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🧠 LLM & Prompt Engineering Beginner–Advanced 10 Modules + Projects

LLM & AI Prompt
Engineering Mastery

Go from zero to production-ready AI developer. Master prompt engineering techniques, LLM fundamentals, RAG systems with vector databases, OpenAI and Anthropic APIs, LangChain pipelines, and real-world AI automation. Hands-on labs using ChatGPT, Claude, and open-source LLMs applied to DevOps and Cloud use cases.

schedule55 Hours
science25+ Labs
workspace_premium4 Real Projects
languageEnglish
terminalHands-on Labs
starstarstarstarstar
4.9 (52 reviews) · 1,200+ enrolled
person Created by Priya Nair · AI Research Engineer & LLM Specialist, 8+ years experience
boltEnroll Now — ₹18,999
psychology LLM + AI
Prompt Engineering & LLM Track
Production AI Engineering
ChatGPT · Claude · RAG · Vector DB · LangChain · OpenAI API
55h
Content
25+
Labs
4
Projects
Tools & Technologies
ChatGPTClaudeGPT-4oLangChainRAGPineconeFAISSOpenAI APIAnthropic APIHugging FaceOllamaChroma
Countdown to next batch
07
Days
:
14
Hrs
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22
Min
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45
Sec

What you'll learn

check_circleWrite effective prompts using zero-shot, few-shot, chain-of-thought, and role-based techniques for any LLM
check_circleUnderstand transformer architecture, tokenization, embeddings, and how LLMs generate text at inference time
check_circleBuild production RAG pipelines with LangChain, vector databases (Pinecone, FAISS, Chroma), and OpenAI embeddings
check_circleIntegrate ChatGPT, Claude, and Gemini APIs into applications via REST and the official SDKs with streaming responses
check_circleBuild autonomous AI agents with LangChain agents, tool use, and function calling for real-world task automation
check_circleDeploy and run open-source LLMs locally with Ollama and fine-tune models with Hugging Face PEFT/LoRA
check_circleApply AI prompt engineering to DevOps & Cloud workflows — infra generation, incident analysis, code review automation
check_circleEvaluate LLM output quality using RAGAS, LLM-as-judge, and human-in-the-loop evaluation frameworks

boltSkills You'll Master

Prompt Engineering (zero-shot, few-shot, CoT, ToT)
LLM Fundamentals & Transformer Architecture
RAG Pipeline Design & Optimization
Vector Databases (Pinecone, FAISS, Chroma)
OpenAI API & Anthropic API Integration
LangChain & LangGraph Basics
AI Agents & Tool Use (Function Calling)
Fine-tuning with LoRA / PEFT
Open-source LLMs (Llama 3, Mistral, Phi) via Ollama
AI in DevOps & Cloud Automation
LLM Evaluation & Observability (RAGAS, Langfuse)
AI Workflow Automation & API Orchestration

constructionTools & Technologies

ChatGPT / GPT-4oClaude 3.5 SonnetGemini Pro OpenAI APIAnthropic APILangChain LangGraphLangSmithLangfuse PineconeFAISSChroma QdrantWeaviateOllama Llama 3.2Mistral 7BPhi-3 Hugging FacePEFT / LoRARAGAS StreamlitFastAPIDocker PythonJupyterAWS Bedrock Azure OpenAIGCP Vertex AI

menu_bookCourse Curriculum

10 modules · 55 hours · 25+ labs · 4 capstone projects

  • play_circleWhat are LLMs? GPT, Claude, Gemini, Llama — how they work
  • play_circleTransformer architecture: attention, embeddings, tokenization
  • play_circlePre-training vs. fine-tuning vs. RLHF vs. in-context learning
  • scienceLab: Compare ChatGPT, Claude, Gemini on same prompts — cost & quality tradeoffs
  • play_circleContext windows, temperature, top-p, and inference parameters
  • play_circleZero-shot, one-shot, and few-shot prompting patterns
  • play_circleChain-of-thought (CoT) and Tree-of-thought (ToT) reasoning
  • play_circleRole prompting, persona injection, and system-level instructions
  • play_circleOutput structuring: JSON mode, XML schemas, Pydantic validators
  • play_circleReAct and Self-Ask prompting frameworks
  • scienceLab: Build a structured data extractor with zero-shot CoT prompting
  • play_circleOpenAI Python SDK: chat completions, streaming, embeddings
  • play_circleAnthropic Claude API: messages API, vision, tool use patterns
  • play_circleFunction calling & structured outputs: tool_use with Claude, tools with OpenAI
  • play_circleRate limits, token budgeting, cost estimation & caching strategies
  • scienceLab: Build a multi-model router that picks GPT-4o vs Claude vs Gemini by task type
  • play_circleWhat are embeddings? Semantic similarity & cosine distance explained
  • play_circleVector DB options: Pinecone vs FAISS vs Chroma vs Qdrant vs Weaviate
  • play_circleChunking strategies: fixed-size, semantic, recursive, and parent-document
  • play_circleHybrid search: combining BM25 keyword search with vector similarity
  • scienceLab: Index a 500-page DevOps knowledge base in Chroma, query with semantic search
  • play_circleNaive RAG architecture: ingest, embed, retrieve, generate
  • play_circleAdvanced RAG: HyDE, query rewriting, re-ranking with cross-encoders
  • play_circleAgentic RAG: tool-using agents that decide when to retrieve
  • play_circleRAG evaluation: context recall, faithfulness, answer relevance with RAGAS
  • scienceLab: Build a company documentation Q&A chatbot with LangChain + Chroma + GPT-4o
  • scienceLab: Evaluate RAG pipeline quality with RAGAS metrics dashboard
  • play_circleLangChain fundamentals: chains, prompts, memory, callbacks
  • play_circleLCEL (LangChain Expression Language): building composable pipelines
  • play_circleLangGraph basics: stateful multi-step AI workflows with graphs
  • play_circleAutomation patterns: email processing, report generation, alert triage
  • scienceLab: Automate DevOps incident summaries from PagerDuty alerts using Claude + LangChain
  • play_circleLLM agents: ReAct loop, tool registration, and execution tracing
  • play_circleOpenAI function calling & Claude tool_use deep-dive
  • play_circleBuilding custom tools: web search, code interpreter, database query tools
  • play_circleHuman-in-the-loop patterns for production-safe agents
  • scienceLab: Build a Cloud cost-analysis agent that queries AWS Cost Explorer and explains anomalies
  • play_circleRunning Llama 3.2, Mistral 7B, and Phi-3 locally with Ollama
  • play_circleQuantization: GGUF, GPTQ, AWQ — running LLMs on CPU/GPU efficiently
  • play_circleFine-tuning with Hugging Face PEFT / LoRA on custom datasets
  • play_circleDeploying fine-tuned models with FastAPI + Docker
  • scienceLab: Fine-tune Mistral 7B on a Terraform/CloudFormation generation dataset
  • play_circlePrompt engineering for Terraform, Helm, and Dockerfile generation
  • play_circleAI-assisted code review: GitHub Copilot, Claude for PR analysis
  • play_circleIncident triage automation: log analysis, root cause explanation with LLMs
  • play_circleUsing AWS Bedrock, Azure OpenAI, and GCP Vertex AI in production
  • scienceLab: Build an AI-powered on-call assistant that triages Prometheus alerts
  • play_circleLLM evaluation metrics: BLEU, ROUGE, BERTScore, and LLM-as-judge
  • play_circlePrompt injection attacks, guardrails, and responsible AI practices
  • play_circleLLM observability: tracing with Langfuse, LangSmith dashboards
  • scienceLab: Deploy a production RAG app to AWS ECS with full observability via Langfuse

