Flagship project
Python API Script
Write a Python script that calls an API and processes JSON data.
- Python
- Requests
- JSON
Prompt Engineering
Practical Prompt Engineering Skills Course in Amritsar with Live Labs & Placement Support
Practical Prompt Engineering Skills Course course in AmritsarJob-oriented professional training built on live labs — small batches, daily hands-on practice and 100% placement assistance.
Flexible batchesBeginner to advancedHybrid
3 / 6 / 9 Months
Duration
Weekday/Weekend
Mode
12th Pass/Any Grad
Eligibility
10-12 Students
Batch Size
Admissions open
Practical Prompt Engineering Skills Course · Flexible batches
Batch timings
- Morning
- Evening
- Weekend
- Live online
About the programme
Course overview
Course overviewtechcadd's Practical Prompt Engineering Skills Course in Amritsar is a lab-based, professional-level programme for learners who want to work as prompt engineers, AI engineers, or AI application developers.
It opens with Python, LLM fundamentals, and prompt design, then moves into advanced prompt patterns, RAG, AI agents, multi-agent systems, and AI application development.
You finish with a professional portfolio of deployed prompt-driven AI applications, a capstone project, CV preparation and interview coaching.
Prompt engineering is the practice of designing, testing, and optimising instructions that guide large language models (LLMs) to produce accurate, useful, and safe outputs. At the professional level, the job changes from writing simple prompts to owning an AI workflow — scoping the use case, designing prompt chains, evaluating model outputs, integrating AI into applications, and defending the results to a business. That is the layer this programme trains you for. Startups, IT firms, banks, healthcare providers, and manufacturers across Punjab are all adopting AI, and each of them needs engineers who can build, evaluate and explain a prompt-driven AI system before it becomes a costly mistake.
Techcadd's Practical Prompt Engineering Skills Course is built around the tools that appear in actual job descriptions — ChatGPT, Gemini, Claude, OpenAI API, LangChain, and vector databases — not academic substitutes. You work in a hands-on lab from the first week and finish with a professional portfolio of prompt-driven AI applications, a capstone project, and interview-ready documentation. The programme is available in 3, 6, and 9-month tracks, and each longer track includes everything from the shorter one without repeating a module. Batch sizes are capped at 10–12 students, and every deliverable is reviewed weekly by mentors who still work on live AI engagements.
Python & APIsLLM FundamentalsPrompt DesignAdvanced Prompt PatternsPrompt Evaluation
- Duration
- Flexible batches
- Level
- Beginner to advanced
- Mode
- Hybrid
- Batches
- Morning, evening & weekend
Module by module
The curriculum, module by module
Module by module curriculumThe Practical Prompt Engineering Skills Course is a nested ladder — each longer track includes every module from the shorter tracks and adds more depth. No module is repeated; you continue building on the skills you have already learned.
- Module 01 of 19
Python Fundamentals
Core Python concepts — syntax, variables, data types, operators, and control flow.
Topics covered
- Basics
- syntax
- variables
- operators
Tools in this module
PythonJupyter NotebookYou finish withA set of Python programs demonstrating core concepts
- Module 02 of 19
Python Data Structures & APIs
Python data structures and API calls — lists, tuples, sets, dictionaries, and working with JSON APIs.
Topics covered
- Lists
- tuples
- sets
- dictionaries
- API calls
Tools in this module
PythonRequestsJSONYou finish withA Python script that calls an API and processes JSON data
- Module 03 of 19
LLM Fundamentals
LLM fundamentals — tokens, context windows, model selection, and capabilities.
Topics covered
- LLMs
- tokens
- context windows
- model selection
Tools in this module
ChatGPTGeminiClaudeYou finish withA written comparison of LLMs and their use cases
- Module 04 of 19
Prompt Design Fundamentals
Prompt design fundamentals — role prompting, few-shot prompting, and prompt structure.
Topics covered
- Prompt design
- role prompting
- few-shot prompting
Tools in this module
ChatGPTGeminiClaudeYou finish withA portfolio of prompt designs with evaluations
- Module 05 of 19
Advanced Prompt Patterns
Advanced prompt patterns — chain-of-thought, ReAct, self-consistency, and prompt chaining.
Topics covered
- Chain-of-thought
- ReAct
- self-consistency
Tools in this module
ChatGPTGeminiClaudeLangChainYou finish withA documented prompt chain with evaluation
- Module 06 of 19
Prompt Evaluation & Testing
Prompt evaluation and testing — metrics, A/B testing, and prompt versioning.
Topics covered
- Evaluation metrics
- A/B testing
- prompt versioning
Tools in this module
LangSmithPromptLayerPythonYou finish withAn evaluation framework with test results
- Module 07 of 19
AI Application Development
AI application development — APIs, deployment, and building production-ready AI apps.
Topics covered
- Building AI applications
- APIs
- deployment
Tools in this module
FastAPIFlaskStreamlitDockerYou finish withA deployed AI application with documentation
- Module 08 of 19
AI Agents
AI agents — tool use, planning, and agent-based workflows.
