Large Language Model Engineering and Optimization

Large Language Models (LLMs) such as GPT, Claude, and LLaMA are revolutionizing the enterprise AI landscape, enabling advanced capabilities in natural language understanding, content generation, automation, and decision intelligence. However, deploying and optimizing LLMs at enterprise scale requires specialized expertise in model engineering, fine-tuning, and performance optimization. This 7-day corporate training program provides a deep, hands-on learning experience focused on the lifecycle of LLMs — from model design and customization to inference optimization, evaluation, and large-scale deployment. Participants will gain the technical and strategic knowledge needed to develop, adapt, and operationalize LLMs for real-world business applications.

Objectives of the Training

Understand the architecture, training, and optimization of Large Language Models.
Learn fine-tuning, prompt-tuning, and adapter-based optimization techniques.
Explore methods to reduce inference cost and improve latency for large-scale deployments.
Gain experience using frameworks like Hugging Face Transformers, DeepSpeed, and PEFT.
Learn model evaluation, interpretability, and governance for enterprise-grade AI systems.

Prerequisites

Proficiency in Python programming and experience with deep learning frameworks (PyTorch/TensorFlow).
Basic understanding of transformer architecture and NLP principles.
Familiarity with cloud environments (AWS, Azure, or GCP) and containerized deployments.
Awareness of ethical AI and data governance concepts is an advantage.

What You Will Learn

Deep understanding of LLM architecture and scaling principles.
Fine-tuning and instruction-tuning methodologies.
Parameter-efficient tuning (LoRA, Prefix, and Adapter Tuning).
Model quantization, pruning, and distillation for performance optimization.
Evaluation metrics and benchmarks for LLMs.
Enterprise deployment strategies and optimization for inference at scale.

Target Audience

This course is ideal for AI Engineers, NLP Specialists, Machine Learning Architects, and Data Scientists who are building or optimizing LLM-based solutions. It is also suitable for Technical Leads, Solution Architects, and AI Product Managers focused on integrating LLMs into enterprise workflows and customer solutions.

Detailed 7-Day Curriculum

Day 1 – Foundations of Large Language Models (6 Hours)

Session 1: Evolution of NLP to LLMs – Transformer Revolution and Scaling Laws.
Session 2: LLM Architecture Deep Dive – Attention Mechanisms, Layers, and Tokenization.
Session 3: Pretraining vs. Fine-tuning – Objectives, Datasets, and Compute Requirements.
Hands-on: Exploring Open-Source LLMs using Hugging Face and LangChain.

Day 2 – Model Customization and Fine-Tuning (6 Hours)

Session 1: Data Preparation and Curation for Fine-Tuning LLMs.
Session 2: Full Fine-Tuning vs. Instruction-Tuning – When and How to Use Each.
Session 3: Parameter-Efficient Fine-Tuning (PEFT) with LoRA, Prefix, and Adapter Tuning.
Workshop: Fine-Tuning a Pretrained Model for Domain-Specific Chatbot.

Day 3 – Scaling and Optimization Techniques (6 Hours)

Session 1: Distributed and Parallel Training using DeepSpeed and Accelerate.
Session 2: Mixed Precision, Gradient Accumulation, and ZeRO Optimization.
Session 3: Checkpointing, Memory Offloading, and Efficient Data Loading.
Case Study: Scaling LLM Training for Financial Document Summarization.

Day 4 – Inference Optimization and Model Compression (6 Hours)

Session 1: Inference Acceleration using Quantization and Pruning.
Session 2: Model Distillation and Lightweight Deployment Strategies.
Session 3: TensorRT, vLLM, and DeepSpeed-Inference for High-Performance Serving.
Hands-on: Deploying an Optimized LLM for Text Generation and Summarization.

Day 5 – Evaluation, Monitoring, and Responsible AI (6 Hours)

Session 1: LLM Evaluation Metrics – Perplexity, BLEU, ROUGE, and Hallucination Testing.
Session 2: Model Interpretability, Safety Guardrails, and Ethical Alignment.
Session 3: Continuous Monitoring and Fine-Tuning Pipelines for Enterprise Environments.
Workshop: Designing an Evaluation Pipeline for a Domain-Specific LLM.

Day 6 – Enterprise Deployment and Integration (6 Hours)

Session 1: Serving LLMs using APIs, Containers, and Serverless Architectures.
Session 2: Integration with LangChain, Vector Databases, and RAG Pipelines.
Session 3: Cost Optimization and Resource Management for Enterprise LLMs.
Case Study: Deploying a Secure Internal Knowledge Assistant using an Optimized LLM.

Day 7 – Capstone Project & Future of LLMs (6 Hours)

Session 1: Capstone Project – Fine-Tuning and Deploying a Custom LLM for Enterprise Use.
Session 2: Project Presentation and Discussion on Performance and Optimization Metrics.
Session 3: Future Trends – Multimodal LLMs, Agentic AI, and Efficient AI Scaling.
Panel Discussion: Strategic Roadmap for LLM Adoption in the Enterprise.

Capstone Project

In the capstone project, participants will fine-tune and optimize an open-source LLM for a real-world enterprise application. Possible projects include developing a document summarization assistant, intelligent chatbot, or code generation model. Participants will focus on tuning, compression, and deployment strategies while ensuring the model meets performance and reliability benchmarks.

Future Trends in LLM Engineering and Optimization

The future of LLM engineering lies in efficiency, autonomy, and adaptability. Advances such as retrieval-augmented generation (RAG), modular architectures, and agent-based systems are redefining LLM capabilities. With breakthroughs in parameter-efficient tuning and lightweight model architectures, enterprises can expect increasingly personalized, cost-effective, and responsible AI systems. Organizations that master LLM optimization today will lead the next wave of AI innovation in productivity and intelligent automation.