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Prompt Engineering and LLM Development

Instructor: Arun Kumar A R Instructor - Brisa Technologies

Centre for Development of Advanced Computing (C-DAC)

Course Fees: ₹6,999/-

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Starting On:
Dec. 30, 2025

Format:
Video Lessons + Demo
Language:
English
Duration:
8 hours
Certificate:
Digital Copy
Audience:
QA Engineers / Test Automation Engineers,Software Professionals,DevOps Engineers

Course Overview

Learn the art and science of designing effective prompts to get the best results from large language models (LLMs). This program covers prompt patterns, advanced techniques, and practical business use cases.

What You'll Learn

Basics of prompts and model responsesStructured prompt frameworks (Chain-of-thought, Role-based)Few-shot and zero-shot promptingAdvanced prompt tuning for accuracy and creativityBuilding prompt libraries for enterprise use

Why Enroll in this Course?

Build practical GenAI solutions using prompt engineering, LLM workflows, RAG pipelines, and fine-tuning—not just theory.

Get hands-on experience with OpenAI APIs, LangChain, embeddings, and vector databases used in real production systems.

End-to-End LLM Development Coverage

Learn the complete lifecycle—from prompt design and evaluation to fine-tuning, retrieval pipelines, and deployment-ready architectures.

Course Syllabus

What are LLMs?
Encoder vs Decoder vs Encoder-Decoder models
Popular LLM families (GPT, LLaMA, Mistral, Claude)
Limitations of LLMs (hallucination, bias, cost)
What is a prompt?
Prompt components
Deterministic vs creative prompts
Prompt length & token optimization
Zero-shot prompting
One-shot & Few-shot prompting
Chain-of-Thought (CoT)
ReAct prompting
Role-based prompting
Step-by-step reasoning prompts
Guardrails & safety prompts
Output formatting (JSON, YAML, XML)
Prompt templates
Prompt versioning
Summarization
Classification & tagging
Information extraction
Code generation & review
OpenAI API overview
Chat Completions vs Responses API
Tokens, temperature, top-p
Streaming responses
Error handling & retries
What are embeddings?
Text → vector representation
Similarity search (cosine similarity)
Why LangChain?
Core components
LCEL (LangChain Expression Language)
Simple chains
Sequential chains
Router chains
Agents vs Chains
Tool calling
Using OpenAI models in LangChain
Prompt templates with OpenAI
Structured outputs (Pydantic, JSON)
Error handling & retries
Show more..

Instructor Profile

Arun Kumar A R
Arun Kumar A R

AI & ML | Data Science & Analytics | AI-ML Model Building | Generative AI | Corporate Trainer

 

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Testimonials

Real stories from developers who upskilled, built projects, and advanced their careers with
GenAInstein Student Internship Program.

The course helped me truly understand how to work with LLMs and build GenAI apps from scratch. The hands-on projects and mentorship gave me the confidence to apply these skills at work.
RM
Rahul M.
Software Engineer
As a student, I was overwhelmed by all the AI hype. GenAInstein's course broke it down clearly and helped me build actual working apps. I even added one to my portfolio!
SK
Sneha K.
Final Year Student
What stood out was how practical everything was. I wasn't just learning theory—I was building things, using RAG pipelines, and even integrating APIs. This course is gold for developers.
AS
Arjun S.
Developer
The curriculum was very well-structured, and the mentors were incredibly helpful. It's rare to find a course that balances cutting-edge content with hands-on coding so well.
PD
Priya D.
Tech Lead

Certification Details

Optional Certification Exam: ₹499/-
Certification Criteria: Minimum 75% score required
Certificate Type: Digital e-certificate (no hard copies)
Exam Date: To be announced after course completion

Certificate Sample