Model Control Protocol training course

Model Control Protocol (MCP): Mastering Claude API Interactions. build powerful applications with Claude AI.

JBI training course London UK

"Our tailored course provided a well rounded introduction and also covered some intermediate level topics that we needed to know. " Brian Leek, Data Analyst, May 2022

Public Courses

03/08/26 - 2 days
£3750 +VAT
14/09/26 - 2 days
£3750 +VAT
26/10/26 - 2 days
£3750 +VAT

Customised Courses

* Train a team
* Tailor content
* Flex dates
From £1200 / day
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JBI training course London UK

  • Comprehensive Claude API setup and hands-on prompt engineering
  • Advanced prompting techniques: few-shot, chain-of-thought, structured outputs
  • Managing multi-turn conversations and real-time streaming responses
  • Working with multimodal inputs: images and documents
  • Building tool-augmented assistants with function calling
  • Performance optimization: context management, batching, async processing
  • Retrieval-Augmented Generation (RAG) and safety best practices
  • Capstone project development with expert guidance and post-workshop support

Day 1: Fundamentals to Advanced Techniques

Introduction and Setup 

• Claude capabilities and API overview

• Environment setup and authentication

• First API calls with the Anthropic Python client

• Hands-on: Sending basic prompts to Claude

Effective Prompting 

• Parameter tuning (temperature, max_tokens)

• System prompts and their impact

• Advanced prompting techniques:

• Few-shot learning

• Chain-of-thought reasoning

• Structured outputs

Hands-on: Optimizing prompts for specific tasks

Managing Conversations

• Multi-turn conversation handling

• Context management strategies

• Streaming responses for real-time applications

Hands-on: Building a simple conversational agent

Lunch Break 

Multimodal Capabilities 

• Working with images and text

• Document analysis patterns

• Multimodal prompt best practices

• Hands-on: Implementing a simple image analyzer

 

 

Tool Use and Function Calling 

• Tool schema definition

• Implementing single and multiple tools

• Handling tool responses

• Hands-on: Building a tool-enhanced assistant

Performance Optimization 

• Context window management

• Token optimization strategies

• Asynchronous processing patterns

• Hands-on: Processing batch requests efficiently

Guided Project Work

• Start work on capstone mini-projects

• Instructor assistance and code review

• Implementation planning

Day 2: Advanced Implementation and Production (Optional Extension)

Retrieval-Augmented Generation 

• Vector embeddings basics

• Simple RAG implementation

• Improving relevance and accuracy

• Hands-on: Building a knowledge-based assistant

Safety and Error Handling

• Content moderation approaches

• Robust error handling

• Rate limit management

• Hands-on: Implementing resilient Claude applications

Production Considerations 

• Model version migration

• Testing and evaluation

• Monitoring and logging

Hands-on: Creating a version-agnostic interface

Capstone Project Development 

• Guided implementation of chosen project

• Apply multiple advanced techniques

• Individual assistance from instructor

• Code optimization and review

Project Presentations and Conclusion 

• Brief project demonstrations

• Best practices review

• Resources for continued learning

• Q&A and workshop wrap-up

Capstone Mini-Project Options

Participants will select one of the following projects to develop:

1. Conversational document analyzer with RAG

2. Multi-tool travel planning assistant

3. Image-based product recommendation system

4. Content moderation and summarization service

• Comprehensive notebook with all code examples

• Starter templates for projects

• Reference cheat sheet for Claude API parameters

• Follow-up resources for continued learning

Post-Workshop Support

• 2-week access to workshop discussion forum

• Additional reference materials and examples

• Recorded solutions to all exercises

JBI training course London UK

  • AI/ML Engineers looking to build advanced applications using Claude
  • Developers integrating language models into products or tools
  • Technical Product Managers overseeing AI-driven projects
  • Data Scientists interested in prompt engineering and LLM capabilities
  • Startup Founders building AI-enabled platforms or assistants
  • Conversational UX Designers working on chatbots or voice assistants
  • Educators & Researchers exploring applied use of multimodal LLMs
  • Anyone with Python experience wanting hands-on LLM skills with Claude
  •  

Prerequisites

• Python programming experience

• Familiarity with REST APIs

• Laptop with Python 3.8+ installed

• Anthropic API key (obtain before workshop)


5 star

4.8 out of 5 average

"Our tailored course provided a well rounded introduction and also covered some intermediate level topics that we needed to know. " Brian Leek, Data Analyst, May 2022



“JBI  did a great job of customizing their syllabus to suit our business  needs and also bringing our team up to speed on the current best practices. ” Brian F, Team Lead, RBS, Data Analysis Course, 20 April 2022

 

 

JBI training course London UK

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Workshop Description

This intensive workshop provides developers with hands-on experience using Anthropic's Model Control Protocol (MCP) to build powerful applications with Claude AI. Participants will rapidly progress from basic API interactions to advanced implementation patterns through practical, code[1]focused sessions.

