DOCUMENT RETRIEVAL / SOURCE-GROUNDED ANSWERS / EVALUATION
RAG Training
Learn to build an assistant that answers from a defined collection of documents and shows its evidence. RAG Training focuses on the complete retrieval pipeline, from cleaning source files to testing citations, missing answers and document updates.
★ 4+ Google rating from 100+ reviews · 9,000+ students trained · 523+ batches completed · Teaching since 2021
Next batch: Monday, 2 November 2026
Course outline8 modules
Practice3 project scenarios
Duration2 months
Course feeOnline ₹20,000
Classroom ₹25,000
About the RAG Training
Retrieval-augmented generation combines a search step with a generation step. A system finds relevant passages and supplies them as context to a language model. This can improve access to specific information, but it does not guarantee a correct answer. The course treats document quality, retrieval quality and answer quality as separate engineering problems.
Choose this course for deeper work on knowledge retrieval, not for a general introduction to every Generative AI tool. LangChain and LlamaIndex may support lab implementations, but neither defines RAG itself. Model fine-tuning, broad agent orchestration and training a foundation model are outside the core learning path.
For product-specific terminology and behaviour, use Retrieval architecture. Match the documentation to the environment used in your exercises.
Benefits of Learning RAG
Turn the syllabus into practical advantages you can demonstrate in your own work. These outcomes come from building, checking and explaining the exercises.
Find the cause of weak answersSeparate missing evidence from poor retrieval and inaccurate generation instead of changing prompts at random.
Make citations usefulTrace important claims to a specific passage, document version and accessible source.
Compare retrieval strategiesMeasure keyword, vector and hybrid approaches against the same question set.
Handle changing knowledgePlan document updates and deletions so a knowledge assistant does not keep using obsolete material.
Respect source permissionsDesign retrieval so protected passages are filtered before they reach the model.
Explain quality with evidencePresent retrieval results, answer checks and known gaps in a reproducible evaluation report.
Who Should Join?
Basic Python, JSON and API knowledge are expected. You should be comfortable reading a small dataset and comparing expected with actual results. Some understanding of language models helps; advanced calculus is not required for the introductory retrieval exercises.
AI application developersImprove document-based assistants beyond a first demonstration.
Data and search engineersConnect ingestion, indexing and access-aware retrieval.
QA and evaluation practitionersInvestigate whether failures come from search, context or generation.
For complementary learning, explore Generative AI Training. Choose the broader program if you need an introduction to AI models, prompting and the wider application landscape before specialising.
RAG Prerequisites and Readiness Checklist
Use these three checks to decide whether you are ready to start or need a short foundation step first. Bring any gaps to your demo discussion.
Python and recordsLoad a small list of documents, keep their identifiers and inspect the fields in a JSON record.
Evidence and expected answersChoose a permitted source collection. Write five questions with supporting passages and two questions that the collection cannot answer.
Search workspaceCheck access to the selected embedding model and index. Keep a keyword-search baseline so you can make a meaningful comparison.
RAG Training Syllabus: 8 Practical Modules
Each module connects a concept to an exercise and an output you can explain. Work through the foundations before attempting the integrated project.
1. RAG architecture and baselines
Draw the path from a user question to a retrieved passage and generated answer. Build a simple keyword-search baseline first. Define a small set of questions with known evidence, including questions the collection cannot answer.
2. Document ingestion and provenance
Load permitted text and PDF material while retaining document IDs, dates, headings and page references. Inspect tables and extraction errors before indexing. Record source ownership and document versions so a citation can be traced back to its origin.
3. Chunking and metadata
Compare fixed-size and structure-aware chunks on the same collection. Keep useful headings and source metadata with each chunk. Test whether an answer requires information split across chunks rather than assuming a single chunk size is always best.
4. Embeddings and vector indexing
Create embeddings and index them in a selected vector store. Understand similarity scores, dimensionality and model compatibility. Demonstrate why changing an embedding model can require rebuilding an index, and document how duplicate records are handled.
5. Hybrid retrieval and reranking
Compare keyword, vector and combined retrieval on labelled questions. Use metadata filters and a reranker where useful. Measure whether the relevant evidence reaches the final context; more retrieved passages are not automatically better.
6. Grounded answers and citations
Construct context with clear source boundaries. Require the assistant to identify evidence and state when the sources do not answer a question. Check each important claim against its cited passage and handle disagreement between documents explicitly.
7. Evaluation and access controls
Evaluate retrieval separately from generation. Include paraphrases, stale documents, missing answers and permission boundaries in the test set. Treat instructions inside documents as untrusted content and enforce document access before supplying context to a model.
