Claude Code · Developer workflows · Instructor-led
Claude Code Training
Learn to use Claude Code inside a real development workflow: understand a repository, plan a small change, inspect the diff, run tests and decide what is safe to merge.
- Audience
- Developers and engineering teams
- Duration
- 16 hours for the full programme
What is the Claude Code Training course?
Claude Code Training is an instructor-led course for people who already write software. It covers how to use Claude Code to explore a repository, plan and implement changes, investigate defects, run tests and review a Git diff before merging. The standard full programme runs 16 hours; private teams can select modules, and the final agenda, exercises and tool access are agreed for each delivery. Participants practise keeping human review and permission boundaries in the loop.
Is this the right course for you or your team?
Who it is for
- Software developers, QA engineers and DevOps practitioners who work in a code repository.
- Technical leads evaluating a repeatable AI-assisted development workflow for their team.
- Independent developers who want guided practice rather than a product overview.
Situations it fits
- A team wants to understand a codebase before asking an agent to change it.
- Developers need a reviewable workflow for debugging, tests and pull requests.
- An organisation is considering project instructions, integrations or governance controls.
Prerequisites
- Working knowledge of at least one programming language; this is not a learn-to-code course.
- Basic Git and terminal familiarity help; no previous Claude Code experience is required.
- For hands-on work, participants need access to an approved account and a suitable practice repository.
When a different course fits better
Choose by the work you need to do, not by the product name alone.
- Claude AI TrainingFor general workplace writing, analysis and document workflows.
- Claude Enterprise TrainingFor organisation-wide deployment, administration and governance.
- Production AI EngineeringFor building and operating AI applications rather than using a coding assistant.
The business problems this course addresses
A coding agent can produce a plausible patch quickly. The harder skill is deciding what to ask, what changed and whether the result belongs in the codebase.
| Task | The usual problem | What participants practise |
|---|---|---|
| Joining an unfamiliar repository | A developer asks for a fix before understanding the architecture or test path. | Map the relevant files and constraints first; ask the agent to show its evidence. |
| Investigating a defect | The first suggested patch treats a symptom, not the cause. | Reproduce the issue, trace the failure and compare the patch with a failing test. |
| Shipping a change | A successful command is mistaken for a reviewed implementation. | Inspect the diff, run appropriate checks and retain human ownership of the merge. |
What participants will be able to do
The aim is a repeatable engineering workflow, not a promise that AI-generated code will be correct.
Orient in a repository
Identify entry points, dependencies, tests and project instructions before editing.
Write a bounded task
Give the agent a goal, constraints, acceptance criteria and a sensible stopping point.
Plan and implement incrementally
Separate investigation and plan from edits; keep changes small enough to review.
Debug against evidence
Use reproduction steps, logs and tests to assess a proposed fix.
Review the result
Read the diff, check tests and identify unsupported assumptions before a commit or pull request.
Apply safe boundaries
Choose permissions and approved data/tool access appropriate to the environment.
Evaluate extensions
Decide when project instructions, hooks, skills, subagents or MCP are useful—and when they add avoidable complexity.
Role-based use cases
How each team applies it to real work
These are practice scenarios, not claims that one prompt can complete an engineering task without review.
Application developer
- Starting point
- A small feature in an existing service.
- How Claude Code may help
- Locate the affected code, propose a plan and draft a focused patch.
- Engineering check
- Inspect the diff, run tests and check edge cases.
- Human decision
- Decide whether the behaviour and design fit the product.
QA & test engineer
- Starting point
- A regression with incomplete test coverage.
- How Claude Code may help
- Trace likely paths and suggest a failing test.
- Engineering check
- Verify that the test fails before the fix and passes for the right reason.
- Human decision
- Set the coverage and risk threshold for release.
Technical lead
- Starting point
- A team wants consistent project instructions.
- How Claude Code may help
- Draft repo-specific guidance and a review checklist.
- Engineering check
- Check that instructions match actual conventions and access policy.
- Human decision
- Approve the workflow and its owners.
What the training uses—and what is optional
Claude Code is the core tool. Extensions are selected only when they solve a real workflow need and the organisation permits them.
Claude Code workflow
Core
Repository exploration, task planning, file edits, commands and review.
