Chelsea AI Ventures
Technical AI Training Developer Pathway 6 Weeks (Online, approx. 4-5 hours/week) or 3 Day Intensive (In-Person, London)

GenAI Application Development: Building with LLMs

Stop just chatting with AI and start building with it. Master LLMs via APIs, advanced prompts, and real-world applications using LangChain and RAG

Target Audience

The Modern Software Developer

Core Value

Go beyond prompting and build real-world applications powered by large language models

Key Differentiator

Focuses on the modern paradigm of using models via APIs and frameworks like LangChain

Learning Objectives

  • Integrate with major LLM provider APIs (OpenAI, Anthropic, Hugging Face) in Python
  • Apply advanced prompt engineering techniques to improve performance and reliability
  • Use the LangChain framework to build complex chains and applications
  • Design, build, and debug a complete Retrieval-Augmented Generation (RAG) pipeline
  • Deploy a simple LLM-powered application using a web framework

Prerequisites

Intermediate Python proficiency and familiarity with using APIs.

Course Structure

Week 1: The Modern GenAI Stack & LLM APIs

Overview of the ecosystem: Foundation Models, APIs, Orchestration Frameworks, Vector Databases. Hands-on with multiple LLM providers.

Activities:

  • Set up development environment
  • Make first API calls to 3 different LLM providers

Week 2: Advanced Prompt Engineering

Beyond basic questions. Few-Shot Learning, Chain-of-Thought prompting, and creating effective system prompts.

Activities:

  • Build prompt template library
  • A/B test different prompting strategies

Weeks 3-4: Building Applications with LangChain

Week 3: Fundamentals - schemas, models, prompts, simple chains. Week 4: Advanced - document loaders, text splitters, embeddings, vector stores.

Activities:

  • Build progressively complex LangChain applications
  • Implement custom chain types

Week 5: Deep Dive into RAG

The most important pattern in GenAI. Build complete RAG pipeline: document loading, embedding creation, vector storage, and query engine.

Activities:

  • Build RAG system from scratch
  • Optimize retrieval quality

Week 6: Deployment and Production Considerations

From notebook to web. Wrap LLM applications in APIs, evaluate RAG systems, manage costs and latency.

Activities:

  • Deploy chatbot as web application
  • Implement cost tracking and optimization

Topics Covered

LLM API integration (OpenAI, Anthropic, Hugging Face)
Advanced prompt engineering techniques
System prompt design
LangChain framework fundamentals
Document processing and chunking
Embedding models and vector databases
Retrieval-Augmented Generation (RAG)
Semantic search implementation
LLM application deployment
Cost management and optimization
Performance evaluation
Production best practices

Capstone Project

Build a complete document-based chatbot: ingest custom documents via RAG pipeline and deploy as a web application that accurately answers questions.

Why This Course Matters

The landscape of AI development has fundamentally shifted. Today’s opportunities aren’t in training models from scratch—they’re in creatively leveraging the incredible capabilities of existing foundation models. Companies aren’t hiring developers who can build GPT-5; they’re hiring developers who can build innovative applications using GPT-4.

This shift represents the biggest opportunity in software development today. While others debate model architectures, practical developers are shipping AI-powered products that solve real problems. This course positions you at the forefront of this new paradigm.

What Makes This Course Different

We focus relentlessly on what companies actually need: developers who can integrate LLMs into production applications. You won’t spend time on model training theory—you’ll spend it mastering the tools and patterns that power real GenAI applications.

The course is built around practical implementation. You’ll work with the same tech stack we use at Chelsea AI Ventures, including building RAG systems similar to those powering KirokuForms. Every technique you learn has been battle-tested in production environments.

Course Philosophy

We believe the future of software development is about orchestration, not implementation. Just as modern web developers orchestrate services rather than building everything from scratch, AI developers orchestrate foundation models to create powerful applications.

This course embraces that reality. You’ll learn to think in terms of prompts as programming, chains as logic flow, and retrieval as dynamic context. It’s a new mental model for a new era of development.

Who Should Take This Course

This course is ideal if you:

  • Are a solid Python developer ready to specialize in GenAI
  • Want to build LLM applications, not train models
  • Need practical skills that companies are hiring for now
  • Are overwhelmed by the GenAI ecosystem and need structure
  • Want to move beyond simple chatbots to sophisticated applications
  • Learn best through building real projects

If you’re ready to become a GenAI developer, this course provides the practical, hands-on training you need to start building immediately.

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