Grounded AI on your own data
Chunking, embeddings, vector search, reranking and evaluation — the architecture behind every AI assistant that answers from a company's own documents.
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Course overview
The RAG course in Ludhiana at techcadd covers Retrieval-Augmented Generation end to end — chunking, embeddings, vector databases, hybrid search, reranking, citations, refusal behaviour and evaluation — because a grounded assistant is only ever as good as what it retrieves.
Retrieval-Augmented Generation is the most requested AI feature in business right now: point a model at your own documents so it answers from them instead of inventing something. Building one that demos well takes an afternoon. Building one an organisation can rely on is a different job, and it is almost entirely a retrieval problem rather than a model problem.
So that is where this course lives. You will work through chunking strategies and why the obvious one fails on tables and contracts, embedding choice, hybrid keyword-plus-vector search, reranking, metadata filtering, citation handling, and a refusal path for when the corpus genuinely has no answer. Then you measure it — recall, MRR and faithfulness on your own document set — because "it seems better" is not a finding.
Prerequisite is Python plus some experience calling an LLM API. This is the natural next course after Generative AI, and students often take the two back to back.
Curriculum
4 modules and 23 topics across 6–8 weeks, in the order they are taught. Each one closes with something that works before the next one opens.
The failure mode RAG solves, and the cases where fine-tuning or plain prompting is the better answer.
Topics covered
Tools & libraries
You finish with
A written architecture decision for a real use case, with the alternatives ruled out.
The unglamorous half that determines whether retrieval works at all.
Topics covered
Tools & libraries
You finish with
An ingestion pipeline handling PDFs, HTML and spreadsheets into a searchable index.
Dense search, keyword search, hybrid retrieval and the reranking step that fixes most bad results.
Topics covered
Tools & libraries
You finish with
A hybrid retriever measurably outperforming a naive vector search on your corpus.
Answering from retrieved context, citing sources, refusing when there is nothing — and proving it works.
Topics covered
Tools & libraries
You finish with
A deployed RAG service with an evaluation report covering recall, faithfulness and cost.
Where projects die
Every failure below is common, fixable, and covered in the syllabus. Together they are the reason this course spends more time on retrieval than on prompting.
Fixed 500-character splits cut tables in half, separate a clause from its heading, and strip the context that made a paragraph meaningful. Chunking strategy is a design decision per document type, not a config value.
Pure embedding search misses exact identifiers — part numbers, invoice references, section numbers — precisely the things people search for. Hybrid retrieval with a keyword component fixes most of it.
Retrieving twenty candidates and passing the first three unranked wastes the retrieval. A reranking pass is the single highest-return change in most projects.
An assistant that cannot say "that is not in these documents" will invent something instead, once, in front of the client. The refusal path is not a nicety; it is what makes the system trustworthy.
Without recall, MRR and faithfulness on a real question set, every change is a guess and every review is an argument. You build the evaluation harness before you tune anything.
Fixing these five is, in practice, the job. It is also the whole interview.
Find your pace
The RAG Systems syllabus in the order you meet it, and what you are able to do by the end of each stretch of it. Every row is a capability, not a topic you sat through.
| Capability | Foundations | Applied | Professional |
|---|---|---|---|
| Hallucination causesWhy RAG, and When Not To | Covered in Foundations | Covered in Applied | Covered in Professional |
| RAG vs fine-tuningWhy RAG, and When Not To | Covered in Foundations | Covered in Applied | Covered in Professional |
| Architecture overviewWhy RAG, and When Not To | Covered in Foundations | Covered in Applied | Covered in Professional |
| Cost trade-offsWhy RAG, and When Not To | Covered in Foundations | Covered in Applied | Covered in Professional |
| Scoping a corpusWhy RAG, and When Not To | Covered in Foundations | Covered in Applied | Covered in Professional |
| Document parsingIngestion, Chunking & Embeddings | Covered in Foundations | Covered in Applied | Covered in Professional |
| Chunking strategiesIngestion, Chunking & Embeddings | Covered in Foundations | Covered in Applied | Covered in Professional |
| Metadata designIngestion, Chunking & Embeddings | Covered in Foundations | Covered in Applied | Covered in Professional |
| Embedding modelsIngestion, Chunking & Embeddings | Covered in Foundations | Covered in Applied | Covered in Professional |
| Batch indexingIngestion, Chunking & Embeddings | Covered in Foundations | Covered in Applied | Covered in Professional |
| Incremental updatesIngestion, Chunking & Embeddings | Covered in Foundations | Covered in Applied | Covered in Professional |
| Vector databasesVector Search & Reranking | Not yet covered in Foundations | Covered in Applied | Covered in Professional |
| Similarity metricsVector Search & Reranking | Not yet covered in Foundations | Covered in Applied | Covered in Professional |
