Use AI to write software, or build AI into software?
Assistant users need good context, review, tests and safe tool habits. Teams building AI features also need APIs, retrieval, evaluation, security and operations.
For engineering teams · Use AI and build with AI
AI Training for Software Developers
Help your developers build good habits with AI coding tools, and the skills to design, test and run software that uses LLMs, not just write more code faster.
- Audience
- Developers, QA, DevOps, SREs and tech leads
- Routes
- Ship faster with AI, or build AI features
- Practice
- Code, tests, evaluation and go-live choices
- Control
- Code review, security and repository limits
Direct answer
What is AI Training for Software Developers?
AI Training for Software Developers is private training for engineering teams that use coding assistants or build AI features. It can cover finding your way around a codebase, planning, refactoring, testing, LLM APIs, structured output, retrieval-augmented generation (RAG), evaluation, monitoring and security. The right route depends on whether your team needs to ship software faster with AI, build AI into products, or both. The proposal confirms the languages, repositories, cloud services, labs and access limits. It is GenAI training for engineers who write code every day.
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Who this page is for
Relevant audiences
- Software developers and full-stack teams
- QA, test-automation and quality engineers
- DevOps, SRE, platform and cloud engineers
- Tech leads and architects making AI choices
Useful when
- Coding assistants are available, but every developer reviews and uses them differently
- A product team needs to call a model API or handle structured AI output
- A RAG or agent prototype needs testing and production controls
- Engineering leads want one agreed approach across repositories and roles
Scope boundary: The programme does not give access to private repositories, live systems or paid model services. You approve the accounts, sample code, repositories, tools and data before any hands-on work.
Decision guide
Pick the skill before you pick the tools
Using coding assistants and building AI applications overlap, but they need different starting skills and produce different evidence.
Which languages and repositories matter?
Practice should match your stack and code-review process. Generic Python labs do not cover Java, TypeScript, C# or platform-specific needs.
What can the tools touch?
Agree the accounts, repositories, files, commands, secrets, data and outside tools before connecting a coding agent.
How will you judge quality?
Check correctness, tests, security, how easy the code is to maintain, speed, cost and error handling, not lines of code generated.
Engineering tasks
Practise the work, and the review around it
Modules are picked for your route and stack. In every exercise, an engineer stays responsible for the change.
Understanding and planning in a codebase
Use an approved assistant to explain unfamiliar code, trace behaviour and plan a small change.
- Practise
- Ask it to point to named files, compare the plan with your architecture and tighten the scope.
- Control
- Never share code you are not allowed to share, and check every explanation against the repository.
Refactoring and writing tests
Generate a small change, unit tests and edge cases from clear acceptance criteria.
- Practise
- Read the diff, run the tests, check which failures they catch and make the code easier to maintain.
- Control
- Generated code goes through the same review, security and licence checks as code written by people.
Calling a model API
Design a service call with structured output, validation, timeouts, retries and usage tracking.
- Practise
- Handle bad output and provider outages, not just the case where everything works.
- Control
- Keys stay out of prompts and source control. You own provider access and its cost.
RAG and answers with sources
Build or study the steps that load, split, search and cite an approved set of documents.
- Practise
- Write retrieval and answer checks using questions whose answers you already know.
- Control
- Retrieval does not make an answer true. You still need permission filters and source checks.
Evaluation and monitoring
Turn expected behaviour and known failure cases into repeatable tests and live signals.
- Practise
- Compare versions on quality, speed, cost and failure cases before release.
- Control
- A demo test set is not proof for production. Test data and pass marks need owners.
AI application security
Look at prompt injection, leaking sensitive data, unsafe handling of output and agents with too much power.
- Practise
- Map the threats to one feature and add input, output, permission and human-approval controls.
- Control
- Training supports your engineering review. It is not a penetration test or a security certificate.
Recommended programmes
Choose a developer route
These programmes meet different engineering needs, and can run one after another once prerequisites are confirmed.
AI-assisted coding with Claude Code
Use a coding agent to plan, change, test and review code in your repository, with clear tool limits.
