Claude Code Training
Learn to use Claude Code as an AI-assisted development environment for exploring repositories, planning changes, editing code, running tests, debugging issues and working with your existing engineering tools.
What is Claude Code training?
Claude Code training teaches developers how to use Claude's coding agent to understand repositories, plan changes, edit files, run commands, debug issues, write tests, review changes and connect approved development tools through controlled workflows.
Who Should Join This Training?
Designed for developers, engineers and technical leads ready to integrate Claude Code into real development workflows. Basic programming knowledge recommended.
Software Developers
Learn to explore unfamiliar codebases, implement changes, debug issues and review generated code safely.
Perfect fitFull-Stack Developers
Accelerate feature implementation, API integration, component work and cross-layer debugging across your full stack.
Perfect fitFrontend Developers
Speed up component creation, refactoring, CSS/JS debugging and test writing with AI-assisted workflows.
Perfect fitBackend Developers
Use Claude Code for service architecture exploration, API implementation, debugging business logic and improving test coverage.
Perfect fitPython Developers
Explore Python repositories, debug data pipelines, refactor services and improve test suites with controlled AI assistance.
Perfect fitJavaScript / TypeScript Developers
Navigate complex JS/TS codebases, resolve type errors, refactor modules and implement features across frontend and backend.
Perfect fitDevOps Engineers
Use Claude Code for infrastructure-as-code exploration, pipeline scripts, debugging configuration and documentation workflows.
Strong fitQA and Automation Engineers
Generate test suites, improve coverage, review test quality and build repeatable testing workflows with Claude Code assistance.
Strong fitAI Engineers
Use Claude Code for RAG pipelines, agent workflows, prompt and evaluation code, LLMOps tooling and API integrations.
Perfect fitTechnical Leads
Understand how to govern Claude Code use across your team — shared settings, coding policies, review requirements and team instructions.
Strong fitEngineering Managers
Learn how to set up AI-assisted development practices, review policies, onboarding guides and governance frameworks for your engineering team.
Good fitFounders Building Products
Move faster on your codebase, understand unfamiliar code and implement features safely with Claude Code as your development partner.
Strong fitPrerequisites
- Basic programming knowledge recommended — this is not a learn-to-code programme
- Command-line familiarity helpful — you will use a terminal throughout
- Git basics helpful — branching, commits, diff review
- No prior Claude Code experience required — the programme starts from setup
What You Will Learn
Practical outcomes you can apply to your development workflow on day one — not slides-and-theory topics.
Install and configure Claude Code for safe, controlled use in your projects
Explore unfamiliar repositories and ask precise codebase questions
Create implementation plans before making multi-file edits
Make controlled, reviewable code changes across files and directories
Debug errors by reading logs, tracing execution and applying targeted fixes
Refactor code for quality while preserving behavior and adding regression tests
Write and run unit, integration and test-suite workflows
Review generated diffs and prepare commits and pull requests responsibly
Use project memory with CLAUDE.md to align Claude Code with your team's standards
Configure permission boundaries and protect secrets and sensitive files
Use hooks for validation, formatting, linting and logging
Build reusable skills and custom commands for repeatable team workflows
Connect approved development tools through MCP integrations
Coordinate scoped tasks with subagents while avoiding conflicting edits
Apply Claude Code to AI engineering workflows — RAG, agents and LLMOps tooling
Decide when human review is mandatory and build review habits into your process
Establish team adoption practices, shared policies and engineering governance
17 Modules. Real Engineering Workflows.
Every module is structured around practical developer outcomes — what you can do with Claude Code in a real repository, not abstract theory.