8. Updates, monitoring and capstone
Design ingestion updates, deletion handling and repeatable regression checks. Track quality, latency and usage without retaining unnecessary private content. Deliver a document assistant with an evaluation report and a runbook explaining known failure modes.
Discuss Your Learning Goals
Tell the team about your experience and preferred schedule. Book a free demo to discuss the course, lab access and the practical work expected from you.
Practical Skills You Will Develop
Use the exercises to build an explanation as well as a result. You should be able to show what you did, how you checked it and where the limits are.
Evidence-aware ingestionPreserve source identity and detect extraction problems before indexing.
Retrieval diagnosisTrace a weak answer back to missing or poorly ranked source material.
Index maintenancePlan re-indexing, updates and deletion rather than treating ingestion as a one-time task.
Citation checkingVerify that an accessible passage supports the actual answer claim.
Access-aware searchApply user permissions before protected passages reach a model.
Evaluation designCreate representative questions and report component-level results.
Tools and Lab Requirements
Use a personal practice environment with synthetic or openly licensed data. Keep credentials outside notebooks and source control. Confirm software access, hardware and any paid API usage with the course team before enrolling.
Document processingPython loaders and parsers appropriate to the permitted source files.
Retrieval stackAn embedding model, a vector store and a keyword baseline selected for the lab.
Evaluation workspaceA labelled question set, source references and a repeatable result sheet.
Consult LlamaIndex RAG documentation during practice. Use documentation matching the installed version and check applicable permissions and service costs.
Your Learning Roadmap
Build confidence in stages. Practice time and your starting knowledge matter as much as the number of scheduled sessions.
1. Set up and understandCheck the prerequisites and lab access. Complete the first two modules and explain the basic workflow in your own words.
2. Build and investigateWork through the middle modules. Keep notes of errors, what you tried and the evidence that supported a fix.
3. Test and presentComplete the final modules and an integrated scenario. Present your output, validation and a short handover guide.
Your RAG project milestone: Include a source manifest, labelled question set, retrieval comparison, citation checks and a document-update test. Use the existing syllabus checklist to track progress; follow the stages during the 2-month course and confirm assignment dates with your trainer.
3 Practical Project Scenarios
These are teaching scenarios using approved lab systems and synthetic data. They are not claims about live client work or previous student results.
Policy knowledge assistantUse a fictional handbook with dated revisions. Answer policy questions with passage references and decline questions outside the collection.
Product documentation searchCompare keyword, vector and hybrid retrieval for technical queries. Record the cases where exact product identifiers matter more than semantic similarity.
Permission-aware helpdesk searchCreate two synthetic user groups with different source access. Demonstrate that restricted documents and citation metadata are not revealed to the wrong group.
How the Skills Work in Practice
A fictional returns handbook contains a 14-day policy, while an archived version says 30 days. A customer question retrieves both. First diagnose whether the archive should have been filtered out. Then check how the answer handles dates and whether its citation supports the chosen rule.
The exercise separates three possible fixes: better document metadata, improved retrieval filtering and clearer answer instructions. Re-run the same questions after each change so you can identify which modification helped rather than crediting a longer prompt for everything.
Why Learn with Brolly Academy?
Choose a course that connects the subject to work you can actually demonstrate. Use a demo to discuss the learning path and decide whether it fits your starting point.
A connected learning pathThe eight-module outline moves from foundations to an integrated task. Each module identifies an output rather than leaving practice as a vague promise.
Discuss your own learning goalShare your experience and preferred format with the course team. Use a demo to assess whether the technical depth, pace and project expectations fit your needs.
Clear course boundariesThe page explains prerequisites, product scope and the difference between training completion and independent certification. You can compare the offer with your actual requirements.
Compare Brolly Academy with Other Training Institutes
Compare the same evidence from each provider: what you will build, what you will test and which services the fee includes. Ask for specific examples rather than relying on broad claims about advanced AI.
What to discuss with Brolly AcademyReview these eight modules and the three project scenarios.
Ask for the assigned trainer profile.
Check the lab version and access period.
Review the fee, schedule and certificate terms.
Use the demo to assess the teaching approach.
What to check with any instituteRequest a detailed outline, not just product names.
Verify the trainer information supplied.
Separate demonstrations from your own lab work.
Check licences, exam costs and refund conditions.
Compare the total commitment, not only the advertised price.
Classroom, Online and Corporate Learning
Discuss the currently available delivery format with the academy before booking. Batch availability and session timings should be agreed in writing.
Classroom learning enquiryConfirm the venue, next available batch and computer requirements before arranging travel.
Live online enquiryCheck the session time zone, remote lab access and arrangements for asking questions or catching up on a missed session.
