DATA CONNECTORS / DOCUMENT INDEXING / QUERY ENGINES
LlamaIndex Course
Turn a collection of documents into a queryable knowledge application. This LlamaIndex Course focuses on loading data, preserving document structure, building an index and inspecting how a question becomes a source-backed response.
★ 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 LlamaIndex Course
LlamaIndex provides components for working with data in LLM applications. The central problem is connecting useful information to an application without losing its source or structure. You examine documents, nodes, metadata, indexes, retrieval and response construction through a series of small Python exercises. The final application must make its data path understandable.
This course is about the LlamaIndex implementation, not a broad tour of Generative AI. RAG Training compares retrieval approaches independently of a framework. Here, you spend more time on LlamaIndex data structures, connectors, query components and workflow integration. It is not a vector database course or foundation-model training program.
For product-specific terminology and behaviour, use LlamaIndex framework. Match the documentation to the environment used in your exercises.
Benefits of Learning LlamaIndex
Turn the syllabus into practical advantages you can demonstrate in your own work. These outcomes come from building, checking and explaining the exercises.
Connect data to a useful queryFollow the complete path from an original source to an answer with supporting evidence.
Keep source history visibleRetain identifiers and metadata so a result can be checked against the original document.
Choose retrieval components deliberatelyCompare the effect of parsing, indexing and retrieval choices rather than accepting default behaviour without testing.
Manage changing documentsPlan refresh and deletion checks when a source is replaced or removed.
Investigate parsing failuresRecognise when a missing table or damaged extraction is the real reason for a weak answer.
Deliver a maintainable projectDocument ingestion steps, evaluation cases and limitations for the next person operating the assistant.
Who Should Join?
Basic Python, file handling and API familiarity are expected. You should be able to inspect a JSON object and understand why a source document needs an identifier. Prior exposure to RAG is helpful but a small retrieval introduction is included.
Python developersBuild knowledge applications around a defined document collection.
Data engineersConnect ingestion and metadata quality with useful retrieval.
RAG practitionersLearn the LlamaIndex components behind an existing prototype.
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.
LlamaIndex 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 source filesRead structured records and text files while preserving a stable identifier for each source.
Document awarenessInspect headings, tables and extraction errors in a permitted document before treating its text as reliable input.
Indexing workspacePrepare the selected parser, embedding endpoint and storage environment. Confirm access and costs before processing a collection.
LlamaIndex Course 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. Environment and data flow
Set up a pinned Python environment and a permitted model provider. Draw the flow from a source file to an answer. Build the smallest possible example and inspect the intermediate data instead of relying only on the final response.
2. Connectors and document loading
Load local documents with an appropriate reader. Preserve file identity, ownership and version information. Compare extracted text with the original source, identifying missing tables, repeated headers and empty files before indexing.
3. Nodes, metadata and transformations
Split documents into useful nodes and attach metadata for later filtering. Apply transformations in a deliberate order. Record which source and section produced each node, then test whether that structure survives a saved-and-reloaded pipeline.
4. Indexes and storage
Create an index and connect it to suitable storage. Separate the document store, index structure and vector storage concepts. Practise adding, updating and removing source records, documenting when an embedding or index rebuild is necessary.
5. Retrievers and query engines
Inspect retrieved nodes before response generation. Configure a retriever and query engine for the task. Compare results for a precise identifier and a paraphrased question, and explain which component should change when the wrong evidence is selected.
6. Response synthesis and structured extraction
Generate answers from selected nodes with source references. Build a small structured extraction task with validation. Handle absent fields explicitly and ensure that a valid output structure is not mistaken for proof that the source supports its content.
7. Evaluation and data permissions
Use labelled questions to inspect retrieval and response quality separately. Add stale-source and missing-answer cases. Apply document permissions before model context is assembled and avoid retaining private source text in unnecessary traces.
8. Knowledge application capstone
Package the ingestion and query paths with a small user interface or API. Include index-refresh instructions, a source manifest and an evaluation sheet. Present a normal query, a source update and a question the collection cannot answer.
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.
Data connectionSelect readers and preserve useful document context.
Node inspectionUnderstand the information passed from ingestion to retrieval.
Index lifecyclePlan updates and deletions as part of the application.
Query debuggingInspect intermediate evidence before changing a prompt.
Structured extractionValidate extracted fields and retain supporting source references.
