Project Description
What is this project all about
Optimizes large language model outputs by integrating authoritative external knowledge bases, enhancing accuracy and relevance beyond pre-trained data sources.
Scope: The project enhances LLM outputs by integrating real-time retrieval from trusted external sources. It focuses on improving factual accuracy and contextual relevance while reducing hallucination.
Prerequisites
What You Should Know Before Starting
- A solid understanding of Python programming is required.
- Familiarity with neural networks and transformer architecture is expected.
- Basic knowledge of machine learning is a plus but not mandatory.
Tools & Technologies
What Tools & Technologies will be used in this project
- Programming Language: Python
- Frameworks & Libraries:
- Hugging Face Transformers – for working with pre-trained language models
- PyTorch / TensorFlow – for deep learning model customization
- LangChain / LlamaIndex – for orchestration and retrieval integration
- Faiss / Weaviate / Milvus – for vector similarity search
- Data Retrieval: ElasticSearch, Pinecone, or Web Search APIs (e.g., Bing, SerpAPI)
- Model Hosting/Serving: Hugging Face Inference Endpoints, FastAPI, or Docker
- Deployment: AWS / GCP / Azure, or local servers
- Version Control & Collaboration: Git, GitHub
- Optional: OpenAI API (for baseline LLM comparisons or integration)
Learning Objectives
What you will learn in this course
- Understand the core concepts of Retrieval-Augmented Generation (RAG).
- Integrate external knowledge sources with large language models to improve output accuracy.
- Implement semantic search using vector databases (e.g., Faiss, Weaviate).
- Design and deploy a complete RAG pipeline using Python and relevant libraries.
- Evaluate and optimize the factual relevance and contextual grounding of LLM responses.
- Apply neural network and transformer principles in retrieval-based language generation tasks.
LESSON PLAN
🟦 Module 1: Neural Networks Essentials(2 Hour)
- Artificial neurons, perceptrons, and feedforward networks
- Quick intro to backpropagation and activation functions
- Overview of CNNs/RNNs (for transformer context)
🟦 Module 2: NLP Evolution & Transformers (2 Hour)
- From rule-based to deep learning NLP
- Why Transformers replaced RNNs/LSTMs
- Self-attention, positional encoding (conceptual only).
🟦 Module 3: Embeddings & Vector Databases (2 Hour)
- Word embeddings (Word2Vec, BERT, Sentence Transformers)
- Practical: Generate embeddings using HuggingFace
- Intro to FAISS, Chroma, Pinecone – when and why to use them
🟦 Module 4: Retrieval-Augmented Generation (RAG) (1.5 Hour)
- Why RAG? Limitations of standalone LLMs
- RAG architecture: Retriever + Generator
- Common use cases (chatbots, Q&A, knowledge grounding)
🟦 Module 5: RAG Practical Implementation (3-4 Hour)
- Setup PostgreSQL (or FAISS/Chroma) for storage
- Store and retrieve embeddings
- Use LangChain to connect retriever with OpenAI/HuggingFace model
- Query → Retrieve → Generate workflow
- Basic error handling & performance check
🟦 Module 6: LLM Fine-Tuning vs RAG (1 Hour)
- What is fine-tuning?
- Cost, flexibility, and scalability: Fine-tuning vs RAG
- When to combine both approaches
🟦 Module 7: Capstone Project (2-2.5 Hour)
- Build a minimal RAG-powered Q&A system
- Use a real dataset or documentation (e.g., company knowledge base)
- Test, improve, and present results
FAQ
This is not just theory. You’ll build a fully working AI-powered IT support automation system — step-by-step, guided by real professionals. It’s practical, project-based, and portfolio-ready.
If you’ve completed some basic programming (especially through our beginner courses), you’re good to go! If not, start with our foundation courses, then return for this project.
Absolutely. You’ll finish with a deployable IT automation project, integrated with GitHub and real-time logging — a great addition to your resume and GitHub portfolio.
It’s beginner-friendly if you’ve completed basic to intermediate programming. If not, take our intro courses first — they’re designed to prepare you for this exact project.
Just install Python, Node.js, and VS Code — we’ll guide you through setup in the first session. Everything else will be covered during the course.
In just 10 focused hours, you’ll learn email automation, GitHub API, WebSocket, and React integration — tools used by real tech teams today.
Yes, you will receive an industry-recognized certificate.
Yes, we specialize in helping businesses find and hire the right talent. Our HR and recruitment services are tailored to meet your company’s specific hiring needs. Contact us to learn how we can support your hiring process.
Please visit our page : WoCons – Custom AI & CAD Solutions | Bengaluru
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