Which sources can be indexed?
Decide ownership, access, version, how long documents are kept, how fresh they must be and how they are deleted.
Private RAG training · Across India
RAG Training India — Retrieval-Augmented Generation for Developer Teams
Help your developers build search-and-answer systems you can still measure as your documents, questions, models and indexes change.
- Audience
- Developer, data and AI engineering teams
- Delivery
- Private, live online or at your office
- Depth
- Search, answers, testing and operations
- Data
- Safe practice documents, or your approved material
Direct answer
What is RAG Training India?
RAG Training India is private training for developer, data and AI engineering teams building Retrieval-Augmented Generation (RAG) systems. It covers choosing and loading documents, splitting them into chunks, embeddings, vector and hybrid search, filters, reranking, answers backed by sources, citations, testing, access control and handing over to production. Your team learns to test the search step and the answer step separately, using realistic questions and evidence. Training runs live online across India, or at your office when agreed. Teams looking for a RAG course in India get a private retrieval-augmented generation course built around their own documents.
Updated
Who this page is for
Relevant audiences
- Software developers building knowledge apps
- Data engineers preparing company documents
- AI engineers improving search and answer quality
- Architects and tech leads reviewing RAG systems
Useful when
- A pilot works on sample PDFs but fails on your real documents
- Teams tune the answers without measuring how good the search is
- Permissions, freshness and citations need a stronger design
- Several RAG projects need the same way to test and release
Scope boundary: Training does not guarantee accuracy, remove the risk of made-up answers or deploy a live system. You own the data rights, access, design, security, testing and release approval.
Decision guide
Make RAG quality visible at every step
A useful programme links document rules, search evidence and answer review, instead of treating RAG as one call.
Which questions must work?
Build a set of realistic questions with the right evidence for each, based on what users and experts really ask.
Where did it go wrong?
Tell apart errors in reading files, chunking, search, reranking, context and the final answer.
What must pass before release?
Set limits for quality, speed, cost, access and unsupported answers before rollout.
Corporate RAG practice
Practise the whole pipeline against realistic evidence
A safe set of your own documents can replace generic samples, when approved and prepared before training.
Choosing and loading documents
Map documents, file reading, metadata, versions, permissions and refresh.
- Practise
- Look at where reading fails, and keep track of each document’s source through the index.
- Control
- Only approved material goes into the training or target system.
Chunking experiment
Compare ways of splitting documents, using questions with known answers.
- Practise
- Check whether the evidence can still be found and understood.
- Control
- Smaller or larger chunks are not always better.
Search starting point
Set up keyword, meaning-based or hybrid search, measured against questions with known evidence.
- Practise
- Review misses, wrong matches and types of question before tuning.
- Control
- One similarity cut-off cannot suit every question.
Filters and reranking
Filter by permissions and metadata, and reorder results when it helps.
- Practise
- Measure the gain in relevance, the effect on speed, missing metadata and whether permissions hold.
- Control
- Better relevance must never weaken access control.
Answers backed by sources
Generate answers from the chosen evidence, with citations and the right to say “I don’t know”.
- Practise
- Check that each claim is supported, nothing is missing, conflicts are handled and doubts are stated.
- Control
- A model can misread good evidence and still sound sure.
Testing and change control
Rerun the tests after any change to documents, model, prompt, chunks or index.
- Practise
- Record search, answer, speed, cost and access results by type of failure.
- Control
- An average score must not hide serious failures.
Options
Choose focused RAG training or a wider team route
The right route depends on whether search is the main gap, or one part of building a bigger application.
RAG design workshop
Frame the documents, users, questions, tests and limits for production.
Best for: Architects, leads and mixed technical groups
- Pipeline design
- Test plan
- Risk and ownership
Hands-on RAG engineering
Build and test a search pipeline in the agreed stack.
Best for: Python developers, data and AI engineers
- Loading and search
- Answers from sources
- Finding failures
LangChain Training India
Use RAG within wider LangChain application and agent patterns.
Best for: Teams standardising on LangChain
- Framework patterns
- Search and agents
- Testing across versions
Production AI Engineering
Link RAG with agents, MCP, APIs, deployment and LLMOps.
