Large Language Models
Develop the skills to compare language-model behaviour on a fixed benchmark through a learning path centred on Large Language Models. Explore concepts, guided practice and an applied project.
Build practical Large Language Models skills.
Skills and knowledge developed in this course.
- Transformer foundations
- Inference behaviour
- Adaptation choices
- Model assessment
- Applied project: Compare language-model behaviour on a fixed benchmark
- Large Language Models — validation and review
About this course
This course develops practical capability in Large Language Models. Its course-specific content covers Transformer foundations, Inference behaviour, Adaptation choices, Model assessment, followed by an applied project focused on compare language-model behaviour on a fixed benchmark. The lessons include concrete configuration, design, testing or operational decisions appropriate to the subject.
Who this is for
Administrators, developers and practitioners who work with Large Language Models or plan to take on responsibilities in this area.
Before you start
Basic computer literacy and a foundational understanding of artificial intelligence & generative ai. Prior experience with the named product helps with the practical exercises.
Compare language-model behaviour on a fixed benchmark
Deliver compare language-model behaviour on a fixed benchmark. Use the module outcomes to state requirements, demonstrate a working result, test an expected failure or boundary case, and document assumptions and follow-up work.
Course content
Course-specific syllabus and practical exercises. Lab release, access arrangements and final delivery scope are confirmed before enrolment.
01Transformer foundations
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- Attention, tokenisation and embeddings
- Pretraining and instruction tuning
Practical exerciseApply transformer foundations to the compare language-model behaviour on a fixed benchmark and capture the validation evidence.
02Inference behaviour
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- Sampling controls and context windows
- Latency, throughput and token budgets
Practical exerciseApply inference behaviour to the compare language-model behaviour on a fixed benchmark and capture the validation evidence.
03Adaptation choices
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- Prompting, retrieval and fine-tuning
- Model selection and deployment constraints
Practical exerciseApply adaptation choices to the compare language-model behaviour on a fixed benchmark and capture the validation evidence.
04Model assessment
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- Task-specific benchmarks and error analysis
- Robustness, privacy and usage monitoring
Practical exerciseApply model assessment to the compare language-model behaviour on a fixed benchmark and capture the validation evidence.
05Applied project: Compare language-model behaviour on a fixed benchmark
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- Define inputs, assumptions and acceptance criteria for: Compare language-model behaviour on a fixed benchmark.
- Implement the project using transformer foundations, inference behaviour, adaptation choices, model assessment.
Practical exerciseCompare language-model behaviour on a fixed benchmark.
06Large Language Models — validation and review
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- Demonstrate the completed project against its acceptance criteria.
- Exercise an invalid-input or failure scenario and document the observed result.
Practical exercisepresent the project evidence, limitations and a repeatable handover.
Technical references
These primary vendor, project or standards sources inform this independent IT Purview outline. Reference pages checked 25 October 2026; current provider documentation and exam objectives take precedence when versions change.
From understanding to application.
Progress by learning milestone. The schedule is agreed around your starting level and learning goals.
- 01Learning milestone 1 In progress
Set the context
Transformer foundations · Inference behaviour
Establish the Large Language Models scope, baseline concepts and operating assumptions. - 02Learning milestone 2 In progress
Build the working skills
Adaptation choices · Model assessment
Apply the core Large Language Models techniques in the course lab and record the result. - 03Learning milestone 3 In progress
Validate and handle exceptions
Check expected behavior, investigate a failure case and capture evidence for compare language-model behaviour on a fixed benchmark. - 04Learning milestone 4 In progress
Deliver the applied project
Complete compare language-model behaviour on a fixed benchmark, explain key decisions and identify a practical next step.
Career outlook after this learning path
Typical roles this roadmap can support, with broad salary ranges by relevant work experience.
| Role | 0–3 years | 3–7 years | 7–12+ years |
|---|---|---|---|
| AI / ML Engineer | ₹10–17 LPA | ₹20–35 LPA | ₹38–62 LPA |
Ranges are indicative India-market compensation estimates in ₹ lakh per annum, not a salary promise. They reflect broad market guides and vary by location, employer, skills and role scope; the experience bands refer to relevant work experience, not time since training. Completing this course does not guarantee employment, a role, compensation or career progression. View market reference ↗