Corporate team enquiryShare your team size, current tools and learning objectives. Agree a scoped program without sharing confidential business data.
For a team program, explore Brolly Academy corporate training.
RAG Training Fee and Duration
The RAG Training runs for 2 months. The fee is ₹20,000 for online training or ₹25,000 for classroom (offline) training. Ask for a written quotation confirming taxes, payment and refund terms, lab access and any separate software or API charges.
Classroom training: ₹25,000Learn in the classroom over 2 months. Confirm the venue, batch start date and session timings with the academy before arranging travel.
Online training: ₹20,000Choose online learning over 2 months. Confirm the session time zone, lab access and arrangements for questions or missed sessions before enrolling.
Duration: 2 monthsPlan time for lessons, independent practice and the course project. Ask for the batch calendar, instructor-led hours, assignment deadlines and support period.
Course Completion and Certification Guidance
Brolly Academy course completion recognizes the training and assessment work agreed for your batch. It is separate from a credential awarded by a software vendor or another certification body.
Brolly Academy course completionAsk for the attendance, assignment and assessment requirements, the certificate wording and how completion will be verified.
Independent certificationUse official vendor information to check whether a relevant credential is currently offered and what preparation it requires. No vendor authorization, exam voucher or pass guarantee is implied.
Career Roles and Skill Applications
Match the learning path to a role, then compare its requirements with your existing experience. The course can support skill development; it does not guarantee employment.
RAG application developerBuild and maintain source-grounded assistants.
Search and retrieval engineerImprove evidence selection and indexing behaviour.
AI evaluation engineerMeasure retrieval and answer quality with reproducible tests.
You can also explore MLOps Training. Extend a working prototype into a monitored, repeatable deployment workflow.
Where These Skills Are Used
RAG can support internal documentation, product help centres, research collections and policy lookup. It is unsuitable as a promise of perfect truth: access rules, freshness and source quality remain essential. Sensitive applications need qualified review appropriate to their use.
Interview and Portfolio Preparation
Prepare evidence you can discuss clearly. Label practice work as training work and do not present a teaching scenario as paid client experience.
Explain a projectDescribe the requirement, your decisions, the result and one limitation. Be ready to answer what you would change for a real deployment.
Show useful evidenceKeep approved files, test results and a concise README or runbook. Remove secrets and private information before sharing a portfolio.
Discuss support servicesAsk the academy which resume, mock-interview or job-search services are included in your batch. Placement assistance is not a job or salary guarantee.
Placement Preparation and Career Support
Use this checklist during your demo to agree the career-support services included with your batch. The preparation below builds on your course project; recruitment and interview outcomes remain with the employer.
Choose a target roleStart with the responsibilities of a RAG application developer. Compare several relevant job descriptions and note the skills you can already prove.
Prepare a project evidence packInclude a source manifest, labelled question set, retrieval comparison, citation checks and a document-update test.
Practise a technical discussionHow would you tell whether an incorrect answer came from chunking, ranking or generation? Explain your decision with a test or project example.
Write an honest resume entryState the project goal, your contribution and the checks you ran. Label it as a learning project and report only results you actually measured.
Request focused feedbackAsk whether the batch includes a resume review, portfolio review or mock interview. Agree the format, number of reviews and support period before enrolling.
Plan applications and follow-upKeep a record of role requirements, applications and feedback. Use the next practice session to address a specific gap rather than adding unsupported claims to your profile.
Career Planning and Salary Expectations
Salary depends on the role, prior experience, location and the employer. A tool course alone does not establish a salary band. Compare recent job descriptions, required experience and responsibilities rather than relying on a headline income promise.
Meet Your Trainer: Dinesh
Dinesh is the trainer for the RAG Training. Use a demo to discuss your learning goals, ask about a practical RAG scenario and understand how project feedback is provided.
Relevant experienceAsk for examples of work with RAG that can be discussed without revealing confidential client information.
Practical explanationAsk the trainer to explain a failure scenario and how a learner would investigate it, rather than only showing a finished result.
Feedback and supportDiscuss how assignments are reviewed, how questions are handled and the support period included in the course.
Brolly Academy Learner Feedback
Read available academy feedback and ask whether a review relates to this particular course, trainer and delivery format. General academy reviews should not be mistaken for verified results from this new course.
The excerpts below are published on Brolly Academy's academy reviews page. They describe Data Science and Machine Learning courses, not this new RAG course.
Aditya - Data Science"The instructors provided a clear and practical understanding of data science concepts."
Read the original academy testimonial
Kavita Singh - Data Science"The hands-on projects helped me apply the concepts effectively."
Read the original academy testimonial
Rahul Desai - Machine Learning"The trainers were experienced professionals who explained complex algorithms with ease."
