Live, instructor-led AI-assisted coding training for software engineers, technical professionals and advanced learners. Register for the complete scheduled programme or individual sessions, with private training also available for organisations and universities. Build, test and govern practical workflows spanning bounded prompts, repository RAG, secure MCP tools, stateful agents and a verified capstone.
UPCOMING LIVE ONLINE TRAINING
AI-Assisted Coding, RAG, MCP and Agentic AI – November 2026
Join the next live, instructor-led 28-hour programme and build practical skills across AI-assisted coding, prompt engineering, repository RAG, Model Context Protocol (MCP), AI agents, agentic workflows, testing, security and verification.
| Training Details | November 2026 Programme |
|---|---|
| Dates | 2–5 & 9–11 November 2026 |
| Time | 6:00 PM – 10:00 PM IST |
| Delivery | Live online via Microsoft Teams |
| Programme | 7 sessions · 28 hours |
| Full programme fee | ₹47,999 (~£372) |
| Student discount | 50% discount for eligible university students |
| Private cohort | 25% discount for eligible cohort bookings |
Book the Full Programme or Individual Sessions
Register for the complete 28-hour programme or choose individual four-hour sessions based on your learning needs. The full programme provides the complete learning path across AI-assisted coding, RAG, MCP, AI agents, agentic workflows and the final capstone.
| Date | Training Session | Fee | Registration |
|---|---|---|---|
| 2–5 & 9–11 November 2026 | Full 28-Hour Programme - All 7 Sessions |
₹47,999 (~£372) | Register for Full Programme |
| 2 Nov 2026 | Session 1: LLMs, Prompt Engineering & Safe Code Generation |
₹7,799 (~£60) | Register |
| 3 Nov 2026 | Session 2: AI-Assisted Software Development Workflow |
₹7,799 (~£60) | Register |
| 4 Nov 2026 | Session 3: RAG for Codebases |
₹7,799 (~£60) | Register |
| 5 Nov 2026 | Session 4: Model Context Protocol (MCP) |
₹7,799 (~£60) | Register |
| 9 Nov 2026 | Session 5: AI Agents & Tool Calling |
₹7,799 (~£60) | Register |
| 10 Nov 2026 | Session 6: Agentic Coding, Quality & Ops |
₹7,799 (~£60) | Register |
| 11 Nov 2026 | Session 7: Capstone Project & Verification |
₹9,599 (~£74) | Register |
Pricing note: India programme fees are shown in INR, with approximate GBP equivalents for reference. The final amount and currency shown at TicketTailor checkout will apply.
Build a Controlled AI Coding Workflow
AI coding assistants can accelerate exploration, implementation and review, but effective use requires more than generating code. This live online, instructor-led training develops the engineering practices needed to define bounded tasks, supply appropriate repository context, verify changes, connect tools safely and produce evidence that individuals and teams can review and maintain.
The course combines prompt engineering, large language models, retrieval-augmented generation (RAG), Model Context Protocol (MCP), AI agents, agentic workflows and AI-assisted coding. Every module follows a practical cycle of explanation, demonstration, a knowledge check and a hands-on exercise.
Course at a glance
| Area | Details |
|---|---|
| Delivery | Live, instructor-led online training. |
| Enrolment routes | Individual enrolment on scheduled courses or private cohorts for organisations and universities. |
| Course options | 4-hour executive briefing, 12-hour foundation workshop or 28-hour professional programme. |
| Practical evidence | Code, prompts, tests, review notes, capstone results and operational handover artefacts. |
| Tools | Tool-neutral principles demonstrated using agreed tools such as Claude Code or Codex, Python, Git, pytest, Qdrant, the official MCP Python SDK, LangChain and LangGraph. |
| Private cohort size | Up to 20 participants. |
AUDIENCE PATHWAYS
Choose the route that fits your goal
The technical core remains consistent. The examples, delivery format and evidence are adapted to the buyer and participant context.
For businesses and engineering teams
Designed for software engineers, technical leads, QA engineers, architects and transformation teams that need a safer, repeatable approach to AI-assisted development.
- Use an approved client repository or Alpinum sample repository.
- Apply the workflow to a relevant engineering use case.
- Finish with adoption, governance and operational handover evidence.
- Available as a private cohort with customisation options.
For universities and advanced learners
Suitable for final-year students, postgraduate learners, faculty and experienced early-career developers who already have basic programming and Git skills.
- Work from a structured sample repository and defined engineering tasks.
- Build practical evidence for a portfolio through exercises and the capstone.
- Develop software engineering skills; the course does not promise interviews or job placement.
For working professionals
A structured route for working engineers and experienced independent developers who want verified practice across AI coding, RAG, MCP and agentic workflows.
- Complete an individual project and evidence pack.
- Strengthen the ability to review, test and explain AI-assisted changes.