| Hybrid BM25 + denseVector Search & Reranking | Not yet covered in Foundations | Covered in Applied | Covered in Professional |
| Cross-encoder rerankingVector Search & Reranking | Not yet covered in Foundations | Covered in Applied | Covered in Professional |
| Filtering by metadataVector Search & Reranking | Not yet covered in Foundations | Covered in Applied | Covered in Professional |
| Query rewritingVector Search & Reranking | Not yet covered in Foundations | Covered in Applied | Covered in Professional |
| Context assemblyGeneration, Citations & Evaluation | Not yet covered in Foundations | Not yet covered in Applied | Covered in Professional |
| Citation formattingGeneration, Citations & Evaluation | Not yet covered in Foundations | Not yet covered in Applied | Covered in Professional |
| Refusal handlingGeneration, Citations & Evaluation | Not yet covered in Foundations | Not yet covered in Applied | Covered in Professional |
| Retrieval metricsGeneration, Citations & Evaluation | Not yet covered in Foundations | Not yet covered in Applied | Covered in Professional |
| Answer faithfulnessGeneration, Citations & Evaluation | Not yet covered in Foundations | Not yet covered in Applied | Covered in Professional |
| Latency & cost tuningGeneration, Citations & Evaluation | Not yet covered in Foundations | Not yet covered in Applied | Covered in Professional |
The full programme runs 6–8 weeks. Where you finish is a question of pace rather than of syllabus — everyone covers all of it.
The case for it
Almost every failed RAG project failed at retrieval and blamed the model. Learning to measure and fix that is the difference between a demo that impresses a manager and a system a company keeps.
You learn retrieval-augmented generation the way it is used in production, with the workflow, tooling and standards a working team expects. Nothing in the syllabus exists only to fill hours.
Sessions are hands-on. You do every example, break it, and fix it. Each module closes with a lab task that has to work before you move on.
Four shipped finished projects — the kind of work an interviewer can open, click through and question you on.
A structured final phase: portfolio review, profile cleanup, aptitude and role-specific practice, mock interviews and placement support.
Why this course
Six things we hold ourselves to for every RAG Systems batch that starts in Ludhiana.
Syllabus built around what retrieval-augmented generation teams actually ship — no filler modules, no theory that never reaches your hands.
Every concept lands in the tool the same day. You work in retrieval-augmented generation during each class, not just in your notes.
Build with real requirements, real data and real problems — from the first task to a finished, reviewable deliverable.
Trainers who work with retrieval-augmented generation daily review your work, your logic and your approach line by line.
Resume, portfolio, aptitude rounds and interview practice built into the last phase of the course.
Interview referrals, mock rounds and continued doubt support after the course ends.
Who can join
Six kinds of people sit in a typical batch, and none of them arrive knowing retrieval-augmented generation. Find the one that sounds like you.
You have finished school and want a skill that pays before a degree does. The first module assumes you have never opened retrieval-augmented generation in your life.
No background neededBCA, B.Tech, B.Sc, BBA, B.Com — any stream. You learn retrieval-augmented generation alongside your degree and finish with projects your syllabus was never going to give you.
Weekend batchesYou are months away from interviews and need work to show, not marks to quote. The final phase is portfolio, mock rounds and referrals.
Placement supportYou already have a job and want retrieval-augmented generation on top of it. Evening and weekend batches exist for exactly this, and the project work is yours to schedule.
Evening batchesYou are coming from a different field entirely — operations, teaching, accounts, sales. Every module starts from why, not from jargon.
Start from zeroYou want to do this work yourself instead of paying for it. You leave able to build, run and judge retrieval-augmented generation work on your own terms.
Practical onlyNot sure which of these you are, or whether the timing works around what you already do? That is exactly what the call is for. Ask about RAG Systems
Technology ecosystem
RAG Systems is the centre. These are the tools you use around it in a working team.
Hands-on projects
Each one lands in your portfolio with the working files, the process and something a reviewer can open.
Capstone project
A grounded assistant over a thousand-document corpus: multi-format ingestion, metadata-filtered hybrid retrieval, cross-encoder reranking, cited answers, an explicit refusal path, a RAGAS evaluation report and a per-query cost budget — served behind an API.
Certification
A techcadd RAG certificate, an internship letter for the live-project phase, and a deployed grounded assistant with its evaluation report — a portfolio piece that answers the technical interview before it is asked.
Issued in your name on completion of the RAG Systems syllabus, with a reference number an employer can verify with us.
A separate certificate for the capstone you submit, naming the project so the work is attached to the credential.
Students who complete the live-project phase receive an internship letter covering the duration and the work delivered.
