hands-on:
● Deploy local model using Ollama
● Build REST endpoint for LLM service

Streaming & Low-Latency Applications

• Token streaming architecture
• Reducing inference latency
• Caching strategies
• Cost optimization strategies
• Observability and logging patterns

Hands-on:
● Implement streaming response endpoint
● Benchmark latency across deployment modes

Multi-Agent AI Systems

• Agentic AI fundamentals (planning, reasoning, memory)
• Tool calling and structured outputs
• Orchestration frameworks (LangChain)
• Designing autonomous workflows
• Error handling and fallback strategies
• Governance and safety considerations

Hands-on:
● Build multi-agent workflow
● Integrate APIs and tool calls

Retrieval-Augmented Generation (RAG) Engineering

• RAG architecture fundamentals
• Chunking and embedding strategies
• Vector databases (ChromaDB vs FAISS)
• Building ingestion pipelines for enterprise documents
• Retrieval optimization techniques
• Hybrid search (semantic + keyword)
• Debugging hallucination issues

Hands-on:
● Build complete RAG pipeline
● Evaluate retrieval quality

Evaluation & Performance Optimization

• Model-centric metrics (loss, perplexity)
• Application-level metrics (latency, cost per query)
• Human evaluation strategies
• Benchmarking using custom datasets
• Monitoring drift and degradation
• Post-deployment logging and observability
• Security and compliance considerations
• Hands-on:
  • Create evaluation benchmark suite
  • Analyze cost-performance trade-offs

Capstone Project: End-to-End LLM Solution

• Participants will choose one of the following tracks:
  • Enterprise RAG Assistant
  • Multi-Agent Research System
  • Multimodal AI Assistant
• Project Activities:
  • Define use case and architecture
  • Prepare and curate dataset
  • Fine-tune or implement RAG strategy
  • Deploy locally (Ollama) or to cloud (AWS/Azure)
  • Benchmark performance (latency, accuracy, cost)
  • Present architectural decisions and trade-offs