Skip to content

PRACTITIONER-LED LEARNING / FIELD ENGINEERING

AI-native
training.

by LockedIn Labs

Build AI you can stand behind.

Practical AI learning through code, failure tests and receiving handoffs. Start with a useful workflow and progress toward field engineering.

Start a free lesson See the practice in 28 seconds

Free entry · Practical technical paths · FDE formation at the advanced end

THE WORK IS VISIBLE

Go beyond the
good-looking answer.

A result needs an explanation, a failure test and a receiving decision. These are actual lesson and team-practice screens from the platform.

28 seconds · Silent, with on-screen text · Actual synthetic teaching cases

Read the film transcript

AI-native training by LockedIn Labs. Build AI you can stand behind. Run the lab: inspect exact delivered bytes in the verification lesson. Challenge the result: a fresh delivery can repeat the same event. Hand over the work: swap builder and receiver and keep your own evidence. Your next step starts with a lab. Visit training.lockedinlabs.ai.

01

Run something real

Execute code, queries and local integration cases. Keep the command, version and observed result.

Inspect a runnable lab →
02

Change the condition

Try stale evidence, a repeated event or a missing dependency. Explain why the system should stop or proceed.

Challenge the evidence →
03

Receive someone else’s work

Attempt a concrete next action without the author taking over. Record assistance and unresolved responsibility.

Try the pod rehearsal →

A PATH WITH TECHNICAL DEPTH

Start where you are.
Build toward the field.

AI fluency is the beginning. The learning path continues into engineering systems, enterprise operating constraints and forward-deployed delivery.

  1. 01

    Understand and use AI

    Workflows, prompting, evidence and human decisions.

    Explore this stage
  2. 02

    Build AI systems

    Agents, retrieval, data, code, evaluation and integration.

    Explore this stage
  3. 03

    Deliver in the enterprise

    Identity, infrastructure, reliability, recovery and receiving teams.

    Explore this stage
  4. 04

    Progress toward FDE

    Discovery, client delivery, pod leadership and governed field evidence.

    Explore this stage

ONE METHOD / FROM PROBLEM TO OWNERSHIP

The DEPLOY Method.

Start with the work people need to do. Carry the evidence through the build, operating decisions and ownership transfer.

  1. D

    Diagnose

    Mission charter, stakeholder map, and observation boundary

  2. E

    Establish

    Outcome contract and wedge decision record

  3. P

    Prove

    Runnable wedge and provider-neutral behavioral contract

  4. L

    Land

    Production-shaped enterprise integration slice

  5. O

    Operate

    Production-readiness and staged-rollout record

  6. Y

    Yield

    Owner-accepted transfer package and owner-executed change/recovery record

Inspect the method and evidence standard →

USEFUL BEFORE YOU ENROLL

Start with a free
workshop.

Use the materials now, on your own or with a team. Each clinic brief gives a facilitator a practice plan, a changed condition and an individual receiving record.

0190 planned min

Start here · professionals and new builders

Your first AI workflow

Compare chat, a workflow and an agent. Change the available evidence and explain the simplest design that still works.

Keep: Your design note, changed-condition reasoning and next build question.

Browser lesson first; optional Python package lab has its own setup.

Open the free practice →Get the clinic brief ↓
02120 planned min

Technical practice · developers and engineers

Evidence before the answer

Run real search queries, challenge candidate limits and withhold a stale or revoked source before it becomes model context.

Keep: Original and changed reports, a source decision and a receiving explanation.

Python 3.11+ with SQLite FTS5; no model or paid API required.

Open the free practice →Get the clinic brief ↓
0390 planned min

Team practice · employee teams and FDE cohorts

Deliver as an engineering pod

Verify delivered bytes, then swap builder and receiver roles to investigate pending and repeated events.

Keep: Separate individual worksheets, observed receiving actions and the next accountable owner.

Two standard-library Python labs; reading, setup and breaks are separate.

Open the free practice →Get the clinic brief ↓

PRACTITIONERS / INSTRUCTORS / HOST TEAMS

Teach the work
you can demonstrate.

Help learners reason through a real engineering decision, reproduce a failure and explain a bounded next step. A strong instructor makes the learner more capable at the keyboard.

A pathway for practitioner instructors

  1. Bring a sanitized artifact and explain your role.
  2. Reproduce the selected lab and a changed condition.
  3. Deliver an observed teach-back and learner receiving task.
  4. Co-teach before a topic-scoped delivery decision.
Prepare a teach-back session →Get the instructor practice kit ↓Discuss teaching with LockedIn Labs →

Bring a practice session to your organization

Employer teams, community hosts and delivery organizations can discuss a bounded session: named faculty, exact materials, environment preparation and individual evidence.

Get the host and delivery planning brief ↓

Teaching and partner relationships require an agreed scope and confirmed people. These materials do not list a signed faculty roster or active delivery partners.

Explore enterprise training →

MAKE YOUR FIRST RESULT INSPECTABLE

Start with the work.

Choose a free lesson. Keep what you built, what changed and what another person could verify.

Start learning free →
What is available now?

Public lessons, downloadable labs and self-guided team practice are available now. Academy lessons use existing access. Staffed cohorts, instructor assignments, live dates, tuition and partner delivery are confirmed separately; course practice does not grant field qualification or client placement.