Assessment and path
A capability assessment and a learning sequence whose prerequisites connect to the chosen training track.
Employee enablement, engineering and field deliveryAI-native Training
Built for the field.
Develop practical AI capability from your first workflow to systems engineering, enterprise delivery and FDE pod leadership.
Explore the interactive exampleDEVELOP / AI-native Training
A pathway for the whole team
Choose the depth.
Practise the work.
Production engineering shapes the lesson: access boundaries, careful data handling, evaluation, recovery and accountable ownership. Each person keeps work they can explain.
Employees, managers and leaders
AI fluency at work
Redesign a workflow, check an AI result and make the human decision explicit.
Developers and engineering teams
AI systems engineering
Build with agents, retrieval and integrations. Inspect evaluations and test the difficult failure cases.
Forward-deployed engineers and delivery leads
FDE and pod delivery
Connect discovery, integration and operations. Practise leading a pod and transferring work to a receiving team.
For enterprise teams
Build a cohort around a real responsibility.
Agree the roles, starting task, practice time and review arrangements. Give the receiving team a concrete next action and retain each participant’s contribution, assistance and correction.
Plan enterprise training Get the team planning briefFor practitioner instructors
Teach through work a learner can reproduce.
Bring a sanitized artifact, run the lab and teach through a changed condition. Observed teaching, learner receiving tasks and scoped co-teaching connect preparation to the delivery decision.
Inspect the instructor practice kit Discuss teaching with LockedIn LabsThe DEPLOY Method carries the learning from problem definition through engineering decisions and ownership transfer. Explore free workshop materials or watch the product film.
Learning through practice
Give the learner a reason for the next exercise.
The training platform connects a stated learning goal to authored curriculum and the learner's recorded work. Each module asks for an artifact, and its published rubric gives the review a concrete basis. A path can change as assessment and review evidence changes, with the reason for each next step available to the learner.
A path built around the workExample
Learning pathPracticeSkills evidence
01Assess
02Learn
03Practice
04Review
Private evidencePractice artifact
Artifact + revisionKeep submissions + revisions
Published criteriaIndependent review requirements
Learning path
Choose the sequence from the learner's evidence.
The adaptive path joins the learner's goal, assessment, progress and reviewed artifacts with the authored prerequisite graph. It explains why a module is on the path and what work should come next.
- Role and capability goals connect to authored tracks.
- Prerequisites and review needs shape the order of the work.
- Path revisions preserve the basis for a changed learning sequence.
Interactive example
Shape the next practice step.
Use a learning goal, submitted work and review method to see the evidence behind the next exercise.
Synthetic inputsComputed in this browser
AI-native TrainingInteractive workspace
Choose a starting pointThen change any input to explore the difference.
01 / Set the context
Example learning path · evaluation design
The goal is to design a defensible agent evaluation.
The exercise requires criteria, paired cases and a reasoned interpretation.
Practice feedback and assessed work retain their own standing.
Authored examples for exploring the workflow. Your choices update only this demonstration.
02 / Inspect the basisLearning-path review record
One review item remains
2 of 3 example checks met. Follow the evidence questions before carrying the example forward.
2 / 3 checks met
Review checks2 / 3 met
Starting assessmentMet
The example learner's input-design prerequisite has a reviewed artifact and feedback.Evaluation artifactMet
The example artifact states acceptance criteria, includes paired cases and explains the observed differences.Review methodReview
The artifact has a heuristic practice check; its method remains personal practice.
Next review step
Use the practice feedback to revise the work, then request the exercise's required assessor review.
Inspect the selected evidence
- Starting assessment
- The example learner's input-design prerequisite has a reviewed artifact and feedback.
- Evaluation artifact
- The example artifact states acceptance criteria, includes paired cases and explains the observed differences.
- Review method
- The artifact has a heuristic practice check; its method remains personal practice.
Illustrative review record · human decisions and subsequent actions keep their own standing.
The product
Develop practical capability.
AI-native training by LockedIn Labs brings production engineering discipline into employee development. Start with practical AI workflows, progress into agents, retrieval, integration and evaluation, and develop toward forward-deployed engineering and pod delivery. Runnable labs and individual evidence connect the lesson to work another person can inspect and receive.
Enterprise AI journeyDEVELOP
Inside AI-native Training
Build capability through work a reviewer can inspect.
Develop forward-deployed engineering skills through an assessed learning path, authored lessons and practical exercises. Labs, simulations and benchmark work create a basis for reviewing both technical output and the decisions behind it.
- Forward-deployed engineers
- Learning leaders
- Technical reviewers
How the work moves
Assess the starting pointCapability assessment
Use capability exercises and defined assessment criteria to establish the learner's starting point and the skills their path needs to develop.
Follow a learning pathStructured development path
Move through authored tracks and prerequisite-linked modules. Connect the learning sequence to the technical and delivery work the learner is preparing to perform.
Practice and preserve the workPractice artifacts and revisions
Work through provisioned labs, simulations and benchmarks. Keep submitted artifacts and revisions in the learner's private evidence record.
Review against the standardEvidence for skills review
Inspect the work against published evidence requirements. Qualification standards connect technical implementation, operating evidence and independent review to the capability being claimed.
What the work produces.
Private evidence portfolio
Learner-owned submissions, revisions and review history attached to the work that produced them.
Qualification requirements
Published criteria for demonstrating engineering capability, including the evidence and independent review required for production-experience claims.
Lessons, labs and review services follow their enrollment and activation requirements. A training record supports qualification review; client work requires separate authority.
In practice · illustrative scenario
Develop the evaluation skill behind an agent workflow.
An engineer can build an agent prototype but needs stronger evaluation practice. Their goal and starting assessment point toward a path that includes evidence design and operating judgment.
- 01
Choose the practice
Inspect why the relevant module is on the path and read the artifact requirements before beginning the exercise.
- 02
Submit the work
Prepare the required evaluation artifact and review feedback against the published criteria, then revise the missing evidence.
- 03
Revisit the path
Use the recorded assessment and review signals to inspect the next exercise and preserve the work for applicable qualification review.
The useful outcome
The illustrative outcome is a clearer development path supported by an inspectable artifact and review history, rather than course attendance alone.
A closer look
Know what you can do with AI-native Training.
What makes a path adaptive?
Its sequence is derived from the stated goal, assessment, prerequisites, progress and reviewed-work signals. Each step includes a reason and next action. The planning layer can recompute the path as that evidence changes; it does not mark the work complete by itself.
How are practice and verified work distinguished?
The record retains the review method and result. Personal practice and mentor snapshots support learning, while completion and qualification follow their evidence and review requirements. A heuristic practice check cannot issue a verified credential.
How can a team use the training platform?
Through a programme with appropriate enrollment, learning paths and assessor or organization access. Cohort views and recorded artifacts support development review. Staffed support, live mentor connections and field qualification follow the programme's activation and review arrangements.
