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Repository work with verification
Claude Code training
Claude Code training teaches developers to use Anthropic’s coding tool within a controlled repository workflow. predeploy scopes exercises around task definition, repository context, permissions, code review and verification. This is a technical course for people responsible for software changes. It differs from business-user Claude training and does not imply permission to merge, deploy, expose credentials or replace the team’s engineering review process.
Leave with a verified repository exercise and a working guide for scoping, reviewing and checking assisted code changes.
Anthropic provides developer learning resources covering Claude Code and the Claude API; no predeploy accreditation is implied. Anthropic: build with Claude ↗
NCSC guidance covers design, development, deployment and operation. NCSC: secure AI system development ↗
New to the topic? Read the background on artificial intelligence.
Repository work with verification
what should a developer course teach first?
Start with the repository’s instructions, a bounded task and the checks needed to accept the change.
Anthropic’s developer learning resources cover Claude Code and building with Claude. Use those for product foundations. Our course applies practice to the team’s approved development workflow. Claude training covers business-assistant tasks instead. Before an exercise starts, identify the relevant repository instructions, working state and expected output so learners do not confuse a successful tool invocation with a correct and reviewable software change.
Non-developers in the same organisation are covered by Claude Enterprise training.
Read repository instructions.
Define a bounded task.
Agree acceptance checks.
Repository work with verification
which repository exercise is useful?
Use a small change whose behaviour and failure cases the learner can explain.
An illustrative exercise might correct a validation bug in a prepared application. The learner should reproduce the issue, identify the relevant code, inspect the proposed diff and run a meaningful check. Avoid an exercise that merely asks the tool to generate a large application the learner cannot review. AI training for project managers addresses project records; this course requires technical judgement about code, tests and the actual behaviour affected.
Repository work with verification
how should permissions and information be handled?
Use an approved environment and make action boundaries explicit before running tools.
Choose a repository and practice material appropriate for the account and environment. Keep credentials and unnecessary customer information out of examples. The NCSC secure development guidance provides lifecycle context. The course should distinguish reading, editing, executing commands and contacting external systems. A developer’s request for assistance does not automatically authorise a deployment or a change to production data.
| Check | What to establish |
|---|---|
| 1 | Use approved material. |
| 2 | Limit available actions. |
| 3 | Keep deployment approval separate. |
Repository work with verification
how should code changes be verified?
Require the developer to explain the diff and present evidence for the behaviour changed.
A passing command alone may not test the relevant failure. Inspect whether the check would fail for the original problem and whether the implementation affects adjacent behaviour. LLM evaluation covers a broader evaluation mindset for AI workflows, while AI technical consultancy can investigate architecture questions. The exercise should preserve the task, patch and verification evidence so another engineer can review the result without needing to reconstruct a conversation.
Repository work with verification
how can the team adopt the working method?
Keep a concise repository task guide and assess a fresh change after supported practice.
Ask a learner to complete a comparable new task and explain uncertainty, failures and verification limits. Bespoke AI training can adapt the course to your engineering stack and starting skills. AI training for managers addresses supervisory review where needed. The proposal should state preparation, delivery and follow-up; no vendor certification, fixed productivity improvement or replacement of professional code review is promised.
Assess a fresh task.
Keep the repository guide.
Record verification limits.
Make the decision concrete
What does the current task cost in time?
Choose a task connected to claude code training and enter its current workload. This gives you a baseline to discuss before buying training or commissioning a build.
A baseline is useful even if you decide to keep the process manual. Measure the work that matters, then compare quality and review effort as well as time.
Assess the workflow before changing it, or plan business AI training if the gap is how people use existing tools.
Your current workload
Estimate how much time the existing task takes before discussing claude code training.
16 hours per week
People × minutes × weekly tasks ÷ 60. A workload baseline from your inputs, not a prediction of AI time savings. Include review and support time when comparing a proposed workflow.
A useful AI investment leaves your team able to do the work and judge the result.
Continue with the right question
Where should you go next?
Use the overview for context, then follow the pages that match your delivery decision.
See our forward deployed engineers service or read the forward deployed engineering overview.
FAQ
Questions, answered
Evidence and further reading
Which sources support the guidance?
Source links support the specific background described here. The practical checklists and illustrative examples are predeploy’s guidance.
Anthropic provides developer learning resources covering Claude Code and the Claude API; no predeploy accreditation is implied. Anthropic: build with Claude ↗
NCSC guidance covers design, development, deployment and operation. NCSC: secure AI system development ↗
Background reading on the broader field of artificial intelligence. Wikipedia: artificial intelligence ↗
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