rocket_launchReal-World Projects

🤖

AI DevOps On-Call Assistant

Builds a Slack bot that ingests Prometheus/PagerDuty alerts, classifies severity, retrieves runbooks from a RAG pipeline, and posts remediation steps — fully automated.

LangChainRAGClaude API
📄

Multi-Doc Enterprise Q&A System

Production RAG system that ingests PDFs, Confluence pages, and Jira tickets into a vector database, and answers queries with cited sources — deployed on AWS.

PineconeOpenAIFastAPI
☁️

Cloud IaC Generator Agent

An LLM agent that takes plain-English infrastructure descriptions and generates validated Terraform/CloudFormation code, runs policy checks, and submits PRs.

GPT-4oFunction CallingTerraform
🔍

Custom Fine-Tuned LLM Service

Fine-tune Mistral 7B on domain-specific DevOps documentation with LoRA, quantize with GGUF, deploy as a private API, and expose with a Streamlit UI.

LoRA/PEFTOllamaStreamlit

workCareer Opportunities

🧠
Prompt Engineer
₹12–20 LPA
🔗
LLM Application Developer
₹15–28 LPA
🤖
AI Engineer
₹18–35 LPA
📊
ML / AI Consultant
₹20–40 LPA
☁️
AI-Augmented DevOps Engineer
₹14–24 LPA
🚀
GenAI Product Engineer
₹20–45 LPA
verified

AI and LLM roles are the fastest-growing tech jobs in India in 2025. Companies across banking, e-commerce, healthcare, and cloud services are urgently hiring engineers who can build and deploy LLM-powered solutions.

Frequently Asked Questions

No prior ML experience is required. You need basic Python programming skills (variables, functions, loops). The course starts with LLM fundamentals and builds up to advanced RAG and agent development. If you can write Python scripts, you're ready.
Lab credits are provided for the first few modules. For extended API usage, we guide you through setting up your own accounts (OpenAI/Anthropic free tiers cover most labs). Exact credit allocation is shared at enrollment. We also use Ollama with open-source models so you can run many labs for free on your laptop.
This course focuses on LLM fundamentals and prompt engineering — the core skills every AI developer needs. The Agentic AI course is the advanced track that builds on this: multi-agent orchestration with CrewAI, LangGraph, Autogen, and production agentic deployments. Many students take LLM Prompt Engineering first, then upgrade to Agentic AI.
Yes — Module 9 is specifically designed for DevOps/Cloud engineers. You'll build AI tools for Terraform generation, incident triage, log analysis, and cloud cost optimization. Even if you never build a GenAI product, knowing how to use LLMs to automate your existing DevOps workflows makes you significantly more productive and marketable.
You receive a Thick Brain Technology course completion certificate for LLM & AI Prompt Engineering. The certificate lists the skills, projects, and tools covered. We also help you add the credential to your LinkedIn profile and resume.
₹39,999 ₹18,999 52% OFF

⏰ Offer ends soon — seats limited

boltEnroll Now — ₹18,999
schedule55 Hours of live instruction
science25+ Hands-on lab sessions
folder_open4 Capstone projects
workspace_premiumCertificate of completion
boltAPI credits for OpenAI & Anthropic
support_agentLifetime access + placement support

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🧠

Start Your LLM & AI Journey

The AI skills gap is real — engineers who can build with LLMs earn 40–60% more. Join 1,200+ students who've already made the leap.

boltEnroll Now — ₹18,999