About Course
About LLM Application Development Training
Chunking, embeddings, reranking and streaming — the retrieval layer that decides whether a RAG app answers or apologises.
The difficult half of an LLM application is not calling the model, it is the retrieval sitting in front of it: what gets chunked, how it is embedded, whether search is lexical, vector or both, and how anything is measured when there is no single correct answer. Teams discover this after a demo succeeds and production answers are wrong in ways nobody can localise. This course separates the two problems explicitly — retrieval quality and generation quality are scored independently — because you cannot fix what you have blurred into one number.
Course Overview
12-week LLM Application Development training (72 hours) covering RAG, chunking and embeddings, vector databases, reranking, function calling, streaming and production evaluation. Classroom, Live Online and Hybrid.
Why Learn LLM Application Development Training
Employers interview for evidence, and a chat window is not evidence. Candidates who bring a retrieval evaluation harness, a chunking rationale written down and a traced request showing where latency actually goes are competing for a different class of role than candidates who have built a demo. Applied AI is one of the few areas where a portfolio answer can settle an interview on its own, because the work is legible to a hiring engineer in two minutes.
It also teaches the software engineering the model does not supply. Streaming responses so a page feels live, tracing a request end to end, validating tool arguments against a schema, retrying safely when a call is not idempotent, and handling a source that disappeared between retrieval and answer. These are the failure modes that decide whether an application survives contact with real users, and none of them are visible in a tutorial.
Who Should Join This Course
- Backend Developers — Adding a retrieval-backed answer to an existing product
- Data Engineers — Moving from pipelines into the serving layer
- Full-Stack Developers — Building a chat or search interface over company documents
- Machine Learning Practitioners — Moving from training notebooks to deployed apps
- Fresh Graduates — Targeting the generative AI engineering track
- Solution Architects — Designing an LLM feature that has to survive production
Prerequisites
Working Python is required: functions, dictionaries, JSON, basic error handling and comfort reading someone else's code. Some exposure to SQL or an API integration helps in the vector store material. No machine learning or deep learning background is assumed, and the embedding and evaluation theory is taught from first principles.
Learning Objectives
- Design a RAG pipeline whose chunking and retrieval you can justify
- Choose and configure a vector database for the scale and latency you need
- Improve answer quality by fixing retrieval before touching the prompt
- Implement function calling and streaming for a responsive interface
- Build a retrieval evaluation set and measure recall independent of the model
- Control cost and latency with caching, model routing and prompt budgets
- Add guardrails in code for injection, refusal and citation enforcement
- Ship a documented, monitored LLM application with a rollback path
What the Syllabus Covers
Each module is assessed against published LLM Application Developer outcomes rather than on memorisation, covering RAG, Vector Databases, Embeddings, Function Calling, Streaming, Retrieval Evaluation together with a chunking strategy argued from the corpus, a hybrid search and reranking pipeline, function calling with validated schemas, and a retrieval evaluation suite that reports recall before it reports answer quality.
Career Opportunities After This Programme
- LLM Application Developer
- Generative AI Engineer
- AI Software Engineer
- RAG Engineer
- Applied AI Developer
- Solutions Engineer (GenAI)
Hands-on Practice and Capstone
Capstone: Ingest a real document corpus with structure-aware chunking, build hybrid retrieval with a reranking step and measure recall against a gold set, generate answers with mandatory citations and a refusal path, add a function-calling tool and stream the response, then instrument tracing, cache the hot path and ship with a retrieval eval suite, runbook and rollback procedure.
Production scenario: The demo answers beautifully on three curated documents and terribly on the real share. The build here starts by proving which half is broken: retrieval metrics show the right chunk was never fetched, so the fix is chunking, hybrid search and a reranking pass rather than a cleverer prompt, and the eval suite is written so that claim can be repeated after every index change.
Course Duration, Mode and Certification
The course runs for 12 weeks (72 hours), three sessions a week of two hours each. Modes: Classroom / Live Online / Hybrid. Certificate issued: SkilBrill Course Completion Certificate. Assessment is against published LLM Application Developer outcomes, and you finish with evidence you can show rather than a pass mark.
Why Train at SkilBrill
SkilBrill Training Institute in Thoraipakkam focuses on making you employable. Trainers are working AI, data and platform engineers, every module produces portfolio evidence rather than a completion badge, and the placement cell connects eligible candidates with hiring companies. Fee: ₹55,000 – ₹85,000. Enquire on +91 8610964691.
Career and Job Support
- Resume assistance tailored to AI, data and platform engineering roles
- LinkedIn profile rewrite and recruiter outreach
- Mock interviews covering evaluation, pipeline design and system-review scenarios
- Capstone walkthrough and portfolio review preparation
- Placement assistance with 200+ hiring partners
- Typical salary band after completion: ₹8 – 22 LPA
Frequently Asked Questions
What does LLM Application Development training at SkilBrill cover?
Twelve weeks and 72 hours across eight modules: the shape of an LLM application, retrieval and chunking, embeddings and vector stores, generation with tools and structured output, production engineering, retrieval evaluation, applied practice, and a full RAG capstone.
Do I need to know Python?
Yes, working Python. You will write and read application code, so you should be comfortable with functions, dictionaries, JSON and asynchronous requests. No machine learning background is required, and no prior LLM experience is assumed.
How is this different from the Generative AI course?
Generative AI Training is the practitioner and governance side: prompting, evaluation, safety and cost, and it needs no code. This course is the engineering side and builds the application itself. Most people take Generative AI first if they are new to the field, then move to this one to build.
What is RAG and why is it the focus?
Retrieval-augmented generation fetches relevant documents first and hands them to the model, so answers can cite real sources instead of relying on training data. It is the dominant pattern for building on private company content, and retrieval quality is where most RAG projects fail.
Is certification included?
The syllabus follows LLM application developer objectives, so the course doubles as preparation. Vendor credentials are booked through their own programmes, and the SkilBrill completion certificate is issued by us on the capstone.
Which frameworks and vector stores are used?
The course is deliberately not locked to one framework. You work with a mainstream Python SDK for the model calls and build the retrieval layer yourself, then use at least two vector stores so the trade-offs are visible rather than assumed.
What do I leave with?
A working retrieval application with citations, streaming, a function-calling tool, tracing, a retrieval evaluation suite, and the runbook and rollback procedure needed to hand it to a team that has to operate it.