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Practical LLM Applications 📍 Dilsukhnagar, Hyderabad ✅ Completion Terms Apply

Generative AI Course

Learn how modern generative models work and build reliable applications using prompting, APIs, retrieval, tool use and evaluation. Confirm the current models, tools and batch plan before enrolling.

⏱

DURATION

8 Weeks (typical plan)

🎯

LEVEL

Beginner to Intermediate

💻

MODE

Classroom · Live Online · Hybrid

🏆

CERTIFICATE

On meeting completion requirements

Tools & Technologies You Will Learn

LLM APIs Prompting Python Retrieval Vector search Evaluation

Enroll on WhatsApp → View Curriculum

Generative AI Course at LearnAI Tech Hub

🤖

Free Demo Available
Contact us for current batch fees & EMI options

⏱ Duration: 8 Weeks (typical plan)
🎯 Level: Beginner to Intermediate
💻 Classroom · Live Online · Hybrid
🏆 Completion certificate after meeting course requirements
📁 Guided project and portfolio preparation
🧭 Prerequisites explained before enrollment
📞 Current trainer and batch details available before payment

Ask About Enrollment Book Free Demo Class Call: +91 93900 23585

Ask for the current syllabus, trainer, schedule, fees and support terms before enrolling.

About This Course

Build reliable Generative AI applications, not only prompt demos

The course explains model capabilities and limits, then moves into structured prompting, API integration, retrieval-augmented generation, tool use, evaluation and responsible deployment.

Because providers and model names change quickly, the current batch syllabus should identify the exact tools being taught and the project deliverables learners will complete.

What makes this course different

🧠

Understand the system

Learn tokens, context, model behaviour, grounding, hallucination risk and evaluation.

🔌

Use APIs

Create small applications that pass structured inputs, handle outputs and manage errors and costs.

📚

Ground responses

Use retrieval and source-aware answer design rather than relying only on model memory.

🧪

Evaluate before release

Test quality, safety, latency and cost against a defined use case and rubric.

Course Curriculum

8 modules · 8 Weeks (typical plan) · Guided practical work

The sequence below describes the current course plan. Ask for the dated batch syllabus because tools and module order may change.

Generative AI and LLM Foundations

8 topics · Week 1

Generative versus predictive systems

Tokens and context windows

Training, inference and model limits

Text, image and multimodal use cases

Privacy and data-handling basics

Common failure modes

Model and provider comparison criteria

Practice: use-case and risk analysis

Structured Prompting

8 topics · Week 2

Clear task and context design

Examples and constraints

Structured output formats

Prompt templates

Tool instructions

Prompt injection awareness

Versioning and test cases

Practice: reusable prompt library

LLM APIs and Application Basics

8 topics · Week 3

Authentication and environment variables

Request and response handling

System and user messages

Streaming

Error and rate-limit handling

Cost controls

Logging without leaking data

Project: small assisted workflow

Retrieval-Augmented Generation

9 topics · Week 4

Why grounding matters

Document preparation and chunking

Embeddings

Vector search

Retrieval quality

Source attribution

Answer constraints

Evaluation dataset

Project: cited document assistant

Tools and Agentic Workflows

8 topics · Week 5

Function and tool calling

Input validation

Workflow state

Human approval points

Retries and fallbacks

Observability

Security boundaries

Practice: multi-step workflow

Multimodal Applications

7 topics · Week 6

Vision inputs

Document extraction

Image-generation basics

Audio and transcription use cases

Quality review

Project: multimodal prototype

Evaluation, Safety and Deployment

8 topics · Week 7

Define success criteria

Build evaluation cases

Factuality and citation review

Safety and refusal testing

Latency and cost measurement

Access control

Monitoring and feedback

Deployment checklist

Capstone and Portfolio Review

7 topics · Week 8

Problem statement

Architecture and trade-offs

Implementation plan

Evaluation report

Demo preparation

README and documentation

Project review

Get Full Curriculum on WhatsApp →

Career Relevance

Roles where these skills may be useful

Training does not guarantee a job, salary, promotion or internship. Outcomes depend on prior experience, project quality, assessment performance and employer requirements.

🧑‍💻

Generative AI Application Developer

Application, integration and evaluation work subject to software-development prerequisites.

⚙️

AI Workflow Builder

Design assisted workflows with clear controls, validation and human review.

📚

Knowledge Assistant Developer

Retrieval, document processing, citation and answer-quality work.

📊

AI Product or Operations Analyst

Use-case definition, testing, measurement and process improvement.

Ask for current career-support terms

We can explain the included resume, portfolio and interview support before enrollment. Employer decisions remain independent.

Who Is This For

This course is designed for you if…

🎓

Students and graduates

Learners who want a structured introduction and can complete regular practical work.

🧑‍💻

Developers

People with programming foundations who want to integrate models into applications.

💼

Working professionals

Domain specialists exploring responsible AI-assisted workflows.

🚀

Product and business teams

People who need to assess use cases, risks, cost and quality before adoption.

Prerequisites

Basic computer literacy Laptop and reliable internet Python basics for the developer exercises Willingness to test and document results

FAQs

Questions about Generative AI Course?

Not finding your answer? Ask for the current syllabus, prerequisites, trainer, fees and batch schedule.

Ask on WhatsApp →

Is programming required?

Concept and workflow exercises can be followed without advanced coding, but API and application projects require basic Python. Ask which track is included in the current batch.

Which models and providers are included?

Model offerings change quickly. Request the dated syllabus for the exact providers, models, accounts and costs used in the current batch.

What is the difference between Generative AI and Agentic AI?

Generative AI produces content or structured outputs. Agentic workflows connect models to tools and state so they can perform controlled multi-step tasks.

Will I build a project?

Yes, the plan includes guided application work. Ask for the current project brief, assessment rubric, source-code policy and portfolio-use conditions.

Does this course guarantee an AI job?

No. The course builds relevant knowledge and project evidence; hiring depends on your broader technical skills, experience, assessment performance and employer requirements.

Generative AI Course

Build reliable Generative AI applications, not only prompt demos

Understand the system

Use APIs

Ground responses

Evaluate before release

8 modules · 8 Weeks (typical plan) · Guided practical work

Tools used during guided practice

Roles where these skills may be useful

This course is designed for you if…

Students and graduates

Developers

Working professionals

Product and business teams

Prerequisites

Questions about Generative AI Course?

Review the Generative AI Course before you enroll

Generative AI Course

Build reliable Generative AI applications, not only prompt demos

Understand the system

Use APIs

Ground responses

Evaluate before release

8 modules · 8 Weeks (typical plan) · Guided practical work

Tools used during guided practice

Roles where these skills may be useful

This course is designed for you if…

Students and graduates

Developers

Working professionals

Product and business teams

Prerequisites

Questions about Generative AI Course?

Review the Generative AI Course before you enroll

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