What is the authoritative corpus?
Define included sources, ownership, versions, freshness, access and deletion before indexing.
Live-online RAG training · Technovids Bengaluru
RAG Training in Bangalore
Build retrieval systems that can be measured: from document preparation and search to citations, answer evaluation, access control and production handoff.
- Scope
- Ingestion, retrieval, generation and evaluation
- Audience
- Python developers, data and AI engineers
- Duration
- 70 hours · live online
- Bengaluru
- HSR Layout head office · not a classroom
Direct answer
What is RAG Training in Bangalore?
RAG Training in Bangalore is developer-focused training on Retrieval-Augmented Generation from Technovids, headquartered in HSR Layout, Bengaluru. It covers ingestion, chunking, embeddings, vector and hybrid retrieval, metadata, reranking, grounded generation, citations, evaluation and operational controls. Participants learn to diagnose retrieval and answer failures separately. Public cohort dates and fees are confirmed before enrolment and classes run live online; private corporate delivery can be scoped for Bangalore teams.
Updated
Who this page is for
Relevant audiences
- Python developers building document or knowledge applications
- Data and AI engineers responsible for retrieval quality
- Technical leads reviewing a RAG architecture
- Bangalore teams seeking a private RAG workshop
Useful when
- A basic vector-search demo fails on real documents
- The team cannot distinguish retrieval failure from generation failure
- Citations, access control or freshness are weak
- A system needs an evaluation set before model or chunking changes
Scope boundary: The course does not guarantee hallucination elimination, retrieval accuracy or production readiness. Data, model, embedding, store, API cost and projects are confirmed for the selected delivery; the standard course runs 70 hours.
Decision guide
Measure retrieval before tuning generation
A fluent answer cannot recover evidence the retrieval layer failed to find.
How should documents be represented?
Choose parsing, structure, chunks and metadata according to questions and evidence boundaries.
What counts as relevant retrieval?
Build query–evidence judgements before comparing embeddings, search or reranking.
How is the final answer checked?
Measure source support, completeness, refusal and citation correctness separately from style.
RAG engineering practice
Build and diagnose the full retrieval path
Labs make the evidence flow visible so participants can locate failures instead of changing models blindly.
Document ingestion and parsing
Load approved files and preserve useful structure, provenance and version metadata.
- Practise
- Inspect tables, headings, duplicated text, failed parsing and unsupported formats.
- Control
- Poor source extraction cannot be fixed by a stronger model.
Chunking and indexing
Compare chunk boundaries, overlap and metadata for representative questions.
- Practise
- Check whether retrieved units contain enough evidence without excessive noise.
- Control
- One global chunk size is not automatically suitable for every document.
Dense, sparse and hybrid retrieval
Compare semantic, lexical and combined search for the same judged queries.
- Practise
- Measure retrieval results before adding generation.
- Control
- Similarity score is not the same as task relevance.
Filtering and reranking
Apply metadata permissions and reorder candidates with a deliberate quality–latency trade-off.
- Practise
- Test filter correctness, missing metadata and reranker failures.
- Control
- Filtering must enforce authorised scope, not merely improve relevance.
Grounded answer with citations
Generate an answer from retrieved evidence and expose source references.
- Practise
- Check each material statement against cited content and refuse unsupported questions.
- Control
- A citation can be present and still fail to support the claim.
RAG evaluation and monitoring
Track retrieval, answer, latency, cost and failure categories across a test set.
- Practise
- Rerun after changes in documents, models, prompts, chunks or indexes.
- Control
- An offline score does not replace production feedback and access monitoring.
Options
Choose focused RAG depth or the wider AI stack
A RAG specialist course is useful when retrieval is the main engineering gap.
This page
Focused RAG training
Build and evaluate the ingestion-to-answer pipeline in depth.
Best for: Developers and data engineers working on knowledge systems
- Retrieval design
- Grounded answers
- RAG evaluation
LangChain Training
Learn wider LangChain application patterns including retrieval and agents.
Best for: Developers standardising on LangChain
- Framework architecture
- Retrieval integration
- Agent patterns
Agentic AI Course
Use retrieval as one tool within controlled multi-step agent workflows.
Best for: Developers building tool-using agents
- Agent architecture
- State and tools
- Approval and evaluation
Production AI Engineering
Combine RAG with agents, MCP, APIs, deployment and operations.