Best for: Development teams adopting Claude Code
- Repository context
- Diff and test review
- Tool and permission limits
Advanced prompt engineering
Design and test reliable, structured prompts for applications and engineering work.
Best for: Developers who already know LLM basics
- Structured outputs
- Testing patterns
- Tool use
Production AI engineering
Build and run RAG, agent and LLM-based systems through hands-on projects with real-world limits.
Best for: Python developers and engineers building AI products
- RAG and agents
- Evaluation and monitoring
- Deployment and projects
AI programming foundation
Build the Python, data and API skills you need before deeper AI engineering.
Best for: Technical staff who are not yet confident in Python
- Python and data
- API practice
- AI-focused exercises
Coding with AI versus building AI applications
Use this split so that one mixed group is not taught at the wrong depth.
| Decision point | Coding with AI | Building AI applications |
|---|---|---|
| Main result | Use approved assistants in your existing engineering work | Design, connect, test and run AI features |
| Core practice | Planning, code, tests, review and documentation | APIs, RAG, agents, evaluation, security and deployment |
| What you need first | Development experience in your team’s stack | Confident programming plus application and data basics |
| Evidence | Reviewed changes and agreed team standards | A working system, test results and go-live decisions |
Practical value
What your engineers should be able to show
The proposal turns these into exercises that suit your stack and level.
Better-scoped AI changes
Developers give context and acceptance criteria before they generate or change code.
Review based on evidence
Tests, static checks, security review and maintainability decide what gets merged.
More reliable AI features
Model output is validated, and failures, speed, usage and cost are planned for.
Clear production limits
Data, permissions, evaluation and human approval are part of the design.
Method
From your first call to skills used at work
The same five steps every time keep the training tied to your team’s real work, with the costs and delivery details set out in writing.
- 01
Talk to our L&D lead
Chandan, our corporate L&D expert, asks who the training is for, what they need, any limits on delivery and what should change afterwards.
- 02
Expert check
A Technovids subject expert checks your team’s starting level, tools, examples, risks and the output you want.
- 03
Written proposal
It confirms the agenda, format, dates, group size, what is included and excluded, and the price.
- 04
Live training
Short explanations, demos, practice and feedback, ending with a task from your team’s own work.
- 05
Handover
Your team keeps the agreed templates and work, and you receive the completion or follow-up details set out in the proposal.
What engineering teams can take away
The exact outputs depend on the route, environment and access you approve.
Team coding standard
A reusable brief, generate, review and test routine for approved assistants.
Reviewed lab or project
A small code change or AI feature, with acceptance and review evidence.
Evaluation checklist
Quality, security, speed, cost and failure questions for the chosen task.
Next-steps backlog
Good engineering uses for AI, what each needs first, owners and the next experiments.
Safe application
Safe use is built into every exercise, not added at the end
Every programme teaches your people to work within your company’s rules, with a person clearly in charge of the result.
Use approved accounts and data
Your team learns what they can type into an AI tool, what must stay out, and when to use an approved workspace or a made-up example.
Check before you use it
Facts, sums, sources, tone and the effect on any decision are checked against the original material.
Keep a person in charge
AI can help with a step, but a named person still checks the output and owns the decision or message.
Measure one clear task
Teams compare quality, effort, risk and take-up on one defined task, instead of claiming general time savings.
Primary sources
Security topics based on a current reference
Every system needs its own threat model. The programme uses the current OWASP GenAI risk list as one engineering reference.
Training in action
See Technovids training in practice
Real photos from Technovids training sessions. They show how we teach, not a promised result for your group.
Photo 1 of 10: Hands-on session 
Photo 2 of 10: Group photo 
Photo 3 of 10: Hands-on session 
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
Trainers
Meet the Technovids trainer panel
We choose the trainer once we know who is attending, how deep the subject goes and how it will be delivered.
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
Your proposal names the trainer. Not every trainer shown here teaches every programme.
To book a trainer for your team, call +91 86183 46384 or email [email protected].
