Developers aren't using AI effectively
Copilot and Cursor licenses are rolled out, but there's no shared playbook — output quality depends entirely on who happens to already be good at prompting.
For engineering teams
Engineers ship code with Copilot daily — few ship with real AI fluency.
Become AI-native.
A developer-first upskilling roadmap: prompting, agentic coding tools, MCP, AI agents, RAG, and fine-tuning — sequenced for engineering teams, not a grab-bag of workshops they forget in a week.
Live workshops or self-paced·Private cohorts available·Certifications on completion
Takes under 2 minutes · Work email required
The problemMost engineering teams have AI tools.
Most engineering teams have AI tools.
Few have AI fluency.
Inconsistent prompting
Every engineer prompts differently, with no shared vocabulary for context, constraints, or iteration, so results are unpredictable across the team.
Low adoption of coding assistants
Tools get installed but usage stalls at autocomplete — teams rarely reach agentic workflows, multi-file refactors, or test generation.
No understanding of AI agents, MCP, or RAG
Engineers can use a chat UI, but can't build with the architecture patterns that production AI features actually need.
Learning roadmapSix stages from
Six stages from
prompting to production.
Every stage builds on the last. Engineers can join at their level, but the sequence stays the same: prompting, coding tools, MCP, agents, RAG, then evaluation and fine-tuning.
01
Prompting
Structured prompting patterns and context engineering for Claude, ChatGPT, Gemini, and Copilot — the shared vocabulary every team needs first.
02
AI coding tools & agentic coding
Move past autocomplete: Cursor, Claude Code, and Copilot for multi-file refactors, test generation, and codebase-aware workflows.
03
MCP (Model Context Protocol)
Design and ship MCP servers that give agents safe, structured access to your internal tools, data, and APIs.
04
AI agents & AI skills
Build autonomous and semi-autonomous agents, tool-use patterns, reusable AI skills, and loop engineering for reliable multi-step execution.
05
RAG (retrieval-augmented generation)
Vector search, chunking, and retrieval pipelines for grounding models in your team's own documentation and codebase.
06
Fine-tuning & evaluation
Model selection, fine-tuning workflows, and evaluation rubrics so teams can judge AI-generated code safely before shipping.
Learning outcomesWhat your engineers can do
What your engineers can do
by the end.
Use Claude, ChatGPT, and Gemini effectivelyFoundation
Build AI agentsCore
Implement MCP serversCore
Build production RAG systemsAdvanced
Evaluate AI code safelyGovernance
Delivery formatsFive formats.
Five formats.
One delivery standard.
Live workshops
Facilitator-led sessions for a cohort of engineers — half-day, full-day, or multi-day.
Self-paced learning
On-demand courses your engineers can complete on their own schedule.
Cohort programs
Multi-week programs with mentor check-ins and measurable adoption milestones.
Hands-on labs
Practice projects that mirror real engineering work, not slide-deck demos.
Assessments & certifications
Graded assessments and a shareable certification on completion.
Who it's for
Every role on the engineering team.
Backend engineers
Frontend engineers
Full-stack developers
Engineering managers
For engineering leadersBuilt for CTOs, L&D, and HR
Built for CTOs, L&D, and HR
to run, not just attend.
Progress tracking
See who's completed what, at the individual and team level.
Team dashboards
Roll up adoption and completion metrics for engineering leadership.
Custom learning paths
Sequence courses and workshops around your team's actual stack.
Private cohorts
Run sessions for your engineers only, with no mixing into public cohorts.
Dedicated instructor
One instructor of record for your program, across every session.
Everything included
Courses, workshops, and the wider explainx.ai ecosystem.
Courses
On-demand courses covering prompting, agents, MCP, and RAG for engineers.
Workshops
Live, facilitator-led sessions for engineering teams and cohorts.
Bootcamps
20+ bootcamps delivered — intensive, project-based AI training.
Certifications
Graded assessments and shareable certifications for AI skills.
AI coding tools directory
Ranked directory of coding assistants, agents, and developer AI tools.
Latest developer AI news
A daily catch-up on what changed in AI — models, tools, and releases.
MCP servers directory
2,000+ MCP servers your team can wire into agents and coding tools.
Community discussions
Where engineers compare notes on prompting, agents, and tool adoption.
Practice projects, mock interviews, and community discussions are woven into every course and cohort, not sold as separate add-ons.
Free AI-readiness check
Evaluate your engineering team's AI maturity.
Answer five quick questions about how your engineers currently use AI. We'll email a short report to your work address with where the gaps are and what to fix first.
FAQ
Frequently asked questions.
How is this different from a generic AI training course?
This program is built for engineering teams specifically — the roadmap goes from prompting straight into MCP, agents, RAG, and fine-tuning, skipping the generic "what is AI" content most corporate training starts with.
Do you cover AI coding assistants like Cursor and Claude Code?
Yes. Agentic coding tools are a core module — engineers practice multi-file refactors, test generation, and codebase-aware workflows, not just autocomplete.
Can this run as a private cohort for our team only?
Yes. Private cohorts, custom learning paths, and a dedicated instructor are available for engineering teams — sessions are scoped to your stack, not a generic curriculum.
What levels of engineers is this for?
Backend, frontend, full-stack engineers, and engineering managers. The roadmap is sequenced so beginners start at prompting while experienced engineers can start at agents, MCP, or RAG.
How long does it take to become AI-native?
Most engineering teams see measurable adoption change within 2–4 weeks of starting the prompting and coding-tools modules, with agents, MCP, and RAG typically covered over 6–8 weeks depending on format.
Is there a certification at the end?
Yes. Assessments and certifications are available to verify individual skill completion, which engineering leaders can track through team dashboards.
Scope your program
Ready to make your engineering team AI-native?
Share your team size, stack, and timeline — most proposals same day.