Covers
- Orient in a codebase
- Make a bounded change
- Inspect and test the result
Depends on
- Approved Claude Code access
- Practice repository
What to verify
Confirm installation and plan access before the session; check the current product documentation.
Project instructions and permissions
Core
Project context and the boundary between assistance and authorisation.
Covers
- Use project instructions such as CLAUDE.md
- Review permission settings
- Keep a human review gate
Depends on
- Repository and organisation policy
What to verify
Permission prompts and defaults vary by mode and configuration. Do not assume every action needs the same approval.
Hooks, skills and subagents
Optional module
Reusable patterns and automation for teams that already have a stable base workflow.
Covers
- Identify a repeatable task
- Set checks and ownership
- Review extension behaviour
Depends on
- Selected modules
- Appropriate access and time
What to verify
Agree which extension is relevant; capability does not imply it is included in every delivery.
MCP integrations
Optional module
Connect approved tools and data sources through Model Context Protocol.
Covers
- Choose an approved server
- Understand tool exposure
- Review returned data and actions
Depends on
- Approved MCP server
- Security and data review
What to verify
Confirm the server, permissions, data flow and access before including an integration lab.
Technovids is an independent training provider, not affiliated with Anthropic. Product features and access can change; the written agenda confirms what is included.
16 hours · standard full programme
Detailed curriculum
This 17-topic outline is a menu, not a single-session promise. Core foundations can be combined with selected advanced modules. The written proposal confirms which topics, exercises and hours fit your group.
Core workflow
01 Claude Code fundamentals
- Where Claude Code works
- What the agent can read, change and run
- The developer’s review responsibility
Practical activityInspect a practice repository and identify the first safe task.
02 Explore an existing codebase
- Entry points and architecture
- Dependencies and conventions
- Evidence from files and tests
Practical activityCreate a short map of the code path relevant to one change.
03 Write effective coding instructions
- Goal and constraints
- Acceptance criteria
- Examples and stopping rules
Practical activityRewrite a vague request as a bounded engineering task.
04 Project context and CLAUDE.md
- Repository-specific instructions
- Useful versus noisy context
- Keeping instructions current
Practical activityDraft a small project instruction and review it against repository conventions.
05 Plan before implementation
- Investigation and alternatives
- Small change sets
- Checkpoints before edits
Practical activityReview an implementation plan before allowing a patch.
Quality and control
06 Debug with evidence
- Reproduction steps
- Logs and traces
- Root-cause hypotheses
Practical activityInvestigate a seeded defect and check a proposed fix.
07 Refactor without changing behaviour
- Refactor scope
- Compatibility checks
- Diff review
Practical activityImprove a small function while preserving observable behaviour.
08 Testing workflows
- Failing tests first
- Unit and integration checks
- Interpreting failures
Practical activityAdd a targeted regression test and verify its result.
09 Git and code review
- Working-tree awareness
- Commit and pull-request summaries
- Human review
Practical activityWrite a change summary that explains evidence and remaining risk.
10 Permissions and safe execution
- Permission modes
- Sensitive data and secrets
- Commands with side effects
Practical activityClassify proposed actions by risk and choose review gates.
Extensions and adoption
11Optional Hooks and automation
- Suitable hooks
- Checks and failure paths
- Ownership of automation
Practical activitySketch a hook for a repeatable check without bypassing review.
12Optional Skills and reusable workflows
- A repeatable task
- Inputs and expected output
- Maintenance
Practical activityDesign a reusable workflow for a common repository task.
13Optional MCP integrations
- Approved servers
- Tool and data boundaries
- Review of actions
Practical activityMap an integration’s permissions and test it only if access is approved.
14Optional Subagents and parallel tasks
- Task partitioning
- Conflicting edits
- Result review
Practical activityDecide whether a sample task benefits from delegation at all.
15Optional AI engineering workflows
- Using Claude Code around AI application code
- Evaluation and tests
- Domain-specific review
Practical activityReview a small AI application change against an explicit evaluation criterion.
16Optional Team adoption and governance
- Shared instructions
- Access and policy
- Review ownership
Practical activityDraft a team checklist for approved use and pull-request review.
17Optional Applied capstone
- Scoped repository task
- Test and diff evidence
- Human decision record
Practical activityApply the agreed workflow to a suitable exercise repository if included in the proposal.
Need a learning plan around your goals?