Reproducible deliveryProvide setup, source and evaluation records with the project.
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.
LlamaIndex Python packagesInstall only the core and integration packages used by the lab.
Approved document collectionUse synthetic or openly licensed sources with a source manifest.
Storage and model accessA lab vector store and permitted embedding and generation endpoints.
Consult LlamaIndex 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 LlamaIndex project milestone: Include an ingestion manifest, node metadata, retrieval examples, source-refresh results and an evaluation notebook. 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.
Documentation Q&A serviceIndex a fictional software handbook and return answers with source sections. Track which questions fail because of missing content.
Structured document extractorExtract agreed fields from a small collection of synthetic reports. Keep absent values empty and show the supporting passage for each extracted field.
Refreshable knowledge indexBuild ingestion that recognises updated and removed documents. Compare retrieval before and after a source revision.
How the Skills Work in Practice
A collection of fictional equipment manuals includes repeated page headers and a table of maintenance intervals. Inspect the extracted text before building the index. If the interval loses its row label, no clever answer prompt can reliably restore the original meaning.
Correct the extraction or preserve a better structured representation, then re-index and re-run the same questions. The project report should show the source issue, the ingestion change and the resulting evidence, not simply claim that the chatbot became more accurate.
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.
LlamaIndex Course Fee and Duration
The LlamaIndex Course 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.
Knowledge application developerBuild data-connected assistants and extraction workflows.
RAG developerImplement and maintain a specific retrieval stack.
AI data integration engineerConnect source systems with application-ready evidence.
You can also explore MLOps Training. Extend a working prototype into a monitored, repeatable deployment workflow.
Where These Skills Are Used
Document-heavy workflows include technical documentation, internal process lookup and structured report extraction. Successful delivery depends on source quality and access permissions as much as the query interface. LlamaIndex supports the implementation but cannot repair every source-data problem automatically.
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 Knowledge application developer. Compare several relevant job descriptions and note the skills you can already prove.
Prepare a project evidence packInclude an ingestion manifest, node metadata, retrieval examples, source-refresh results and an evaluation notebook.
Practise a technical discussionHow would you investigate an answer that cites the right PDF but misses a value in its table? 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 LlamaIndex Course. Use a demo to discuss your learning goals, ask about a practical LlamaIndex scenario and understand how project feedback is provided.
Relevant experienceAsk for examples of work with LlamaIndex 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 LlamaIndex 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.
LlamaIndex peer-practice ideaProcess the same document with two parsing approaches and compare which headings, tables and source references survive.
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 LlamaIndex framework. 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.
LlamaIndex framework
LlamaIndex documentation
Retrieval architecture reference
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
For a framework-independent understanding of retrieval design and grounded-answer evaluation, explore RAG Training.
To broaden your application-building practice beyond data indexing and query pipelines, see the LangChain Course.
If your next step is using managed foundation models and knowledge workflows on AWS, explore the Amazon Bedrock Course.
Talk to the LlamaIndex 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 44555LlamaIndex Course FAQs
Is LlamaIndex a database?
No. It provides application components for data ingestion, indexing, retrieval and other LLM workflows. It can integrate with databases and vector stores, but it is not interchangeable with them.
Is LlamaIndex only for RAG?
No. Its ecosystem also supports workflows, agents and structured extraction. This course deliberately concentrates on data-connected applications so that its scope remains different from a general agent course.
How is this different from LangChain?
The syllabus starts from documents, nodes, indexes and query components. The LangChain Course starts from model interfaces, tools and application composition. There is overlap in capability, but the project goals and teaching emphasis differ.
Can it process every PDF correctly?
No parser handles every document perfectly. Scans, tables and unusual layouts need inspection and sometimes additional extraction tooling. You will check the source-to-text step rather than assuming it is lossless.
Do I need a paid cloud service?
Not every exercise requires one. The final lab stack and any model, parsing or hosting charges must be confirmed for the batch. Open-source tooling does not mean every connected service is free.
What should I show in a portfolio?
Show the ingestion design, source manifest, retrieval examples, update behaviour and evaluation results. Include known limitations and remove confidential material before sharing.
How much does the LlamaIndex Course cost?
The LlamaIndex Course 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 LlamaIndex?
Talk to the Brolly Academy team about your starting skills, learning goal and available batch options. Request a free demo before choosing your course.