---
url: "https://xcademia.com/courses/llm-app-development"
title: LLM App Development
description: "Learn to build real-world AI applications using LLM patterns, RAG architecture, and prompt pipelines in this mentor-led practical course.

"
publishedAt: "2026-03-16T10:56:46.949295+00:00"
updatedAt: "2026-03-30T22:50:53.7265+00:00"
type: course
code: "AID-0013"
level: Practitioner
duration_days: "3"
track: "Generative AI & Prompt Engineering"
category: "AI, Data & Analytics"
credential_tier: tier1
price_gbp: "1999"
---

# LLM App Development

> Build real-world applications powered by large language models using modern LLM design patterns and toolchains. This mentor-led course uses practical scenarios to design, integrate, and deploy production-ready AI assistants and workflows.

## Overview

Large Language Models are transforming how applications are built, enabling intelligent assistants, automation tools, and knowledge systems. This programme introduces the architecture and patterns behind modern LLM-powered applications, helping developers move from experimentation to reliable implementation.

Across this mentor-led programme, participants explore prompt pipelines, retrieval augmented generation (RAG), tool usage, and application orchestration. Through practical scenarios, learners design AI-powered features such as document assistants, knowledge chatbots, and automated workflows.

The course emphasises responsible development, evaluation strategies, and scalable deployment patterns. By the end of the programme, learners will understand how to integrate LLM capabilities into production-ready systems while maintaining reliability, governance, and cost awareness.

## Prerequisites

- Basic programming knowledge (Python or JavaScript)
- Understanding of APIs and web applications
- Familiarity with basic AI or ML concepts

## What you will learn

- Design scalable LLM-powered application architectures
- Analyse prompt pipelines and optimisation strategies
- Implement retrieval augmented generation workflows
- Evaluate reliability and safety of AI outputs
- Communicate design decisions for AI systems
- Lead development of practical LLM-powered solutions

## Skills you will gain

- Prompt pipeline design
- Retrieval augmented generation
- LLM application architecture
- Prompt chaining strategies
- AI safety controls
- LLM deployment basics

## Career progression

- AI Application Developer
- LLM Engineer
- AI Software Engineer
- Generative AI Developer
- ML Application Engineer

## Curriculum

1. **Module 1: Getting Ready**
   - LLM ecosystem overview
   - Course tools and environment setup
   - Safety and responsible AI usage
   - LLM application architecture overview
2. **Module 2: Understanding LLM Application Patterns**
   - Prompt-driven application architecture
   - Tokenisation and context windows
   - Prompt templates and chains
   - Common LLM use cases
3. **Module 3:  Prompt Engineering for Applications**
   - Structured prompt design
   - Prompt chaining strategies
   - Output control and formatting
   - Handling hallucinations
4. **Module 4:  Retrieval Augmented Generation**
   - Knowledge retrieval architectures
   - Vector databases and embeddings
   - Document chunking strategies
   - Retrieval pipelines
5. **Module 5:  Building AI Assistants and Tools**
   - Tool usage and function calling
   - Agents vs workflows
   - Memory and conversation management
   - Integrating external APIs
6. **Module 6: Evaluation, Safety, and Reliability**
   - Evaluating LLM outputs
   - Guardrails and moderation
   - Observability for AI systems
   - Error handling and fallbacks
7. **Module 7: Deploying LLM Applications**
   - Architecture patterns for deployment
   - Scaling LLM-based services
   - Cost optimisation strategies
   - Production monitoring basics

## 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

**Who should take this course?**

Developers, AI engineers, and technical professionals who want to build real applications using large language models.



**Do I need machine learning experience?**

No advanced ML experience is required, but basic understanding of APIs and programming is recommended.



**Will we build a real LLM application?**

Yes, learners work through practical scenarios to design and implement a working LLM-powered assistant.



**Which tools or frameworks are used?**

Examples include modern LLM APIs, vector databases, and orchestration frameworks used in industry.



**Does this course need an exam?**

No. Completion is based on participation in mentor-led sessions and practical scenario exercises.

## Course at a glance

| Field | Value |
| --- | --- |
| Code | AID-0013 |
| Duration | 3 days |
| Level | Practitioner |
| Track | Generative AI & Prompt Engineering |
| Category | AI, Data & Analytics |
| Credential tier | tier1 |
| Price (GBP) | £1999 |

---

## About this content

This Markdown course profile is the citation-grade twin of [LLM App Development](https://xcademia.com/courses/llm-app-development). 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-app-development
- Publisher: Xcademia — https://xcademia.com
- Catalogue index: https://xcademia.com/llms-full.txt