Share your experience, stack and goals. The proposal sets out the suitable modules.
Seven Practical Project Briefs for Real Repository Work
These seven project briefs use realistic repository workflows. Choose the projects that fit your stack, starting skills and agreed course scope; completing all seven is not a universal course requirement. Use a training repository or approved code, and review every generated change before accepting it.
Repository Architecture Analysis
Explore an unfamiliar repository end-to-end: understand its structure, data flow, key abstractions and dependencies using Claude Code codebase queries. Produce a written architecture summary and component map.
Bug Investigation and Fix
Reproduce a reported defect, trace it to its root cause, apply a minimal targeted fix and add a regression test. Practice the full debugging workflow from error message to reviewed commit.
API Feature Implementation
Plan and implement a new API endpoint or service method — from requirements to implementation plan to code changes to tests and pull-request description. Review every generated diff before accepting.
React or Next.js Component Improvement
Refactor a frontend component for readability, extract shared logic, improve prop typing and add snapshot or unit tests. Preserve all existing behavior throughout the refactor.
Python Service Refactor
Refactor a Python service module: extract reusable utilities, improve naming, reduce duplication, add type hints and cover critical paths with unit tests. Validate that all existing tests still pass.
Automated Test Suite Improvement
Audit an existing test suite for gaps and low-value tests. Generate meaningful additions, improve assertion quality, remove trivially-passing tests and verify coverage improves on the modules that matter.
MCP-Connected Developer Workflow
Configure an approved MCP server connection, build a workflow that pulls context from a connected tool (repository, documentation or issue tracker) and use it to complete a development task end-to-end.
Project memory, automation and team operating practice
Write useful project instructions
Build a concise CLAUDE.md covering repository layout, architecture constraints, naming conventions and actual build and test commands. Keep team instructions in version control and review them as the project changes. Instructions provide context; they are not an access-control boundary or a guarantee that every rule will be followed. In the exercise, test whether a new session can carry out a small repository task using the guidance, then remove ambiguity and stale instructions.
Use hooks deliberately
Hooks connect configured actions to lifecycle events. A practical exercise can introduce a formatting or validation check and inspect its success and failure paths. PreToolUse and PostToolUse occur at different points; a check after a change is not the same as preventing that change. Review hook code and permissions before running it, and keep CI checks and human review as independent controls. Do not use a hook to silently grant broader access.
Package repeatable work as skills
Choose a bounded workflow such as documentation updates, test preparation, onboarding or a review checklist. Define the inputs, steps and expected evidence, then try it against more than one example. Assign an owner to maintain the workflow; reusable instructions still need testing when the repository or tool changes.
Connect MCP tools with a clear boundary
Discuss approved connections to issue tracking, documentation or other development systems. For a database exercise, prefer a restricted read-only training environment. Review the server’s source, authentication, exposed tools and requested permissions; third-party content can contain malicious instructions. Keep credentials out of repository instructions, provide a revocation route and review what data each connection can access.
Make the workflow maintainable across a team
Agree repository ownership, approved tools, protected files, review gates and who can change shared settings. Evaluate usefulness through completed tasks, review effort, defects and rework, not generated lines of code. A retrospective should produce concrete updates to instructions, skills or checks. These exercises complement the existing seventeen-module curriculum and seven project briefs; selection follows the agreed engagement scope.
Product references: Claude Code project memory, hooks reference and MCP connections. For broader deployment decisions, compare Claude Enterprise training.
Exercises and practical outputs
Practice follows a deliberate loop that still works when the agent’s first answer is wrong.
How every module runs
Frame
Define the task, repository, constraints and acceptance criteria.
Investigate
Read relevant code and ask for a plan before a broad edit.
Change
Make a bounded patch and keep track of commands and files changed.
Verify
Run suitable checks, inspect the diff and decide whether to revise or merge.
Codebase exploration Exercise 1: Repository architecture map
Trace one feature through code and tests.
Practice output A short, cited map of the relevant files and assumptions.
Debugging and testing Exercise 2: Bug investigation
Reproduce a seeded defect and assess a candidate patch.
Practice output A failing test, proposed fix and review notes.
Planning, implementation and Git Exercise 3: Small feature change
Plan and implement a bounded API or UI change in a practice repository.
Practice output A diff, test results and pull-request summary.