Claude Code Fundamentals
- What agentic coding means and how it differs from chat-based coding help
- Claude Code architecture at a practical level: terminal, IDE and MCP layers
- Supported working environments: terminals, VS Code, JetBrains and CLI usage
- Installation, authentication concepts and initial project setup
- Understanding the permission model before running any session
- First repository session: exploring a real codebase from scratch
Exploring an Existing Codebase
- Repository structure discovery and architecture understanding
- Locating features, tracing data flow and finding relevant files
- Understanding dependencies and configuration files
- Asking scoped, precise codebase questions instead of broad questions
- Validating Claude's understanding of the codebase before editing
- Identifying what Claude Code does not yet know and filling the gap
Effective Instructions for Coding Tasks
- Defining the task clearly: scope, constraints and expected output
- Setting acceptance criteria before Claude writes a single line of code
- Planning before editing: why you must understand the change before applying it
- Giving examples that guide the output format and style
- Avoiding vague instructions that lead to unpredictable repository-wide changes
- Iterative refinement: when to accept, reject or rephrase
Project Memory and CLAUDE.md
- What project memory is and how CLAUDE.md shapes every Claude Code session
- Writing effective repository conventions, coding standards and testing commands
- Including architecture notes and data-flow descriptions Claude needs to be useful
- Writing shared team instructions that keep all developers and Claude aligned
- Keeping CLAUDE.md concise: what to include and what to leave out
- Reviewing and maintaining project memory as the codebase evolves
Planning and Implementation Workflows
- Requirement clarification and impact analysis before any edit
- Creating explicit implementation plans: what files, what changes, in what order
- Identifying affected files and potential knock-on effects across modules
- Making incremental changes and reviewing each diff before continuing
- Validating assumptions throughout implementation — not just at the end
- Avoiding uncontrolled repository-wide edits that overwrite unrelated code
Debugging with Claude Code
- Reading and interpreting error messages before asking Claude to fix them
- Reproducing defects reliably and sharing the reproduction steps with Claude
- Investigating logs, stack traces and runtime behavior systematically
- Root-cause analysis: tracing the real source, not just the visible symptom
- Testing hypotheses and applying minimal, targeted fixes
- Preventing regressions by adding tests alongside the fix
Refactoring and Code Quality
- Identifying code smells and explaining them clearly to Claude Code
- Extracting reusable logic, improving naming and reducing duplication
- Understanding dependency impact before refactoring shared modules
- Preserving external behavior while improving internal structure
- Adding regression tests before and after a refactor
- Reviewing generated refactors carefully — not accepting without reading
Testing Workflows
- Generating unit tests for functions, classes and modules
- Creating integration tests for service boundaries and API interactions
- Test failure diagnosis and root-cause identification
- Improving coverage thoughtfully without chasing meaningless percentages
- Avoiding generated tests that pass trivially without exercising real behavior
- Running test suites and reviewing test quality as part of every PR workflow
Git and Code-Review Workflows
- Reading repository status and working safely with branches
- Reviewing diffs carefully before staging and committing
- Writing meaningful, accurate commit messages for AI-generated changes
- Preparing pull requests with clear descriptions and testing notes
- Handling issue-to-code workflows from ticket to reviewed PR
- Human approval before any merge — not deferring review to automation
Permissions, Security and Safe Execution
- Understanding permission prompts: what they are and why they exist
- Setting file and command boundaries appropriate to your project
- Protecting secrets, environment files and credentials from accidental exposure
- Recognising dangerous commands and refusing or adjusting them before approval
- Prompt injection awareness: how malicious repository content could influence Claude
- Least-privilege workflows: granting only the access each task requires
Hooks and Automation
- What hooks are and how they trigger on Claude Code actions
- Suitable hook use cases: validation, formatting, linting, security checks, logging
- Writing validation hooks that run before Claude applies changes
- Formatting and linting hooks that ensure generated code meets team standards
- Avoiding unsafe hook logic: hooks should not grant elevated permissions
- Maintaining team-shared hooks alongside CLAUDE.md for consistent enforcement
Skills, Commands and Reusable Workflows
- What skills are and how they package repeatable instructions for Claude Code
- Creating reusable skills for documentation, testing, review and onboarding tasks
- Custom commands and slash commands where currently supported
- Building team workflow templates for common engineering tasks
- Ownership and maintenance: who updates skills and how changes are reviewed
- Composing skills with hooks for consistent, automated developer workflows
MCP Integrations
- What MCP adds to Claude Code: connecting approved development tools
- Configuring MCP servers for repositories, issue trackers and documentation
- Database and data-source connections with appropriate access controls
- Observability and logging tool integrations for production context
- Authentication patterns and permission boundaries for MCP connections
- Trusting and monitoring MCP servers: review, audit and access revocation
Subagents and Parallel Workflows
- What subagents are and when to use them for scoped parallel tasks
- Separating research, implementation and review into independent agents
- Context boundaries: each subagent should have a well-defined, narrow scope
- Coordinating subagent results without conflicting edits to shared files
- Validating subagent results before merging output into the main context
- When not to use subagents: tasks that need a single coherent session context
Claude Code for AI Engineering
- Exploring RAG and retrieval pipeline repositories with Claude Code
- Working with agent workflow code: understanding task graphs and tool calls
- Editing prompt templates, evaluation scripts and LLM application code
- Debugging data pipelines and AI inference integrations
- Testing LLM applications: evaluation-driven development patterns
- LLMOps and observability awareness in AI engineering repositories
Team Adoption and Engineering Governance
- Shared settings and coding policies across an engineering organization
- Defining approved MCP servers and tool connections for the team
- Repository safeguards: what Claude Code should never edit or execute
- AI-generated code review policies and mandatory human review gates
- Team CLAUDE.md instructions: who owns them and how they are updated
- Measuring Claude Code usefulness and running retrospective improvement cycles
Capstone Project
- Explore an unfamiliar repository: document its architecture and data flow
- Investigate and fix a real defect with root-cause analysis and a regression test
- Implement a small scoped feature with a complete implementation plan and diff review
- Add or significantly improve a test suite for an existing module
- Prepare a pull request with a meaningful description and testing evidence
- Document verification steps and rollback procedure for the changes made
Upcoming Claude Code Training Batches
Live instructor-led sessions for developers and engineering teams. Contact us if no batch is listed — corporate batches available on request.
