Build with LLMs. Ship AI Applications.
Our OpenAI, LangChain & LLM Apps Course takes you from API calls to production AI systems — GPT-4, LangChain, vector databases, RAG, agents and deployment, taught through real AI applications and capstone builds, not just tutorials.
Why Technogeeks X AI
A training partner built around outcomes, not just attendance.
Deep Technical Curriculum
Every module covers what LLM engineers actually build — from basic completions to multi-agent systems.
Production-Ready Code
Build applications that handle real users, real data and production constraints — not notebook demos.
Practicing AI Engineers
Learn from mentors who actively ship LLM applications and stay updated with the latest models.
Small Batch Sizes
Focused, interactive batches so every learner gets code reviews and architecture feedback.
Placement & Career Support
Resume building, portfolio reviews and job assistance built into the program.
Proven Track Record
5000+ learners trained across our AI & development programs.
Real skills, not just certificates
By the end of the OpenAI, LangChain & LLM Apps course, here's exactly what you'll be able to walk into an interview and demonstrate.
Integrate
Work with OpenAI, Anthropic and open-source LLM APIs effectively.
Orchestrate
Build complex chains and workflows with LangChain and LlamaIndex.
Enhance
Implement RAG pipelines with vector databases and embeddings.
Deploy
Ship AI agents and applications to production with monitoring.
Everything an LLM Engineer needs
One connected toolkit, not a checklist — each skill below is taught in the context of the others.
APPS
- OpenAI
- LangChain
- GPT-4
- Embeddings
- Pinecone
- RAG
- Agents
- LlamaIndex
- FastAPI
- Streamlit
From prototype to production AI
Every project in this course follows the same pipeline a professional LLM engineer uses on the job.
-
01
Design
Use Case · Architecture
-
02
Prompt
Templates · Few-Shot
-
03
Chain
LangChain · LlamaIndex
-
04
Memory
Vector DB · RAG
-
05
Agent
Tools · Function Calling
-
06
Deploy
API · Monitoring · Scale
AI Apps you'll actually build
Not toy demos — these are the real capstone projects inside the course, the same ones you'll show in interviews.
Enterprise RAG Chatbot
Problem — Company needed AI chatbot trained on internal docs with accurate citations.
Stack — LangChain, Pinecone, OpenAI, document loaders, custom retrievers.
Outcome — Production chatbot answering 1000+ queries/day with 94% accuracy.
Multi-Agent Research Assistant
Problem — Researchers needed automated literature review and summarization.
Stack — AutoGen, LangChain, arXiv API, GPT-4, custom tools.
Outcome — Multi-agent system generating research summaries in minutes.
AI Code Review & Refactoring Tool
Problem — Dev team needed automated code review with actionable suggestions.
Stack — OpenAI, LangChain, GitHub API, custom prompts, Streamlit UI.
Outcome — Tool reviewing 200+ PRs/month with detailed feedback.
Six phases, one connected program
The full syllabus is grouped into six learning phases so it's easier to see how everything fits together.
How LLMs work, tokenization, prompt design patterns, few-shot learning, system prompts and output parsing.
LangChain architecture, prompts, chains, agents, memory, callbacks and building multi-step LLM workflows.
Understanding embeddings, similarity search, Pinecone, Weaviate, Chroma and vector store integrations.
Retrieval-Augmented Generation, document loaders, text splitters, retrievers and building production RAG systems.
Building autonomous agents, tool use, function calling, multi-agent systems with AutoGen and CrewAI.
API development with FastAPI, monitoring, cost optimization, followed by an end-to-end LLM application capstone.
Get an Industry-Recognized Certificate
A certificate that reflects real AI application work, not just seat time.
- Project-basedAwarded only after completing capstone AI applications, not just attendance.
- ShareableAdd directly to your LinkedIn profile and resume.
- VerifiableEvery certificate carries a unique ID employers can verify.
- Industry-alignedMapped to the tools and skills employers actually screen for.
Learn by building real AI apps
Every module pairs theory with hands-on coding — not slides you scroll past.
- Use Case DesignDefine problem, constraints and success metrics
- Prompt EngineeringCraft prompts, few-shot examples and output parsers
- Chain BuildingOrchestrate multi-step workflows with LangChain
- RAG IntegrationAdd vector search and document retrieval
- Deploy & MonitorShip to production with logging and cost tracking
Don't just call APIs. Learn to engineer with AI.
Production patterns and best practices are part of the core curriculum, not a bonus webinar.
Where this course takes you
Learn
OpenAI, LangChain, embeddings, RAG, agents and deployment fundamentals.
Build
Apply every module to real AI applications with actual users and data.
Portfolio
Ship 4–5 deployed AI applications you can demo live.
Interview
Mock interviews built around your own AI architectures.
Apply
Apply for roles with mentor and profile support.
Support that goes beyond the syllabus
Resume / CV
AI-focused resume highlighting LLM applications and technical depth.
Mock Interviews
Practice rounds with real interview feedback on AI system design.
Interview Prep
Common LLM engineer interview questions and architecture reviews, solved.
Mentorship
Ongoing guidance from practicing LLM engineers.
Job Assistance
Access to relevant openings and referrals in AI teams.
Profile Guidance
GitHub, LinkedIn and portfolio review before you apply.
Alumni Working Across the Industry
A growing network of LLM engineers who started exactly where you are now.
Course Snapshot
OpenAI, LangChain & LLM Apps Program
OpenAI, LangChain & LLM Apps Program
₹25,000 / full program
No-cost EMI options available
- + Live instructor-led training
- + 5 real-world AI application projects
- + OpenAI, LangChain & vector DB access
- + Resume, mock interviews & job assistance
- + Certificate of completion
Frequently asked questions
A job-focused program covering GPT-4, LangChain, RAG, vector databases, AI agents and production deployment, built around real AI applications.
Developers, data scientists and engineers with Python experience — basic programming knowledge is required.
OpenAI, LangChain, LlamaIndex, Pinecone, Weaviate, Chroma, AutoGen, CrewAI, FastAPI and Streamlit.
Yes — Python is the primary language for LangChain and most LLM tooling. Basic Python proficiency is expected.
Yes — the program includes real AI application assignments and 5 capstone-style LLM projects.
Yes, a certificate of completion is provided at the end of the program.
Resume building, mock interviews, mentorship and job assistance are included — see the Career Launchpad section above.
Yes, both online and offline learning modes are available.
Yes — use "Watch Free Demo" above or book a free counselling call to get access.
Yes — production patterns, cost optimization, monitoring and deployment are core parts of the curriculum.