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Client & participant proof
See how we deliver before you decide
Clients who agreed to be named, feedback from people we have trained and real photos from our sessions show how Technovids works. They are not a promise of any particular result.
Organisations we have delivered training for
Hands-on training for people and teams in AI, data, cloud, productivity and workplace skills.
Participant voice
Participant experiences across workplace roles
What people we trained found useful in their day-to-day work.
- 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.
- 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.
- KrantiSenior Software Engineer · Siemens
The AI training delivered by Technovids was practical, structured, and relevant for software professionals. The sessions connected AI concepts with real industry use cases, making it easier to understand how AI can be applied in development, automation, and productivity improvement. It was a valuable learning experience.
- Rahul DesaiSenior Software Engineer
I had been trying to transition from traditional backend development to Generative AI, but self-learning was getting overwhelming. The AI Engineering course at Technovids gave me the exact structured roadmap I needed. The modules on LLM architectures and practical prompt engineering are brilliant. It’s not just theory; you actually build functional AI applications. Highly recommend this for working professionals looking to upgrade their tech stack.
- Sneha ReddyFull Stack Developer
What I loved most about the Technovids GenAI program is how up-to-date the curriculum is. Instead of outdated practices, we learned to integrate the Model Context Protocol (MCP) and practically apply tools like Claude and Cursor for AI-assisted coding. It felt like I was learning the exact skills that top tech companies are hiring for right now. The pace is perfect, and the support is great.
- Neha GuptaBackend Developer
I was initially intimidated by the math and complexity behind AI, but the trainers at Technovids explain things in such an accessible way. They break down everything from basic prompt engineering to complex integrations without losing you in the jargon. The session recordings and project materials are top-notch. If you are serious about becoming an AI Engineer, Technovids provides the perfect launchpad.
Frequently asked questions
These answers cover what this page offers. Your written proposal sets out the terms for your own programme.
Which programming languages can the labs use?
We confirm the route and language when we plan. Python suits many AI engineering labs. API work and coding with AI can be adapted to TypeScript, Java, C# or another common stack, where the exercises and trainer skills support it.
Do developers need machine-learning knowledge?
Not for coding with AI. Production AI engineering needs stronger programming, data, API and system-design skills. The proposal lists the prerequisites rather than assuming everyone starts at the same level.
Can the programme use AWS Bedrock, Azure OpenAI or Google Cloud services?
Yes, once you confirm your platform, approved services, accounts, regions and lab setup. Access, usage charges and production setup are not included unless the proposal says so.
Does the programme cover AI-generated code review?
Yes. The relevant routes cover acceptance criteria, reading diffs, tests, static analysis, dependency and security checks, and the limits of AI explanations. The engineer stays responsible for the merged change.
Can this run as a build day or hackathon?
Yes, when the team already has access to the tools, a repository or dataset it is allowed to use, and a problem small enough to finish in the time. It suits senior engineers who want to ship one small thing, such as a retrieval prototype, an assisted refactor or a test-generation routine, and then review what the assistant got wrong and why. It is a poor fit when skill levels are uneven, because a build day shows the gaps without time to close them. A short structured session first, then the build, usually works better. The format, prerequisites, materials and what each team should leave with are agreed in the proposal.
Can participants work on our production repository?
Not by default. You can provide an approved sandbox or cleaned-up repository under your own access and security controls. Technovids does not assume production access or permission to keep your code.
Talk to Technovids
Get a training proposal for your team
Tell us who the training is for, roughly how many people, and what you want them to do better at work. The Technovids team will scope it and reply with a written proposal.
Request received
Thank you — your request has been sent to the Technovids corporate training team. If you would rather speak to someone directly, the phone and WhatsApp details are at the top and bottom of this page.
Technovids Consulting
Bengaluru head office. India-wide live delivery.
2nd Floor, Chandrodaya Complex, 19/19, 24th Main Rd, near Hanuman Temple, Agara Village, 1st Sector, HSR Layout, Bengaluru, Karnataka 560102.
Please contact us before visiting the head office. The HSR Layout address is a head office, not a walk-in public classroom.