Next Claude Code Training batch is being scheduled.
Contact our training team for the next available batch date, format and corporate programme details.
Ask for Next Batch7 Practical Projects You Complete on Real Code
Every project uses real repository workflows — not toy examples. You will 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.
Claude Code Development Workflow
Every Claude Code task follows this seven-step workflow. You review and control every step — Claude Code does not act without your approval.
Permission Boundaries and Safe Execution
Claude Code uses a permission model that requires your approval for file edits and command execution. Module 10 covers how to configure these boundaries so Claude Code operates within your approved scope.
Claude Code vs Claude Chat: What Each One Does
Claude Chat supports general conversation and coding help. Claude Code is designed for deeper repository and development-tool workflows. This course covers Claude Code — the Claude AI Training Course covers the full Claude platform including both.
| Dimension | Claude Code | Claude Chat |
|---|---|---|
| Primary purpose | Agentic coding: explore repositories, edit files, run commands, debug, test and prepare commits | General conversation, coding explanations, document analysis and workplace productivity |
| Environment | Terminal, VS Code, JetBrains and MCP-connected tools — operates inside your development environment | Browser-based chat interface or API — operates outside your development environment |
| Codebase access | Reads your actual repository files, directory structure and project context directly | Sees only what you paste into the conversation; no direct file access |
| File changes | Can edit files directly with diff review and permission controls | Generates code in the conversation; you paste it into your editor manually |
| Command execution | Can run shell commands, tests and build scripts — with your approval at each step | Cannot execute commands; describes what to run, not runs it |
| Development workflow | Works inside your Git repo, reads history, branches, status and can prepare commits | Has no access to your Git repo or project history |
| Review requirements | Every file edit and command requires your review and approval — human-in-the-loop by design | You review and apply code suggestions manually in your editor |
| Target user | Developers and engineers who want Claude to work inside their development environment | Any professional — developers, analysts, managers, writers, HR, finance, operations |
How Claude Code Fits Into an AI-Assisted Development Stack
Claude Code operates in your terminal and IDE, reading actual repository files and running commands with your approval. This makes it distinct from browser-based AI chat tools that only see what you paste into the conversation. Claude Code brings context-aware, repository-native AI assistance into your existing engineering environment without replacing your other tools.
The goal of this training is not to replace your development judgment — it is to extend it. Claude Code handles exploration, boilerplate, test generation and refactoring suggestions while you retain control over every edit, every command and every commit. You decide what reaches version control; Claude Code helps you get there faster.
Terminal and IDE Workflows
Claude Code operates inside your existing environment — your terminal, your editor, your project files. It does not require a separate interface or copy-paste workflow.
Repository Context
Claude Code reads your actual codebase — not just snippets. It understands your project's structure, conventions and history as context for every task.
Developer Control
Every file edit and command execution requires your approval. Permission boundaries, protected files and hooks let you configure exactly what Claude Code can and cannot do.
Individual Developers and Engineering Teams
Individual Developers
- Personal project instructions in CLAUDE.md for your own conventions
- Local permission boundaries for your machine and repository
- Learning workflows: exploration, debugging, refactoring, testing
- Building review habits for every AI-generated change before committing
- Using hooks for personal formatting and linting preferences
- Connecting tools via MCP for your individual development context
Engineering Teams
- Shared CLAUDE.md with team coding standards and architecture notes
- Approved MCP servers documented and maintained centrally
- Shared settings and tool connection policies for the team
- Review policies: what requires human approval before merge
- Governance framework: who owns the CLAUDE.md, hooks and skills
- Onboarding new developers to consistent Claude Code practices
For broader organizational Claude adoption beyond developer teams, see Claude Code training for engineering teams in our Claude Enterprise programme.
Training Format and How to Enrol
- Instructor-led live training — not pre-recorded video
- Developer-focused exercises on real repository workflows
- Weekday and weekend options based on batch availability
- Individual developer and corporate engineering-team formats
- Custom engineering-team workshops with agreed scope
- Online delivery; mentoring or review sessions where included in the agreed programme
- 7 practical projects completed on real or sample codebases
- Post-training workflow reference kit
Contact Technovids for the current syllabus, batch format, duration and corporate-team proposal.
