---
url: "https://xcademia.com/courses/llm-engineering-bootcamp"
title: LLM Engineering Bootcamp
description: " Learn LLM engineering, RAG, agents, and LangChain. Build production-ready AI applications in this practical, mentor-led bootcamp."
publishedAt: "2026-03-21T12:22:14.348935+00:00"
updatedAt: "2026-03-30T22:50:53.7265+00:00"
type: course
code: "AID-0083"
level: Professional
duration_days: "4"
track: "AI-Native App Development "
category: "AI, Data & Analytics"
credential_tier: tier1
price_gbp: "2199"
---

# LLM Engineering Bootcamp

> Build end-to-end LLM applications from transformer basics to production systems. Gain hands-on experience with RAG, agents, and evaluations in mentor-led, practical scenarios.

## Overview

Large Language Models are transforming how software is designed and delivered. This intensive bootcamp provides a complete journey from understanding transformer-based models to building production-ready LLM applications using modern frameworks and tools.

Participants will explore key concepts including fine-tuning, retrieval-augmented generation, evaluation frameworks, and agent-based systems. The course emphasises practical implementation, enabling learners to move beyond theory into real-world LLM product development.

Through mentor-led sessions and practical scenarios, learners will build multiple LLM-powered applications using tools such as LangChain, integrate data sources, and apply best practices for performance, cost, and reliability. By the end, participants will have the skills to design and deploy scalable AI systems.

## Prerequisites

- Intermediate Python or JavaScript
- Understanding of APIs and data handling
- Basic knowledge of machine learning concepts

## What you will learn

- Design scalable LLM application architectures
- Analyse model performance and outputs
- Implement RAG and agent-based systems
- Evaluate LLM applications using metrics
- Communicate technical solutions effectively
- Lead LLM product development initiatives

## Skills you will gain

- Transformer fundamentals
- Prompt engineering techniques
- RAG system design
- LLM fine-tuning basics
- Agent architecture patterns
- LangChain development
- LLM deployment strategies

## Career progression

- LLM Engineer
- AI Engineer
- ML Engineer
- AI Product Developer
- Software Engineer

## Curriculum

1. **Module 1: Getting Ready**
   - LLM fundamentals and environment setup
   - Overview of tools and frameworks
   - Development workflow preparation
2. **Module 2:  Transformer Architecture Fundamentals**
   - Attention mechanism basics
   - Transformer components and flow
   - Tokenisation and embeddings
3. **Module 3: Working with LLM APIs and Models**
   - Using hosted LLM APIs
   - Prompt structuring and optimisation
   - Managing tokens and cost
4. **Module 4: Retrieval-Augmented Generation (RAG)**
   - RAG architecture and patterns
   - Vector databases and embeddings
   - Document ingestion and retrieval
5. **Module 5: Fine-Tuning and Model Adaptation**
   - Fine-tuning concepts and methods
   - When to fine-tune vs prompt
   - Dataset preparation basics
6. **Module 6:  LLM Evaluation and Observability**
   - Evaluation frameworks and metrics
   - Testing outputs and reliability
   - Monitoring and logging
7. **Module 7: Building Agents and Tool Use**
   - Agent design patterns
   - Tool integration and orchestration
   - Multi-step reasoning workflows
8. **Module 8: LangChain and Application Frameworks**
   - LangChain fundamentals
   - Chains, agents, and memory
   - Building structured applications
9. **Module 9: Production Deployment and Scaling**
   - Deploying LLM applications
   - Performance and latency optimisation
   - Security and cost control
10. **Module 10: Practical Scenarios and Capstone**
   - Build an end-to-end LLM product
   - Real-world use cases
   - Review and optimisation

## Exam & certification

You will receive an Xcademia certificate of completion based on participation and successful completion of labs and scenario simulations.

## Delivery options

- **Live Online** — Join live instructor-led sessions from anywhere. Interactive, engaging, and flexible.
- **Onsite Training** — We come to you. Training delivered at your workplace for teams of 6 or more.
- **Venue-Based** — Classroom training at a professional venue. Ideal for focused, immersive learning.
- **Blended** — Combine online and in-person learning for maximum flexibility and impact.

## Frequently asked questions

**Do I need prior AI experience?**

Basic understanding of machine learning is recommended but not advanced expertise.



**Will I build real LLM applications?**

Yes, the course is mentor-led with practical scenarios and end-to-end builds.



**Which tools are used in this bootcamp?**

Tools include LangChain, vector databases, and modern LLM APIs.



**Is fine-tuning covered in depth?**

You will learn practical approaches and when it is appropriate to use fine-tuning.



**Can this help me transition into AI roles?**

Yes, it provides job-ready skills for LLM engineering and AI development roles.

## Course at a glance

| Field | Value |
| --- | --- |
| Code | AID-0083 |
| Duration | 4 days |
| Level | Professional |
| Track | AI-Native App Development  |
| Category | AI, Data & Analytics |
| Credential tier | tier1 |
| Price (GBP) | £2199 |

---

## About this content

This Markdown course profile is the citation-grade twin of [LLM Engineering Bootcamp](https://xcademia.com/courses/llm-engineering-bootcamp). It is published by **Xcademia** (UK Companies House 12322710) and is available for AI search engines and large language models to index, summarise, and cite.

When citing or quoting, please attribute *Xcademia* and link back to the source URL above.

- Source: https://xcademia.com/courses/llm-engineering-bootcamp
- Publisher: Xcademia — https://xcademia.com
- Catalogue index: https://xcademia.com/llms-full.txt