Why Alpinum
- Engineering-first: the course begins with requirements, repository context, acceptance criteria and verification, not tool hype.
- Evidence-led: participants retain prompts, code, tests, review notes and capstone handover artefacts.
- Safe by design: data classification, secrets, intellectual property, least privilege, approvals and human review are integrated throughout.
- Tool-neutral: reusable engineering principles are taught through agreed contemporary tools.
- Adaptable: one technical core supports buyer briefings, workshops, professional cohorts and company-specific delivery.
TRAINING PORTFOLIO
Choose Your Training Route
Individuals can join scheduled live online training, including the full programme or selected sessions. Organisations and universities can also request private training tailored to their teams, tools and learning requirements.
Individual Registration
Joining independently? Register for the scheduled November 2026 live online training. You can book the complete 28-hour programme or select individual four-hour sessions, with the same structured teaching, practical exercises and evidence-based learning approach.
Private Training for Organisations & Universities
Organisations and universities can request private live online training tailored to their team, approved tools, repositories, policies and learning requirements. Delivery format, schedule and course emphasis can be agreed to suit the group.
What will participants be able to do
- Use Claude Code or Codex to make bounded, reviewable and testable repository changes.
- Write prompts that define the task, constraints, examples and acceptance criteria.
- Build a repository RAG workflow with controlled indexing, retrieval, citations and evaluation.
- Design and secure MCP resources and tools using least privilege and approval controls.
- Create typed AI agents and stateful workflows with appropriate guardrails.
- Apply security checks, tracing, tests, evaluation, cost controls and human review.
- Complete an AI-assisted coding capstone and produce a clear handover pack.
FLAGSHIP CURRICULUM
Professional Certificate: Seven sessions, 14 modules
The 28-hour flagship programme combines guided learning with demonstrations, exercises, quizzes and a capstone. The sequence moves from controlled prompting to retrieval, tool integration, agents and operational handover.
| Session | Module A | Module B | Practical emphasis |
|---|---|---|---|
| 1 | LLM Fundamentals & Prompt Engineering | Safe Context & Verified Output | Understand model behaviour; define bounded tasks, constraints and acceptance criteria. |
| 2 | Requirements & Repository Context | Test-Driven Debugging & Review | Ground changes in the codebase; use tests, debugging and review to validate AI-assisted work. |
| 3 | RAG Architecture & Repository Indexing | Retrieval, Context Assembly & Evaluation | Index approved repository content; retrieve relevant context with citations and measurable evaluation. |
| 4 | MCP Architecture & Server Design | Building, Connecting & Securing MCP | Expose controlled resources and tools; apply authentication, least privilege and approvals. |
| 5 | Agent Fundamentals & Typed Tools | Orchestration, Guardrails & Tracing | Build tool-using agents; manage state, failure modes, human checkpoints and traceability. |
| 6 | Agentic Coding Workflow & Evaluation | Security, Observability & Cost | Combine the workflow end to end; assess quality, security, latency, usage and cost. |
| 7 | Capstone Architecture & Implementation | Verification & Operational Handover | Build, test and review the capstone; document limitations, evidence and operating guidance. |
How each module works
| Step | Participant experience |
|---|---|
| Explain | A concise engineering concept and the decisions behind it. |
| Demonstrate | A live example using the agreed toolchain. |
| Check | A short quiz to confirm understanding. |
| Practise | A bounded exercise with expected evidence and review criteria. |
EVIDENCE AND READINESS
Assessment, prerequisites and delivery controls
Assessment and completion
- 14 short quizzes, one for each module.
- 14 demonstrations and 14 corresponding practical exercises.
- A capstone project that integrates AI-assisted coding, retrieval, tool use, verification and handover.
- A certificate of completion following required attendance and satisfactory practical evidence.
Prerequisites
- Basic programming experience; Python is preferred for the practical examples.
- Working familiarity with Git and the command line.
- A suitable laptop and access to the agreed AI coding assistant and development tools.
- Individual participants use the supplied sample repository. For private cohorts, access is limited to repositories and data approved by the organisation.
Responsible delivery
Alpinum supplies the sample repository, setup guidance, practical labs, demonstrations, quizzes, rubrics, capstone criteria and review checklists. Individual participants use the agreed course tools and sample repository. For private cohorts, the organisation confirms participant readiness, approved tools, repository and data policies, online platform and access arrangements, and certificate wording.
Tools and controls
The course teaches reusable principles rather than dependence on one vendor. Subject to the agreed online delivery setup, examples may use Claude Code or Codex, Python, Git, pytest, Qdrant, the official MCP Python SDK, LangChain and LangGraph.
- Data classification and safe context selection
- Secrets and intellectual-property protection
- Least-privilege tools and approval checkpoints
- Testing, tracing, evaluation and human review
- Usage, latency and cost awareness