For AI engineering teams: Pair this course with the Production AI Engineering programme for full-stack AI engineering capability — from Claude Code workflows to RAG, agents and MCP systems.
Pricing
On Request
Contact us for current batch fee and corporate training pricing. Fees may vary for public batches, 1:1 mentoring and corporate engineering team formats.
Book Free Course Consultation →WhatsApp Us NowReady to learn Claude Code for real development work?
Tell us about your role, team and technical background. We will recommend the right format and share the current syllabus.
Frequently Asked Questions
What is Claude Code training?
Claude Code training teaches developers how to use Claude's coding agent to understand repositories, plan changes, edit files, run commands, debug issues, write tests, review changes and connect approved development tools through controlled workflows. The Technovids Claude Code Training programme covers all major Claude Code capabilities across 17 structured modules.
Who should learn Claude Code?
Claude Code training is designed for software developers, full-stack, frontend and backend engineers, Python and JavaScript/TypeScript developers, DevOps engineers, QA and automation engineers, AI engineers, technical leads, engineering managers who supervise AI-assisted development, and founders building software products. Professionals transitioning into AI-assisted coding will also find significant value in this programme.
Do I need programming experience for this training?
Basic programming knowledge is recommended. Familiarity with the command line and Git basics is helpful. No prior Claude Code experience is required — the programme starts from installation and configuration and progresses through real development workflows. This is not a learn-to-code course; it is a course for developers learning to work effectively with an AI coding agent.
Does the course cover CLAUDE.md?
Yes. Module 4 is dedicated to project memory and CLAUDE.md — covering how to write effective project instructions, repository conventions, coding standards, testing commands, architecture notes and shared team instructions. You will learn how to maintain useful project memory that keeps Claude Code aligned with your team's practices without replacing your project documentation.
Does it include debugging and testing workflows?
Yes. Module 6 is dedicated to debugging — reading error messages, reproducing defects, tracing execution and applying minimal targeted fixes. Module 8 covers testing workflows including unit tests, integration tests, test generation, failure diagnosis, coverage improvement and reviewing test quality to avoid meaningless generated tests.
Does it cover Git and pull-request workflows?
Yes. Module 9 covers Git workflows — reviewing repository status, working with branches, reviewing diffs, preparing commits with meaningful messages, pull-request preparation, issue-to-code workflows and reviewing generated changes. The module emphasises human approval before merging and responsible review of AI-generated commits.
Does it cover hooks and skills?
Yes. Module 11 covers hooks — what they are, suitable use cases, validation hooks, formatting and linting hooks, security checks, logging and avoiding unsafe hook execution. Module 12 covers skills and reusable commands — building repeatable team workflows for documentation, testing, review and onboarding. Only currently supported behavior is covered in both modules.
Does it cover MCP integrations?
Yes. Module 13 covers MCP integrations in Claude Code — connecting approved developer tools including repositories, issue trackers, databases, documentation systems and observability tools. The module covers authentication, permission boundaries, trusted MCP servers, review and monitoring. It links naturally to the broader What is MCP article and MCP Training Bangalore programme.
Is Claude Code suitable for engineering teams?
Yes. Module 16 is dedicated to team adoption and engineering governance — shared settings, coding policies, approved tools, repository safeguards, AI-generated code review, shared CLAUDE.md instructions, audit and ownership, onboarding developers, measuring usefulness and escalation procedures. The module links to Claude Enterprise Training for broader organizational Claude adoption.
How is this different from the general Claude AI training course?
The Claude AI Training Course covers Claude Chat, business productivity, document analysis, connectors, MCP concepts, team adoption and Module 6 on developer workflows as part of a broader programme. Claude Code Training is a dedicated developer-focused programme covering Claude Code in full depth — 17 modules on installation, codebase exploration, planning, implementation, debugging, refactoring, testing, Git, permissions, hooks, skills, MCP, subagents, AI engineering and team governance. It is intended for engineers who want to work deeply with Claude Code, not a general Claude overview.
Are live batches available?
Yes. Technovids runs public and corporate Claude Code training batches with live instructor-led sessions on weekday evenings and weekend mornings. Check the upcoming batches section on this page or contact us on WhatsApp for the next available cohort. Corporate batches can be scheduled on request for engineering teams.
Official Claude Code Resources
The following links go to official Anthropic pages. Features, supported environments and availability may change — always refer to current Anthropic documentation for the latest information.
Claude and Claude Code are trademarks of Anthropic. Technovids is an independent training provider and is not affiliated with or endorsed by Anthropic. Product features, supported environments and availability may change based on Anthropic plans, documentation and administrator configuration. Features described reflect publicly documented capabilities at the time of writing — verify current availability in your specific setup before building production workflows.
Related Programs and Resources