Topics covered
- AI agents
- tool use
- planning
Tools in this module
LangChainAutoGenCrewAIYou finish withAn AI agent with documented tools and planning
- Module 09 of 19
Multi-Agent Systems
Multi-agent systems — orchestration, collaboration, and agent-based workflows.
Topics covered
- Multi-agent systems
- orchestration
- collaboration
Tools in this module
LangChainAutoGenCrewAIYou finish withA multi-agent AI system with documentation
- Module 10 of 19
Vector Databases & Advanced RAG
Vector databases and advanced RAG — embeddings, similarity search, and integration with LLMs.
Topics covered
- Vector databases
- embeddings
- similarity search
Tools in this module
PineconeChromaDBWeaviateYou finish withA vector database-powered application
- Module 11 of 19
Deployment & Production
Deployment and production — Docker, Kubernetes, AWS, monitoring, and scaling.
Topics covered
- Production deployment
- monitoring
- scaling
Tools in this module
DockerKubernetesAWSMLflowYou finish withA production-deployed AI application with monitoring
- Module 12 of 19
Industry Capstone Project
A complete industry-style capstone combining prompt engineering, RAG, AI agents, and deployment.
Topics covered
- End-to-end AI project
- deployment
Tools in this module
Full AI stackGitHubYou finish withA complete end-to-end capstone project and report
- Module 13 of 19
AI System Design
AI system design — architecture, scalability, and designing production AI systems.
Topics covered
- AI system design, architecture, scalability
Tools in this module
Architecture patternsAWSDockerYou finish withAn AI system design document with architecture
- Module 14 of 19
MLOps & Model Monitoring
MLOps — experiment tracking, model monitoring, and CI/CD for ML.
Topics covered
- MLflow
- model monitoring
- CI/CD for ML
Tools in this module
MLflowDockerGitHub ActionsYou finish withAn MLOps pipeline with experiment tracking and monitoring
- Module 15 of 19
Multi-Agent AI Systems
Multi-agent AI systems — orchestration, collaboration, and agent-based workflows.
Topics covered
- Multi-agent systems
- orchestration
- collaboration
Tools in this module
LangChainAutoGenCrewAIYou finish withA multi-agent AI system with documentation
- Module 16 of 19
AI System Design
AI system design — architecture, scalability, and designing production AI systems.
Topics covered
- AI system design
- architecture
- scalability
Tools in this module
Architecture patternsAWSDockerYou finish withAn AI system design document with architecture
- Module 17 of 19
Cloud Deployment (AWS/GCP)
Cloud deployment for AI — AWS, GCP, Docker, and Kubernetes.
Topics covered
- Cloud deployment
- AWS
- GCP
Tools in this module
AWSGCPDockerKubernetesYou finish withA cloud-deployed AI application with documentation
- Module 18 of 19
Real-World AI Solutions
Real-world AI solutions — case studies, problem-solving, and industry applications.
Topics covered
- Real-world AI
- problem-solving
- case studies
Tools in this module
Full AI stackYou finish withA real-world AI solution with documentation
- Module 19 of 19
Advanced Capstone Project
An advanced capstone project combining all skills learned throughout the programme.
Topics covered
- Advanced capstone
- end-to-end AI
Tools in this module
Full AI stackGitHubYou finish withA complete advanced capstone project and report
What you build
Projects that answer the interview question for you
Portfolio projectsEach project is reviewed individually, and the review notes go into your portfolio documentation.
Prompt Design Portfolio
Design a portfolio of prompts with evaluations.
- ChatGPT
- Gemini
- Claude
Prompt Chain
Build a documented prompt chain with evaluation.
- LangChain
- PromptLayer
Evaluation Framework
Build an evaluation framework with test results.
- LangSmith
- Python
AI Application
Deploy an AI application with FastAPI or Streamlit and Docker.
- FastAPI
- Streamlit
- Docker
RAG Pipeline
Build a retrieval-augmented generation pipeline with vector databases.
- LangChain
- Pinecone
- ChromaDB
AI Agent
Build an AI agent with tool use and planning.
- LangChain
- AutoGen
- CrewAI
Multi-Agent System
Build a multi-agent AI system with LangChain or CrewAI.
- LangChain
- AutoGen
- CrewAI
MLOps Pipeline
Build an MLOps pipeline with MLflow and CI/CD.
- MLflow
- Docker
- GitHub Actions
End-to-End Capstone
Complete an end-to-end prompt engineering capstone combining prompts, RAG, agents, and deployment.
- Full AI Stack
- GitHub
The working loop
Learn it. Build it. Make it yours.
Learn it. Build it. Make it yours.Every project moves through the same loop: understand the brief, build it with guidance, then explain the decisions behind your work. The certificate is the receipt — the portfolio is the point.
- 01
Industry-focused curriculum
You learn it the way it is used on real AI projects, with the workflow and standards an AI team expects.
Python API Script
- 02
Live labs, not slides
Every module runs in a hands-on lab against real LLM APIs and datasets — not screenshots.
