Creating Industry ready solutions with LLM Models

Hallucinations are the biggest blocker to enterprise LLM adoption. Learn how Retrieval-Augmented Generation provides the factual grounding your models need.

#1about 3 minutes

Understanding LLMs and the transformer self-attention mechanism

Large Language Models (LLMs) are defined by their parameters and training data, with the transformer's self-attention mechanism being key to resolving ambiguity in language.

#2about 4 minutes

Exploring the business adoption and emergent abilities of LLMs

Businesses are rapidly adopting LLMs due to their emergent abilities like in-context learning, instruction following, and chain-of-thought reasoning, which go beyond their original design.

#3about 9 minutes

Demo of an enterprise assistant for integrated systems

The Simplify Path demo showcases a unified chatbot interface that integrates with various enterprise systems like HRMS, Jira, and Salesforce for both informational queries and transactional tasks.

#4about 3 minutes

Demo of a document compliance checker for pharmaceuticals

The Doc Compliance tool validates pharmaceutical documents against a source-of-truth compliance document to ensure all parameters meet regulatory requirements.

#5about 3 minutes

Demo of a chatbot builder for any website

Web Water is a product that converts any website into an interactive chatbot by scraping its HTML, text, and media content to answer user questions.

#6about 5 minutes

Navigating the common challenges of building with LLMs

Key challenges in developing LLM applications include managing hallucinations, ensuring data privacy for sensitive industries, improving usability, and addressing the lack of repeatability.

#7about 7 minutes

Using prompt optimization to improve LLM usability

Prompt optimization techniques, such as defining a role, using zero-shot, few-shot, and chain-of-thought prompting, can significantly improve the quality and relevance of LLM outputs.

#8about 4 minutes

Advanced techniques like RAG, function calling, and fine-tuning

Overcome LLM limitations by using Retrieval-Augmented Generation (RAG) for domain-specific knowledge, function calling for real-time tasks, and fine-tuning for specialized models.

#9about 10 minutes

Code walkthrough for building a RAG-based chatbot

A practical code demonstration shows how to build a RAG pipeline using LangChain, ChromaDB for vector storage, and an open-source Llama 2 model to answer questions from a specific document.

#10about 9 minutes

Q&A on integration, offline RAG, and the future of LLMs

The discussion covers integrating LLMs into organizations, running RAG offline, suitability for small businesses, and the evolution towards large action models (LAMs).