Read the original academy testimonial
Student Community and Continued Learning
Keep learning visible: ask precise questions, share permitted examples and record what changed after feedback. Check with the course team about any batch-specific discussion group or mentor access.
Academy updatesFollow the Brolly Academy WhatsApp channel for public academy updates. A public channel is separate from individual technical support.
RAG peer-practice ideaCompare retrieval results on the same permitted documents and explain one case where keyword search beats vector search.
Share safely and ask clearlyInclude the tool version, a small permitted example, the expected result and what happened. Remove API keys, employer documents and personal data before asking for help.
For continued technical learning, use Retrieval architecture. Recheck examples against the version you are using.
Download Your Practice Checklists
Use these editable CSV files to organize your learning. They open in common spreadsheet tools and contain real module and project-checklist content.
Download Syllabus (CSV) Project Checklist (CSV)Official Documentation for Further Reading
Use the documentation that matches your product version and environment. These sources support technical learning; linking to them does not imply endorsement of Brolly Academy.
Retrieval architecture
LlamaIndex RAG documentation
Haystack retrieval pipelines
Related Courses at Brolly Academy
Choose complementary learning based on your current skill gaps. These are separate courses, not automatically included in this program.
Generative AI TrainingChoose the broader program if you need an introduction to AI models, prompting and the wider application landscape before specialising.
View Generative AI Training
AI Testing TrainingDevelop a deeper QA practice for AI answers, tool behaviour and release decisions.
View AI Testing Training
MLOps TrainingExtend a working prototype into a monitored, repeatable deployment workflow.
View MLOps Training
To specialise in document ingestion, indexing and query pipelines, explore the LlamaIndex Course.
For application code that connects retrieval with model calls and tools, see the LangChain Course.
To trace retrieval failures and compare answer quality across experiments, continue with the LangSmith Course.
Talk to the RAG Course Team
Call +91 81868 44555 or send a course enquiry to discuss the syllabus and current batch options. For a classroom visit, confirm the venue and appointment with Brolly Academy before travelling.
Read our contact details for the academy location and enquiry options. Online delivery, session time zones and tool access should be confirmed for your batch.
Call +91 81868 44555RAG Training FAQs
Can I learn RAG without LangChain?
Yes. RAG is an architecture, not a single library. You can implement its components directly or use a framework. The course explains the pipeline before relying on framework shortcuts.
Does RAG remove hallucinations?
No. It can supply useful evidence, but retrieval may miss the right passage and generation can still misstate it. Source checks, missing-answer tests and evaluation remain necessary.
Is RAG the same as fine-tuning?
No. RAG retrieves information at application time. Fine-tuning changes model parameters through additional training. The right choice depends on the problem, data, cost and evaluation results.
Do all RAG systems need a vector database?
No. Retrieval may use keyword search, SQL, structured filters or other methods. Vector search is useful for many semantic queries, but the baseline should fit the information and question type.
What is the main capstone output?
A working document assistant, a documented source collection, a question-and-evidence evaluation set and a failure analysis. A chatbot screenshot by itself is not enough to explain retrieval quality.
Will this cover advanced RAG?
The outline includes hybrid retrieval, reranking, metadata, source updates and permission checks. More specialised graph or multimodal retrieval should be agreed as an extension, not assumed to be included.
How much does the RAG Training cost?
The RAG Training fee is ₹20,000 for online training and ₹25,000 for classroom (offline) training. Confirm taxes, payment and refund terms, lab access and any separate software, API or examination costs in your written quotation.
What is the course duration and batch schedule?
The course duration is 2 months for both online and classroom training. Contact Brolly Academy for the next batch start date, session timings, instructor-led hours and independent practice expectations.
Can I attend a free demo before enrolling?
Use Book Free Demo to open the enquiry form, or contact the team on WhatsApp. Share your experience and learning goal so the counsellor can discuss a suitable session and current availability.
What certificate will I receive?
Discuss the Brolly Academy course-completion requirements for your batch, including attendance, assignments and project assessment. Course completion is separate from any credential issued by a software vendor or independent examination body.
Does the course guarantee a job or salary?
No. The learning path is designed to build demonstrable skills. Ask which portfolio, resume or interview-support services are included. Hiring decisions depend on your wider experience, preparation, location and employer requirements.
How should I compare this course with another institute?
Compare the actual syllabus, your own lab work, trainer experience, project feedback, tool costs and written terms. Ask each provider the same questions and use a demo to assess the teaching style before committing.
Got More Questions About RAG?
Talk to the Brolly Academy team about your starting skills, learning goal and available batch options. Request a free demo before choosing your course.