Prompt Design Portfolio
- 03
Mentor-reviewed deliverables
Each module ends with a documented prompt and evaluation, reviewed weekly by a mentor who still works on live AI engagements.
Prompt Chain
- 04
Reporting and certification
Every exercise ends with a written report, and the programme closes with a practical assessment and certification.
Evaluation Framework
The honest pitch
Your months should build more than a certificate.
Your months should build more than a certificate.A course is worth the time you give it only if you finish with work you can show and skills you can defend.
Project review week at the Amritsar campus
Mentors mark up work line by line
Hiring drive & mock interview day
Practitioners run the panel, not HR
Why choose Practical Prompt Engineering Skills Course
Why students choose techcadd
Why students choose techcaddWhat makes this course at techcadd different from a typical alternative.
- 01
Mentors who still ship client work
Not full-time lecturers — trainers who are still doing the job they teach.
- 02
Live labs, not slides
Every module runs in a hands-on lab against real LLM APIs and datasets — not screenshots.
- 03
Tools used in job descriptions
ChatGPT, Gemini, Claude, OpenAI API, LangChain, Pinecone — not outdated or academic substitutes.
- 04
Nested ladder structure
Each longer track includes every module from the shorter one without repeating a module.
- 05
AI-assisted learning
You learn to use AI assistants and prompt engineering as practical tools in AI engineering.
Eligibility
Who can join the Practical Prompt Engineering Skills Course course
Who can join the Practical Prompt Engineering Skills Course courseThis course works for a specific starting point — here's who gets the most out of it.
- Educational backgroundNo specific degree or stream required — school leavers, graduates and career switchers all qualify.
- Prior experienceNone required. The programme opens at beginner level and builds from there across flexible batches.
- AgeNo upper age limit. What matters is being able to commit to the full course rather than dropping off midway.
- EquipmentA laptop or desktop with a stable internet connection for classroom or live-online batches.
- Time commitmentAble to attend regularly across flexible batches, in a morning, evening or weekend batch.
Where it leads
The roles this portfolio opens
Job titles vary by company, but the underlying expectations do not. Each destination below maps to work you will have already done during the programme.
Tools you will actually use
The working stack for Practical Prompt Engineering Skills Course
Tools and technologies coveredEvery tool below is installed, configured and used by you during the course — not demonstrated on a slide. You leave able to set up your own environment from scratch.
20tools covered
Programming language
- Python
Development environment
- Jupyter Notebook
AI assistant
- ChatGPT
- Gemini
- Claude
AI API
- OpenAI API
- Claude API
- Gemini API
LLM framework
- LangChain
LLM evaluation
- LangSmith
Prompt management
- PromptLayer
Vector database
- Pinecone
- ChromaDB
- Weaviate
Multi-agent framework
- AutoGen
- CrewAI
Web framework
- FastAPI
- Flask
Deployment framework
- Streamlit
Containers
- Docker
Orchestration
- Kubernetes
Cloud platform
- AWS
- GCP
MLOps
- MLflow
Version control
- Git
Code hosting
- GitHub
CI/CD
- GitHub Actions
Certification
Get certified in Practical Prompt Engineering Skills Course
Get certified in Practical Prompt Engineering Skills CourseIssued on completion against the modules you finished and the projects you submitted — plus an internship letter where the industrial training track applies. Shareable to LinkedIn, and verifiable by an employer who calls the Amritsar desk.
- Module-wise assessment, not attendance-based
- Project submissions logged against your certificate
- Internship letter on the industrial training track
- Verifiable directly with the campus office
Future scope
Where this takes you over the next five years
Where this takes you over the next five yearsThe skill does not stand still, and neither does what it is worth.
- Generative AI and LLMsEvery application is adding AI features, making LLM skills increasingly valuable.
- AI agents and multi-agent systemsAutonomous agents are moving from research to production applications.
- RAG and vector databasesRetrieval-augmented generation is becoming standard for enterprise AI.
- Multi-modal AIModels that handle text, image, and audio are becoming standard.
- Responsible AIEthics, bias, and explainability are becoming standard requirements in AI job descriptions.
The objective is practical knowledge rather than memorised concepts.
Due diligence
How techcadd compares
How techcadd comparesWhat actually differs between this course at techcadd and a typical alternative.
| What you should ask | techcadd Amritsar | Other institutes |
|---|---|---|
| Live prompt engineering project work | Every course, from week one | Varies, often only at the end |
| Small batches (10–12) | Capped for mentor attention | Often 30+ students per batch |
| Tools used in job descriptions | ChatGPT, Gemini, Claude, OpenAI API, LangChain, Pinecone | Outdated or academic substitutes |
| Mentor-reviewed deliverables | Weekly review by working professionals | Often no review or feedback |
| AI-assisted learning | Built into the curriculum | Rarely included |
Every comparison here is about substance, not marketing language.
Before you enrol
Questions we get asked at the admissions desk
Frequently asked questionsIf something here is not covered, the Amritsar desk will answer it directly — no call-back queue.
No. The programme opens at beginner level and assumes no background beyond comfort with a computer. Students who already have some exposure move through the early modules faster and spend the saved time on project work.