Every file, repository and deployed link stays yours — the part of the credential a reviewer can actually open.
techcadd has been training in Ludhiana since 2007. The certificate carries that record; the retrieval-augmented generation work you did carries the rest.
This is to certify that
has successfully completed the RAG Systems programme
RAG (Retrieval-Augmented Generation) Course
This is to certify that
for project work delivered under supervision in Ludhiana
RAG Systems capstone
Where it takes you
The route from your first module to the roles RAG Systems opens — and the work that has to exist at each step.
Build and tune retrieval systems behind AI assistants.
Ship grounded AI features on company data.
Own relevance across hybrid retrieval systems.
Combine retrieval, prompting and serving into one product.
Make an organisation's documents genuinely searchable.
Deliver grounded assistants for client organisations.
Salary outlook
RAG is rarely its own job title and almost always the reason somebody got the job. These are the roles it wins.
| Role | Ludhiana & Punjab | Delhi NCR & Bengaluru | Remote & freelance |
|---|---|---|---|
| AI Application DeveloperEntry to mid | ₹3.2–5.8 LPA | ₹6–13 LPA | ₹40k–1.2L / project |
| Generative AI EngineerEntry to mid | ₹3.6–6.5 LPA | ₹7–16 LPA | ₹50k–1.4L / project |
| Search / Retrieval EngineerMid (2–4 yrs) | ₹6–11 LPA | ₹12–26 LPA | ₹1L–3L / project |
| AI Solutions ConsultantMid (3–5 yrs) | ₹7–12 LPA | ₹13–28 LPA | ₹1.5L–4L / project |
Ranges are indicative, drawn from what our own students report and from openings we see through the placement cell. Actual offers depend on your portfolio, the interview and the company — nobody can promise you a number, and we do not.
Almost always part of one. You will be hired as an AI application developer or generative AI engineer, and RAG will be the thing they actually needed. Dedicated retrieval roles exist at companies with large document estates — legal, insurance, healthcare, enterprise search.
Retrieval quality is measurable, which makes it unusually easy to argue for. Engineers who can show a before-and-after on recall and faithfulness for a real corpus tend to move up faster than the general AI cohort.
Yes — this is portfolio-first work. A deployed assistant over a public document set, with an evaluation notebook beside it, is enough to win remote contracts without a local reference.
Locally: legal and chartered accountancy practices, hospitals and diagnostic chains, insurance and finance offices, education groups with large syllabus archives, and manufacturers with technical documentation and compliance records. Every one of those is a corpus somebody is currently searching by hand.
Very relevant to information-retrieval and NLP specialisations, and the evaluation work translates directly into the methodology section of a dissertation.
Future scope
A course ends; the field does not. Here is the honest version of what the next few years look like for retrieval-augmented generation — where the roles go, and what is shifting underneath them while you learn.
Entry roles such as RAG Engineer open as soon as you have projects that run. At this stage nobody is asking about your marks — they are asking you to walk through something you built.
The generalists plateau; the specialists do not. Depth in one part of retrieval-augmented generation — the part your first job leans on hardest — is what moves you towards ai application developer work.
Architecture, standards, hiring and mentoring. The technical skill is assumed by now; what you are paid for is judgement, and judgement only comes from having shipped things that mattered.
Teams expect retrieval-augmented generation work to be done alongside AI tooling. It raises the floor on output and raises the bar on what counts as a junior — which is exactly why the fundamentals in this course are taught the hard way.
What used to be one narrow role now touches data, deployment and the product decision behind it. Every extra layer of retrieval-augmented generation you understand is another kind of room you get invited into.
Remote and hybrid hiring means Ludhiana, Jalandhar and Chandigarh candidates now compete for the same roles as Bengaluru ones. Location has stopped being the ceiling it used to be.
Hiring has moved to portfolios, take-home tasks and live problem solving. A certificate opens a shortlist; work that runs is what gets you through the round after it.
Sectors hiring for this skill set
The comparison
Every institute in Ludhiana claims industry training and placement support. These are the differences you can actually check on a demo visit — ask any of them of anyone, including us.
| What to ask about | techcadd | Typical institute |
|---|---|---|
| Who teaches | Trainers who still do retrieval-augmented generation work outside the classroom | Full-time faculty teaching from a fixed deck |
| 1-on-1 classes | One-to-one teaching available on every course — the pace is yours, and your work is looked at by name | One group class moving at one pace, whoever that pace happens to suit |
| Project work | 5 real projects plus a capstone you deploy and defend | Guided exercises copied from the board |
| Curriculum | Reviewed every batch against what working teams ship | Updated when the printed syllabus is reprinted |
| How a module ends | A lab task that has to run before you move on | Notes to revise before an exam |
| Doubt support | Trainer sits with your code; open lab hours between classes | Ask at the end of class if there is time left |
| What you leave with | Completion certificate, project certificate, internship letter — all verifiable | One printed certificate |
| After the course | Portfolio review, mock interviews, referrals, continued doubt support | The course ends and so does the contact |
Written about the market, not about any particular institute in it. Visit two or three, sit through a demo class at each, and ask all eight of these questions — that is the only version of this table worth trusting.
