Mastering Conversational AI with Llama 2, Flowise, and LangChain Explore the conversational AI Development Lifecycle

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Overview

Mastering Conversational AI with Llama 2, Flowise, and LangChain delivers a comprehensive exploration of cutting-edge open-source technologies that are democratizing conversational AI development. This hands-on training program equips participants with the skills to leverage Meta’s powerful Llama 2 language model alongside intuitive no-code and code-based frameworks to create sophisticated conversational applications. Participants will gain practical expertise in implementing, customizing, and deploying AI-powered conversational systems that can be integrated into various platforms and business environments.

The course offers an immersive journey through the entire conversational AI development lifecycle, from foundational model setup to advanced deployment strategies. By combining theoretical understanding with extensive hands-on implementation, participants will learn to build context-aware conversational systems using both visual workflow tools and programmatic approaches. The curriculum bridges the gap between technical complexity and practical application, empowering developers of all skill levels to create production-ready conversational AI solutions using open-source technologies.

Cognixia’s Mastering Conversational AI program stands at the intersection of accessibility and technical depth. Participants will not only master the technical aspects of implementing conversational AI using Llama 2, Flowise, and LangChain but will also develop a nuanced understanding of memory management, context handling, and ethical considerations. The course goes beyond basic implementation by addressing critical aspects of production deployment, security, and bias mitigation, preparing professionals to develop responsible and effective conversational AI systems in this rapidly evolving field.

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What you'll learn

  • Configure and deploy Llama 2 in both local and cloud environments
  • Build no-code AI workflows using Flowise's visual interface
  • Implement advanced conversational patterns with LangChain's components
  • Optimize conversational AI responses through prompt engineering & fine-tuning
  • Develop context-aware AI assistants with sophisticated memory management and multi-turn conversation capabilities
  • Deploy secure, scalable conversational AI solutions across web, mobile, and enterprise platforms

Prerequisites

Familiarity with APIs and HTTP requests

Curriculum

  • What is Conversational AI
  • Overview of Meta’s Llama 2 Model
  • Setting Up Llama 2 Locally and on Cloud Platforms
  • What is Flowise? Overview of No-Code AI Workflows
  • Setting Up Flowise for AI Workflow Automation
  • Creating a Simple AI Chatbot with Flowise
  • Introduction to the LangChain Framework
  • Key Components: Prompt Templates, