DataSense LMS

In today’s enterprise environment, LLM-powered chatbots are transforming the way businesses handle internal queries, customer support, and documentation access. But here’s the challenge: every query to an LLM is time-consuming and expensive, especially when users often ask similar or repeated questions.

That’s where this workshop comes in.

In this intensive weekend workshop, you’ll build a production-ready Enterprise AI Chatbot that goes beyond just replying — it learns from every interaction, stores responses, and intelligently detects similar questions using semantic caching techniques.

Instead of hitting the LLM for every input, your chatbot will:

• Check if a similar question was asked before
• Retrieve the previous answer from a vector database (like FAISS)
• Only query the LLM when absolutely necessary

This drastically reduces latency, cuts down on API costs, and gives your users a much faster, smoother experience.

You’ll also deploy your chatbot using FastAPI, Docker, and GitHub Actions, making it truly enterprise-ready and scalable for real-world use cases.

Whether you’re building an internal knowledge assistant, an automated customer support tool, or a documentation Q&A system, this workshop is your gateway to building AI chatbots that are smart, fast, and efficient.

🔧 What You’ll Learn & Build:

✅ End-to-end FastAPI backend to handle user queries

✅ Integrate LLM (OpenAI/HuggingFace) for intelligent responses

✅ Implement Semantic Caching using FAISS or Pinecone

✅ Detect and bypass similar/repeated queries with Vector Search

✅ Store embeddings + responses persistently

✅ Containerize your chatbot with Docker

✅ Automate deployment using GitHub Actions

💡 Key Takeaways:

• Understand enterprise-level caching strategies for AI apps
• Build and deploy scalable chat systems with low latency
• Learn how to balance cost, performance, and memory
• Get hands-on with the tools used in real-world LLM workflows

🗓️ Schedule:

Saturday (2.5 hrs)- 8 PM IST

• LLM integration with FastAPI
• Semantic search + vector DB setup
• Caching logic + memory store

Sunday (2.5 hrs)8 PM IST

• Cache bypass for similar queries
• Dockerizing the app
• CI/CD pipeline with GitHub Actions
• Final deployment + Q&A

👨‍💻 Who Should Attend:

• Developers building LLM apps
• AI/ML engineers creating smart tools
• Teams scaling internal AI assistants
• Anyone tired of slow, costly LLM calls