Student Voices
Feedback from students who completed the RAG Systems programme at techcadd Ludhiana.
Simranjeet Kaur
RAG Systems / B.Sc. IT graduate
I joined with no background in this. By the third month I was building retrieval-augmented generation work on my own, and the project reviews are where I actually learnt to do it properly.
Harman Sethi
RAG Systems / Now working as an intern in the field
The classes are practical. Every session ends with a task that has to work, so you cannot fake understanding. That habit helped me most in interviews.
Ankit Verma
RAG Systems / BCA final year
Doubt support was the difference for me. My trainer sat with my work, found the mistake and made me fix it myself instead of handing over the answer.
Navjot Singh
RAG Systems / Career switch from operations
I came for the skill and left with a portfolio — projects I could actually demo on a call, not a certificate I had to explain.
Simranjeet Kaur
RAG Systems / B.Sc. IT graduate
I joined with no background in this. By the third month I was building retrieval-augmented generation work on my own, and the project reviews are where I actually learnt to do it properly.
Harman Sethi
RAG Systems / Now working as an intern in the field
The classes are practical. Every session ends with a task that has to work, so you cannot fake understanding. That habit helped me most in interviews.
Ankit Verma
RAG Systems / BCA final year
Doubt support was the difference for me. My trainer sat with my work, found the mistake and made me fix it myself instead of handing over the answer.
Navjot Singh
RAG Systems / Career switch from operations
I came for the skill and left with a portfolio — projects I could actually demo on a call, not a certificate I had to explain.
Simranjeet Kaur
RAG Systems / B.Sc. IT graduate
I joined with no background in this. By the third month I was building retrieval-augmented generation work on my own, and the project reviews are where I actually learnt to do it properly.
Harman Sethi
RAG Systems / Now working as an intern in the field
The classes are practical. Every session ends with a task that has to work, so you cannot fake understanding. That habit helped me most in interviews.
Ankit Verma
RAG Systems / BCA final year
Doubt support was the difference for me. My trainer sat with my work, found the mistake and made me fix it myself instead of handing over the answer.
Navjot Singh
RAG Systems / Career switch from operations
I came for the skill and left with a portfolio — projects I could actually demo on a call, not a certificate I had to explain.
Real, unedited feedback from techcadd learners on Google.
Frequently asked questions
The 12 things people ask most often about the RAG Systems course at techcadd Ludhiana.
Python, and some experience calling an LLM API. If you have not built anything with a language model yet, take the Generative AI course first — it covers RAG at an introductory depth and makes this course land properly.
Generative AI covers RAG as one module among five. This course is that module taken to production depth: chunking strategy, hybrid search, reranking, metadata filtering, citation handling, refusal behaviour and a full evaluation harness on your own corpus.
FAISS for local work and Pinecone for managed, with LangChain and LlamaIndex for the pipeline, a reranker, RAGAS-style evaluation and FastAPI for serving. The concepts transfer to Weaviate, Qdrant, pgvector or whatever your employer already runs.
Yes, and we encourage it — several students build their capstone on their employer's or family business's document set, which is the fastest route from course to something that gets used. We will talk through what can and cannot be brought into a classroom setting.
6–10 weeks depending on batch. It is a focused specialisation rather than a broad programme, and the final phase is given over to building and measuring one system properly.
The complete RAG Systems programme runs for 6–8 weeks depending on the batch you choose. Weekday, weekend and fast-track options are available, along with shorter modules for students who only need the fundamentals.
School students after 12th, college students from any stream, graduates and working professionals changing track. The first module assumes no prior experience.
Python and some experience calling an LLM API. Take the Generative AI course first if either is new to you.
FAISS, Pinecone, LlamaIndex, LangChain, Claude API, RAGAS, Elasticsearch and FastAPI — plus the day-to-day tooling and workflow that surrounds them in a real team.
Yes. Each module closes with a lab project, and the course ends with a capstone you can put on your portfolio and defend in an interview.
Placement support includes resume and portfolio review, aptitude and role-specific practice, mock interviews and interview referrals through our hiring network.
Yes. You receive a techcadd RAG Systems completion certificate, and a separate project certificate for the capstone you submit.
Next batch
Have a document set you want an assistant over — a firm's records, a syllabus archive, a technical manual? Tell us about it and we will tell you what building it would actually involve.
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