Only Until June 18: ✓ Save up to £755 ✓ Free Workshop Day ✓ 10% Team Discount 

Only Until June 18: ✓ Save up to £755 ✓ Free Workshop Day ✓ 10% Team Discount 

Master Practical Generative AI Skills

Go from tokens to agents with the ex-Visa Chief Architect
behind Fortune 500 AI systems

September 29 - 30, 2025 | London or Online

CONFERENCE STARTS IN:

Take home working code in Python, Java, TypeScript, and C#
to use in your next AI project.

Deep dive intoTransformers, RAG, & Agentic Systems,
all hands-on coding

Meet The Mastermind

John Davies

After a degree in Astrophysics at UCL John started in hardware then assembler, C, C++ and later Java. Almost exclusively in finance he ran FX at Paribas was a global chief architect at JP Morgan, BNP Paribas and VISA. John has co-founded four successful startups since 2000, selling one of them twice to Nasdaq & LSE listed companies. After co-founding Velo Payments with the former president of VISA John has spun off the AI company Incept5. John has co-authored several Java books and is a frequent speaker at technical and banking conferences around the world. He is married to a French wife and has three boys in their 20s.

5 Reasons to join
the Bootcamp

Learn From Industry Veteran John Davies
Ex-Chief Architect at Visa, JP Morgan and BNP Paribas
check icon
check icon
Hands-on with practical code examples
across multiple languages that you can immediately apply to your projects
Master the Critical RAG
pipeline that powers modern AI applications with company data
check icon
check icon
Generate & Control Structured Outputs (JSON, XML)
from natural language for seamless integration with existing systems
Cut Through The Hype
learn what actually works and what doesn’t in Gen-AI, learn what’s actually being used today
check icon

STAY TUNED!

JOIN OUR NEWSLETTER

[mc4wp-simple-turnstile]

5 Things You’ll Finally Feel Confident Doing

  • Build and deploy multi-modal, mutli-lingual AI systems using both local models (Llama, Mistral, Qwen etc.) and cloud services with optimized performance
  • Design effective prompts that consistently produce high-quality, structured outputs from any LLM
  • Implement complete RAG pipelines with proper chunking, embedding, and vector storage techniques
  • Generate and control structured outputs (JSON, XML) from natural language for seamless integration with existing systems
  • Develop agentic systems that can reason, use tools, and solve complex problems autonomously

Differentiation from other workshops at the conference

  • Focused on practical implementation rather than theoretical concepts
  • Covers multiple programming languages instead of being language-specific, you can work in your preferred language
  • Balanced approach to both local and cloud models, not advocating for a single vendor
  • Comprehensive end-to-end coverage from fundamentals to advanced topics like agentic systems
  • Includes live coding and debugging sessions with take-home templates

Bootcamp Day 1

Kick-Off & Intro

  • Welcome from John Davies (ex-Chief Architect at Visa, JP Morgan and BNP Paribas)
  • Overview of the day: bridging AI & Gen-AI theory with live coding, deep technical dives, and real-world integration

AI Fundamentals – How Transformers & Tokenisation Really Work

  • Neural networks (classic AI) vs. Generative AI & LLMs
  • Deep dive into tokenisation, embeddings, transformers, and attention mechanisms
  • Live demo: Running LLMs (Text, Chat, Instruction, Vision, Speech) locally, remotely, and in-cloud

Hands-On with Local, Remote & Cloud Models

  • Installing and using local models (e.g., Llama, Qwen, Mistral, Gemma)
  • Live coding: Encode/decode text in Python, inspect token IDs, tune parameters (batch size, context window, temperature)
  • Code samples provided in C, C#, Java, TypeScript, Python and others
  • Examples for local, remote, and cloud deployment

Prompt Engineering & Model Configuration

  • Understanding model parameters – temperature, context windows, top-k, top-p etc.
  • Techniques for effective prompt design and output control
  • Live coding: Build a Python tool that outputs structured JSON for summarisation/data extraction
  • Best practices for prompt debugging and iterative refinement
  • Share debugging tips and optimisation hacks with fellow devs

RAG – Retrieval-Augmented Generation

  • Chunking, embedding, parsing, and vector storage
  • RAG optimisation strategies
  • Hands-on RAG – bring your own data!
  • Advanced chunking, embedding techniques, and vector DB options

Bootcamp Day 2

Summarisation, Data Extraction & Sentiment Analysis

  • Reduce document size, reformat or translate content
  • Extract meaning, sentiment, and key data from raw text
  • Hands-on

Structured Output with LLMs

  • Techniques for consistently generating structured JSON and other formats from natural language input
  • Hands-on

Code Generation Techniques

  • How code generation really works
  • Complete, insert (fill-in-the-middle), and instruct modes
  • Exploring specialist coding models
  • Hands-on

Tool Calling & Model Component Protocol (MCP)

  • How to call tools with LLMs
  • Building and orchestrating MCPs

Agentic Systems in Action

  • What are agentic systems?
  • A walkthrough of a basic agentic flow

MCPs

  • The MCP architecture
  • Building MCP services, servers and clients
  • Running MCPs on any model

Q&A & Wrap-Up

  • Ask questions, share feedback, or pitch your own AI ideas
  • Where to go next: further learning and project suggestions

Designed for These Roles

Secure Your Spot