Best for: Teams that need complete application skills
- Joined-up system design
- Testing
- Operations
Focused RAG training versus production AI engineering
The focused route goes deeper into search. The wider route links search to the rest of the system.
| Decision point | RAG-focused corporate training | Production AI Engineering |
|---|---|---|
| Main goal | Better search and answers backed by sources | Build and run complete AI applications |
| Core evidence | A question-and-evidence set and a measured RAG pipeline | Joined-up projects and testing of the whole system |
| Best for | Teams with one knowledge-system need | Teams building RAG plus agents, APIs and deployment |
| Scope | Confirmed for your documents, stack and team level | Confirmed for the wider engineering programme |
Practical value
What your team should be able to show
The result is a repeatable trail of evidence across data, search and answers.
Document rules map
Source, access, version, refresh and deletion are all written down.
A measured search starting point
Realistic questions link to checked evidence and types of failure.
Answer checks
Support, completeness, citations and “I don’t know” answers can be checked.
Production handover note
Gaps in scale, security, speed, cost, monitoring and ownership are recorded.
Method
From the right course to working code
We confirm the route before training starts, so everyone practises at the right level and nobody mistakes a tutorial demo for something ready for production.
- 01
Agree the goal
Confirm whether you need a public course, a private team workshop or a wider engineering programme.
- 02
Check skills and setup
Agree the coding level, accounts, model access, practice data, repository and any security limits.
- 03
Confirm the scope in writing
Dates, length, topics, exercises, format, fees and what is included are set out before you book.
- 04
Build and review
Short explanations, then building, testing, debugging and feedback from the trainer.
- 05
Plan the next step
Everyone leaves with reviewed work, its known limits and a plan for more practice or a real build.
What your team can take away
The proposal picks the outputs that fit your team, documents, stack and time.
RAG design worksheet
Sources, users, questions, pipeline, access, tests and owners.
Reference pipeline
A limited build for loading, search and answers backed by sources.
Tests and failure pack
Questions with known evidence, results, traces, errors and next experiments.
Production readiness checklist
Security, document lifecycle, scale, speed, cost, monitoring and release.
Safe application
Production safeguards are taught as you build
In every exercise, you can see who has access, how the work is tested, how failures are handled and where a person decides.
Use approved data and accounts
Labs use approved model access, safe documents and practice connections. Passwords, keys and private data never go in prompts or source control.
Test on realistic tasks
A demo that works is tested on normal, hard and failing cases before anyone calls it reliable.
Limit tools and actions
Permissions, input checks, timeouts, budgets and approval steps limit what an agent or connection can do.
Keep a person reviewing
A qualified person checks sources, outputs, tool calls and important actions before release or use.
Primary sources
Current RAG references
We check the agreed framework and provider versions before training and link to the makers’ own documentation.
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].
Updated
Client & participant proof
Developer and participant perspectives
Feedback shared with permission and photos from our sessions show how Technovids trains. They are not a promised result from this course.
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.
Who should attend corporate RAG training?
Software developers, data engineers, AI engineers and tech leads working on document, search or knowledge apps. People who only need the design view can join a decision workshop. Hands-on participants need to know how to program.
Can the training use our documents?
Yes, if you own them or are allowed to use them, and you approve the setup, access and preparation. Otherwise we use a safe made-up or public set of documents.
Does the programme cover vector databases and reranking?
It can cover vector search, keyword or hybrid search, metadata filters and reranking. The products, depth, accounts and datasets are confirmed in the proposal.
Does RAG guarantee factual answers?
No. Both search and answering can fail. The programme teaches how to test search with evidence, check answers, use citations, let the system say “I don’t know” and keep a person reviewing, rather than promising accuracy.
Can training be delivered onsite across India?
Private training can run live online or at your office when agreed. The proposal confirms location, travel, facilities, group size, lab readiness, length and price.
Does training include production implementation?
No. Training builds skills and the agreed practice work. Production discovery, data preparation, building, security review, testing, integration and support need a separate scope.
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.