Optional MCP module Exercise 4: Integration design
For an approved use case, assess whether an MCP server is needed.
Practice output An access and data-flow checklist; hands-on connection only when authorised.
Exercises use representative, non-sensitive examples. Outputs are produced during the workshop for practice and review; anything participants keep is their own working material.
Verification, data handling and human judgement
The useful question is not “Can the agent do this?” but “What may it access, what did it change, and who will approve the outcome?”
Technovids is independent of Anthropic. Product behaviour varies by version, plan and configuration; verify current settings against official documentation.
Set the permission boundary
Choose a permission mode that fits the environment, and review commands or edits with material side effects. Do not assume that every action always prompts for approval.
Protect repository data
Use a suitable practice repository and approved account. Keep secrets, private code and customer data inside your organisation’s policy and access controls.
Review before merge
Inspect changed files, test results, dependency changes and security implications. Passing tests are evidence, not proof of correctness.
Review integrations separately
An MCP server can expose tools or data beyond the repository. Confirm its provenance, scope and permissions before enabling it.
How the course is tailored and delivered
The agenda is built around a suitable practice codebase and the roles in the room, with the final scope agreed before delivery.
From first conversation to evaluation
Share your context
Tell us the language, repository type, participant level and policy constraints.
Select the modules
Choose foundational topics and only the extensions relevant to your workflow.
Confirm the lab
Agree approved accounts, access, sample code and review rules in the written proposal.
Delivery formats
Private team workshop
Best for: Engineering teams using a shared repository or policy
Live instruction and exercises scoped to the team; duration, venue and group size are confirmed in the proposal.
Individual developer enquiry
Best for: A developer seeking guided practice
Tell us your experience and preferred topics. We will confirm whether a suitable delivery option is available.
Training sessions
Photos show Technovids training across programmes. They are not presented as images of this Claude Code course.
Photo 1 of 10: Hands-on session 
Photo 2 of 10: Group photo 
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Photo 4 of 10: Group photo 
Photo 5 of 10: Classroom session 
Photo 6 of 10: Group photo 
Photo 7 of 10: Computer-lab session 
Photo 8 of 10: Group photo 
Photo 9 of 10: Classroom session 
Photo 10 of 10: Group photo
Course details at a glance
The published course parameters on one page. Final scope, dates and fees are confirmed in the written proposal.
Discuss your training goals| Course | Claude Code Training |
|---|---|
| Provider | Technovids Consulting · independent of Anthropic |
| Audience | Developers, QA/DevOps practitioners and technical leads with programming experience |
| Prerequisites | Programming required; Git and terminal familiarity helpful; no prior Claude Code use required |
| Product | Claude Code; approved account and practice repository required for hands-on work |
| Duration | 16 hours for the full programme; a private team can select fewer modules, confirmed in the written proposal |
| Delivery | Live online; private team format or onsite delivery subject to agreement |
| Advanced topics | Hooks, skills, MCP, subagents and capstone only when selected and access allows |
| Fees, dates and certificate | Confirmed before booking; no public schedule or certificate promise on this page |
People who may lead the training
The Technovids trainer panel brings practical AI, software and learning-design experience. The assigned instructor depends on the agreed scope and availability.
Pankaj Rana
Co-founder & Lead Instructor, Technovids Consulting
Pankaj is a co-founder and the lead instructor at Technovids Consulting. His programme work spans practical AI adoption, AI engineering, evaluation, responsible production AI, data analytics and corporate learning design, with an emphasis on hands-on exercises connected to workplace tasks.
Expertise:
- Practical AI adoption
- Prompting and evaluation
- Responsible AI
- Corporate learning design
Junaid Khateeb
AI & Machine Learning Trainer, Strategist and Consultant
Junaid is an AI and machine-learning trainer, strategist and consultant with more than 22 years of training experience. His work combines enterprise AI education, business-centred adoption, responsible AI practices and applied solution design, helping professional audiences connect AI concepts to real organisational needs.
M.E. in Computer Engineering, specialising in Artificial Intelligence · author of programming books on Python, Java and C++
Expertise:
- Enterprise AI and ML training
- AI strategy and adoption
- Responsible AI
- Applied AI solution design
Pradeep Varadarajan
Technical Trainer & Curriculum Architect
Pradeep is a technical trainer and curriculum architect with more than 20 years of experience across enterprise technology, cloud infrastructure and advanced AI systems. He designs practitioner-focused programmes, labs and assessments covering AI, LLM systems, Python and machine learning, translating complex technical subjects into structured learning experiences.
