Name: LLM Engineering
Availability: InStock

LLM Engineering

Live Online (VILT) & Classroom Corporate Training Course

Design, integrate and optimize LLM applications - architecture, tokenization, embeddings, inference, APIs, structured outputs, tool calling, memory, context management, reliability, cost optimization and evaluation.

Expert-Led VILT & Classroom 16 Hours Level: Intermediate Certificate of Completion

Overview

What You Will Learn

By the end of this course, learners will be able to:

Explain the architecture and lifecycle of Large Language Models.
Understand Transformers, attention mechanisms, tokenization and embeddings.
Work with LLM APIs and integrate models into software applications.
Implement streaming responses and structured outputs.
Design and execute function/tool calling workflows.
Implement conversation memory and context-management strategies.
Design scalable and maintainable LLM application architectures.
Implement error handling, retries, fallbacks and cost-optimization techniques.
Evaluate LLM application quality using systematic evaluation methods.
Build and demonstrate a complete LLM-powered application.

Prerequisites

Basic Python programming and familiarity with Generative AI concepts are recommended.

Course Outline

What is a Large Language Model?
Evolution of Language Models
LLM Application Lifecycle
Model Architecture Overview
Pre-training, Fine-tuning and Inference
Parameters, Weights and Model Size
Capabilities and Limitations of LLMs
Choosing an LLM for an Application

Neural Network Foundations
Sequence Modeling and Language Representation
Transformer Architecture
Encoder and Decoder Concepts
Attention Mechanism
Self-Attention
Multi-Head Attention
Positional Information
Why Transformers Work for LLMs

What is Tokenization?
Tokens, Token IDs and Vocabulary
Common Tokenization Approaches
Token Counting and Token Budgets
What are Embeddings?
Dense Vector Representations
Semantic Similarity
Embedding Models and Use Cases

What Happens During Inference?
Input Processing and Context Construction
Logits and Next-Token Prediction
Temperature and Sampling
Top-k and Top-p Sampling
Deterministic vs Stochastic Generation
Latency and Throughput
Model Selection and Inference Trade-offs

LLM API Fundamentals
API Authentication and Configuration
SDKs and Client Libraries
Request and Response Structures
System, User and Assistant Messages
Streaming Responses
Rate Limits and Quotas
API Error Handling
Building an LLM Service Layer

Structured Output Fundamentals
JSON Responses and Output Schemas
Schema Validation
Function Calling
Tool Definitions and Parameters
Tool Selection and Execution
Multiple Tools and Tool Workflows
Handling Tool Errors
Designing Safe Tool Interfaces

Why LLM Applications Need Memory
Short-Term Conversation Memory
Long-Term Memory Concepts
Conversation History Management
Context Windows
Token Budget Management
Context Summarization
Context Compression and Selection
Avoiding Context Overflow

Anatomy of an LLM Application
Frontend, Backend and Model Layers
LLM Service and Orchestration Layers
Prompt Management
Data and External Service Integration
Synchronous vs Asynchronous Architecture
Scalability and Performance Considerations
Security and Access Control
Production Architecture Patterns

Common LLM Application Failures
Input Validation
Output Validation
Retries and Exponential Backoff
Timeouts and Fallback Models
Caching Strategies
Token and Context Optimization
Model Selection for Cost Efficiency
Monitoring API Usage and Costs

Why LLM Evaluation Matters
Defining Evaluation Criteria
Accuracy and Relevance
Factuality and Groundedness
Consistency and Reliability
Test Datasets and Evaluation Cases
Human Evaluation
LLM-as-a-Judge
Automated Evaluation Workflows
Regression Testing for LLM Applications

Project Requirements and Use-Case Definition
Design the Application Architecture
Configure an LLM API
Build the LLM Service Layer
Implement Prompt and Message Management
Add Streaming Responses
Implement Structured Outputs
Add Function/Tool Calling
Implement Conversation Memory
Manage Context and Token Budgets
Add Error Handling, Retries and Fallbacks
Implement Basic Cost Controls
Create an Evaluation Dataset
Evaluate and Improve the Application
Final Testing and Demonstration

LLM Engineering

Overview

What You Will Learn

Prerequisites

Course Outline

Why enterprise teams pick Synergific.

Live Hands-On Cloud Labs

Hire-Train-Deploy

Four-Hour Solutions SLA

Voucher-Backed Certification

Authorised Curriculum, Local Delivery

Pick the format that fits your team.

Live Online (VILT)

Classroom

Self-Paced

Blended

How a course becomes measurable skill.

Discover & set goals

Curate the right path

Deliver hands-on training

Assess & mentor

Certify & apply on the job

LLM Engineering

Overview

What You Will Learn

Prerequisites

Course Outline

Module 1: LLM Architecture Fundamentals (60 min)

Module 2: Transformers & Attention (75 min)

Module 3: Tokenization & Embeddings (60 min)

Module 4: Model Inference (60 min)

Module 5: LLM APIs & Application Integration (75 min)

Module 6: Structured Outputs & Function/Tool Calling (75 min)

Module 7: Conversation Memory & Context Management (60 min)

Module 8: LLM Application Architecture (75 min)

Module 9: Reliability, Error Handling & Cost Optimization (60 min)

Module 10: LLM Evaluation (60 min)

Module 11: Hands-on Project - Build an LLM Application (240 min)

Why enterprise teams pick Synergific.

Live Hands-On Cloud Labs

Hire-Train-Deploy

Four-Hour Solutions SLA

Voucher-Backed Certification

Authorised Curriculum, Local Delivery

Pick the format that fits your team.

Live Online (VILT)

Classroom

Self-Paced

Blended

How a course becomes measurable skill.

Discover & set goals

Curate the right path

Deliver hands-on training

Assess & mentor

Certify & apply on the job