Best for: Developers needing end-to-end production capability
- Broader architecture
- Hosted projects
- LLMOps concerns
Focused RAG course versus full AI engineering programme
The narrower course offers more retrieval depth; the full programme covers more of the surrounding system.
| Decision point | RAG Training Bangalore | Production AI Engineering |
|---|---|---|
| Primary scope | Ingestion, retrieval, grounding and RAG evaluation | RAG, agents, MCP, APIs, deployment and LLMOps |
| Best fit | Teams with a retrieval or knowledge-system goal | Developers building complete AI applications |
| Duration | 70 hours, live online | 80 hours: 40 live plus 40 guided project work |
| Typical output | Measured RAG pipeline and evaluation pack | Five integrated portfolio project patterns |
Practical value
What participants should be able to demonstrate
A good result shows retrieval and answer evidence separately.
A traceable corpus
Documents, versions, chunks, metadata and access expectations are explicit.
Measured retrieval
Queries have judged evidence and retrieval changes can be compared.
Supported answers
Material claims can be checked against cited passages and refusal rules.
A production-gap brief
Security, freshness, scale, latency, cost and monitoring gaps are documented.
Method
From course fit to working evidence
The route is confirmed before delivery so participants practise at the right technical level and do not confuse a tutorial demo with production readiness.
- 01
Clarify the learner or team goal
Confirm whether the need is a public learning route, a private team workshop or a broader engineering programme.
- 02
Check prerequisites and environment
Agree programming level, accounts, model access, practice data, repository and any security restrictions.
- 03
Confirm scope in writing
Dates, duration, topics, exercises, delivery mode, fees and inclusions are stated before booking.
- 04
Build and review
Short explanations lead into implementation, evaluation, debugging and instructor feedback.
- 05
Record the next engineering step
Participants leave with reviewed artefacts, known limitations and a route for further practice or implementation.
Possible RAG course artefacts
The selected format determines the corpus, implementation and evaluation depth.
Corpus and ingestion map
Sources, parsing, versions, metadata, permissions and refresh assumptions.
Working RAG pipeline
A bounded ingestion, retrieval and grounded-answer workflow.
Evaluation set and results
Queries, judged evidence, answer checks, failures and changes.
Production-readiness checklist
Access, privacy, scale, freshness, latency, cost and monitoring questions.
Safe application
Production controls are taught alongside implementation
The practical work keeps access, evaluation, failure handling and human authority visible.
Use approved data and accounts
Labs use authorised model access, safe documents and practice integrations; credentials and private data never belong in prompts or source control.
Evaluate representative tasks
A successful demo is tested against expected, difficult and failure cases before anyone calls it reliable.
Constrain tools and actions
Permissions, validation, timeouts, budgets and approval gates limit what an agent or integration may do.
Keep an accountable reviewer
A qualified person reviews sources, outputs, tool calls and material actions before release or use.
Primary sources
Current RAG references
The lab stack is confirmed before delivery; framework and provider integrations are checked against current first-party documentation.
Training in action
See Technovids training in practice
Real instructor-led sessions from the Technovids delivery archive. The photographs show delivery context; they do not imply a result for a future cohort.
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
Technovids assigns a suitable trainer after the audience, subject depth and delivery needs are confirmed.
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
The proposal names or confirms the trainer assignment for the engagement; every trainer shown here does not teach every programme.
Updated
Client & participant proof
Developer and participant perspectives
Approved feedback and delivery photographs show wider Technovids training experience. They are not presented as guaranteed outcomes from this specific 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 voice
Participant experiences across workplace roles
What professionals found useful, practical and relevant to 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
Published answers define the page scope. A written proposal records engagement-specific terms.
How long is the RAG training?
The standard RAG training runs 70 hours, delivered live online. A private team version can be shortened or extended, and the written proposal confirms its final duration.
What prerequisites are recommended?
Working Python, APIs and basic LLM concepts are recommended. Data handling and search familiarity help, but the confirmed cohort or proposal states the exact starting level.
Which vector database and framework are used?
The primary practice stack is confirmed for each delivery based on current integrations and access. The engineering principles apply across stores and frameworks, but equal hands-on depth in every product is not promised.
Does RAG eliminate hallucinations?
No. RAG can provide relevant external evidence, but retrieval, context use and generation can still fail. The course teaches separate retrieval and answer evaluation plus refusal and review patterns.
Are public RAG classes held at the HSR Layout office?
No. HSR Layout is the Technovids head office. When a public cohort is scheduled, classes run live online through Zoom. The Technovids LMS includes notes, assignments, lab instructions, a cohort discussion group, a completion certificate when requirements are met and recordings uploaded within 24 hours with three months of access.
Are public dates and fees currently guaranteed?
No. Enquire for the next confirmed option, full timetable, prerequisites, lab stack, inclusions and fee.
Can a company request a private Bangalore programme?
Yes. Private training can use a safe client-relevant corpus and approved stack, delivered live online or onsite at the client’s workplace when agreed.
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.