Bachelor of Engineering in Electrical Engineering · instructor-led, virtual and blended delivery
Expertise:
- AI and LLM systems
- Curriculum architecture
- Python and machine learning
- Hands-on lab design
A listed trainer is not a confirmed assignment for a particular delivery.
Updated
Client and participant perspectives
These are Technovids relationships and participant comments across training programmes, not endorsements of this specific Claude Code course.
Organisations we have delivered training for
Practical training programmes delivered for learners, professionals and teams across AI, data, cloud, productivity and professional skills.
Participant experiences across workplace roles
What professionals found useful, practical and relevant to their day-to-day work.
- Gauraw DeshmukhData Engineer · Capgemini
Technovids AI training was very practical and well structured. The sessions helped me understand how AI concepts can be applied in real data engineering and business workflows, instead of only learning theory. The trainer explained the topics clearly with relevant examples, which made the learning experience valuable and easy to follow.
- PriyanshaSoftware Developer
The AI training from Technovids helped me gain a better understanding of Generative AI, prompt engineering, and practical AI use cases for software development. The course was clear, interactive, and focused on real-world applications. It gave me confidence to explore AI tools more effectively in my work.
- Sidda RajuData Engineer
Technovids delivered the AI training in a very practical and industry-relevant way. As a data engineer, I found the examples and hands-on discussions useful for understanding how AI can improve data workflows, automation, and decision-making. The sessions were well planned and easy to understand.
- Akshat KabraData Engineer
The AI training program by Technovids was insightful and professionally delivered. It covered important AI concepts in a simple and practical manner, with examples that connected well with data and analytics use cases. The sessions helped me build a stronger foundation in applying AI in real business scenarios.
- MangeshSoftware Engineer · Siemens
Technovids conducted the AI training in a very effective and engaging manner. The sessions were practical, easy to follow, and focused on how AI can be used in real software engineering and business workflows. It was a valuable program for understanding the current direction of AI adoption in the industry.
- ReshuSoftware Developer
Technovids AI training was a very useful learning experience. The course explained AI concepts in a practical way and showed how developers can use AI tools to improve productivity, problem-solving, and application development. The trainer's approach was simple, clear, and focused on industry use cases.
Common questions before planning this course
Answers about the published course. The written proposal records any agreed customisation, delivery details and commercials.
Who is Claude Code Training for?
It is for people who already write or review code: software developers, QA and DevOps practitioners, and technical leads. Basic programming is required; prior Claude Code experience is not.
Is this a fixed 17-module course?
No. The standard full programme runs 16 hours, and the 17 topics are a published scope menu. A private team can select fewer modules; the written proposal confirms the modules, exercises, duration and delivery format that fit your group.
How long is Claude Code Training?
The standard full programme runs 16 hours, live online. A private team can choose a shorter set of modules, and the written proposal confirms the final duration and schedule.
Do participants need a Claude Code account?
Hands-on work requires an approved account, suitable access and a practice repository. Please confirm your organisation’s plan and security policy before delivery; the proposal will set out the lab requirements.
Does Claude Code ask permission before every command and edit?
Not necessarily. Behaviour depends on the configured permission mode and environment. The training teaches participants to inspect settings and apply human review to actions with consequences.
Are MCP, hooks and subagents included?
They are optional extension topics, not automatic inclusions. We add them only when the goals, time, product access and policy make them useful.
Can the workshop use our own repository?
Possibly, if access, confidentiality and preparation are agreed in advance. A safe practice repository is an alternative. Do not send private code or credentials through the enquiry form.
What happens after the training enquiry?
We discuss your audience, stack, use cases and constraints, then confirm the proposed agenda, format, timing and fees in writing. Individual delivery availability is also confirmed at that stage.
Is this the same as Claude AI Training?
No. Claude AI Training focuses on general workplace research, writing, analysis and document workflows. This course is for developers using Claude Code in a repository, with code, tests, Git and review.
Plan your next step with this course
Tell us whether you are enquiring for yourself or a team, which development tasks matter and what access you have. We will confirm suitable delivery options and commercial details in writing.