Prerequisites

• Python programming experience

• Familiarity with REST APIs

• Laptop with Python 3.8+ installed

• Anthropic API key (obtain before workshop)

Workshop Materials

• Jupyter Notebook with complete code examples

• Reference documentation

• Solution templates for exercises

Generative AI training teaches individuals and teams how to use AI systems that generate text, code, images, and other content — including tools such as ChatGPT, Claude, Gemini, and Microsoft Copilot. JBI's Generative AI training courses are suitable for business professionals, developers, analysts, managers, and technical leaders who want to use AI more effectively in their work, improve productivity, or build AI-powered applications and workflows.
Prompt engineering is the practice of designing and structuring inputs to AI language models to obtain accurate, relevant, and consistent outputs. Effective prompt engineering helps users get better results from AI tools, reduce errors and hallucinations, and build reliable AI-assisted workflows. JBI offers dedicated prompt engineering courses for general LLM use, ChatGPT-specific use, and advanced GPT and LLM applications.
Yes. All JBI Generative AI and LLM training courses are available as live online instructor-led sessions, with the same hands-on exercises and expert instruction as classroom delivery. Online training is available to delegates across the UK and internationally.
A Large Language Model (LLM) is an AI system trained on large amounts of text data to understand and generate human language. LLMs such as GPT-4, Claude, Llama, and Gemini are the foundation of modern generative AI tools including ChatGPT and Microsoft Copilot. JBI's LLM training courses cover how LLMs work, their capabilities and limitations, how to use them effectively through prompt engineering, and how to build applications on top of LLM APIs.
Yes. All JBI Generative AI training courses can be delivered as bespoke closed-group programmes for corporate teams. Content is tailored to your team's role, existing AI experience, specific tools in use, and business objectives. JBI has delivered bespoke Generative AI and LLM training to teams in financial services, professional services, retail, media, the public sector, and technology organisations across the UK.
Retrieval-Augmented Generation (RAG) is a technique that enables AI language models to access and reason over external, up-to-date knowledge sources — such as internal documents, databases, or APIs — rather than relying solely on their training data. RAG is widely used to ground AI responses in factual, organisation-specific information. JBI covers RAG in several courses including Build Agentic AIs with Python, RAG and MCP and Build a Chatbot with Python, RAG and OpenAI.
Model Control Protocol (MCP) is an open standard for connecting AI models to tools, data sources, and external services in a structured and interoperable way. It provides a consistent interface for AI agents to access APIs, databases, file systems, and other resources. JBI offers a dedicated MCP training course covering server and client implementation, Claude API integration, and production deployment of MCP-enabled AI systems.
Yes. JBI Training offers a 3-day LangChain for AI Agents training course covering LLM workflow design, chain construction, agent development, memory systems, retrieval integration, and production deployment using the LangChain framework in Python. The course is designed for developers building LLM-powered applications and AI agent systems.
Prompt engineering focuses on crafting effective inputs to AI models to improve the quality and consistency of outputs — a skill relevant to any user of AI tools, technical or non-technical. Building AI applications with LLMs involves programming against model APIs, designing application architecture, managing context and memory, handling tool use and retrieval, and deploying AI-powered systems. JBI offers training for both — from introductory prompt engineering to advanced LLM application development.
Yes. JBI's Generative AI and LLM training range includes courses for complete beginners such as Harnessing Generative AI, Prompt Engineering for ChatGPT, and AI Prompt Engineering, which require no prior programming or AI experience. Developer-focused courses such as LangChain for AI Agents and Mastering LLMs require programming experience and prior familiarity with AI concepts. Each course page specifies the recommended experience level and prerequisites.
JBI Training regularly reviews and updates its Generative AI and LLM training content to keep pace with the rapid developments in this field. New model releases, updated prompt engineering best practices, emerging frameworks such as LangChain and MCP, and evolving governance requirements all feed into JBI's course refresh cycle. Whether you are learning about ChatGPT, Claude, Gemini, or open-source LLMs, JBI's training reflects how these tools are being used in practice today — not how they worked a year ago.

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