Natural Language Processing (NLP)
NLP covers the full arc of language processing: from tokenisation and classical statistical methods through word embeddings to transformer-based models.
2 months Classroom & Online
View syllabus 6 modules
AI & Advanced Technologies
Large Language Models (LLM)
Build with language models, properly.
This track treats large language models as engineering material rather than magic. You learn how they are trained, why they fail the way they do, and how to build reliable products on top of something fundamentally probabilistic.
2 months Classroom & Online Intermediate to Advanced 6 modules
LLM Build with language models, properly.
Overview
What this course is, plainly
This track treats large language models as engineering material rather than magic. You learn how they are trained, why they fail the way they do, and how to build reliable products on top of something fundamentally probabilistic.
You work with both hosted APIs and open-weight models, write structured prompts, add tool calling, evaluate output systematically, fine-tune with LoRA, and control cost and latency the way a production team has to.
Why choose this course
- Hosted APIs and open-weight models, both
- Structured prompting and tool calling
- Parameter-efficient fine-tuning with LoRA
- Systematic evaluation instead of vibes
- Cost, latency and safety engineering
Curriculum
Module-by-module syllabus
The same structure on every Xypron course page, so you can compare tracks like for like. Each module ends in working code, not a quiz.
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Module 1: How LLMs Work
The mechanics under the API call.
- Transformer decoder architecture
- Tokenisation & context windows
- Pretraining, instruction tuning & RLHF
- Sampling: temperature, top-p, greedy decoding
- Why models hallucinate
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Module 2: Prompt Engineering
The highest-leverage skill in the whole discipline.
- System, user & assistant roles
- Few-shot and chain-of-thought prompting
- Structured output & JSON mode
- Prompt templating & versioning
- Prompt injection & defensive design
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Module 3: Building LLM Applications
From a single call to an actual product.
- Working with the OpenAI and Anthropic APIs
- Streaming responses & partial rendering
- Tool / function calling
- Conversation state & memory
- LangChain and framework tradeoffs
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Module 4: Open-Weight Models
Running your own model when data cannot leave the building.
- Llama, Mistral & Gemma model families
- Local inference with Ollama & llama.cpp
- Quantisation: GGUF, 4-bit & 8-bit
- Hardware sizing & throughput
- Self-hosting tradeoffs vs. API
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Module 5: Fine-Tuning
Adapting a model to a domain without retraining it.
- When to fine-tune vs. prompt vs. retrieve
- Dataset construction & formatting
- LoRA & QLoRA parameter-efficient tuning
- Training runs & overfitting signals
- Evaluating a fine-tuned model
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Module 6: Evaluation, Cost & Safety
The operations layer of an LLM product.
- Building an evaluation set
- LLM-as-judge and human review loops
- Token accounting & cost control
- Caching, batching & latency reduction
- Guardrails, moderation & end-to-end capstone project
Projects you'll build
- Domain-specific assistant with tool calling
- LoRA fine-tune on a custom instruction dataset
- Evaluation harness comparing three models on one task
Roles this prepares you for
- LLM Engineer
- AI Engineer
- Applied AI Developer
- ML Engineer — Generative
Tools you'll master
Python Hugging Face LangChain Ollama PyTorch PEFT / LoRA OpenAI & Anthropic APIs Outcomes
What happens after the last module
The course does not end at the capstone. Placement support runs from your first mock interview until you have an offer — and it is support, not a guarantee.
Resume rewrite
Rebuilt around the projects you actually shipped here, targeted at the roles you are applying for.
Mock interviews
Technical and HR rounds with written feedback after each one, repeated until the gaps close.
Profile sharing
Your profile goes to our hiring partners with a note from the trainer who taught you.
Interview scheduling
We book the interviews and follow through until there is a decision either way.
Next step
Ask us anything about Large Language Models (LLM)
Batch timings, the fee, whether your background fits, what the trainer's day job is — all fair questions. A counsellor will call you back within two business hours.
Python fluency and comfort with APIs. Prior NLP or deep learning exposure is an advantage but not mandatory.
The course runs for 2 months in classroom & online mode. Weekday, weekend and evening batches are scheduled continuously — call +91 73493 01293 for the next available start date.
Domain-specific assistant with tool calling; LoRA fine-tune on a custom instruction dataset; Evaluation harness comparing three models on one task — plus an end-to-end capstone project you deploy yourself and can walk an interviewer through line by line.
Yes. Sit a full session with the trainer who would actually teach your batch before you decide anything. Ask for one on WhatsApp or through the enquiry form on this page.
Enquiry
Enquire about Large Language Models (LLM)
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Also in AI & Advanced Technologies
Compare with the neighbouring tracks
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Artificial Intelligence (AI)
The Artificial Intelligence program is the broad foundation track for the AI pillar. It covers the field as a whole — search and reasoning, machine learning, neural networks, computer vision, language, and the ethics and deployment questions that come with all of it.
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Next step
Ready to start Large Language Models (LLM)?
Sit a free demo class before you commit. Ask about batch timings, the trainer, and exactly what the placement support covers.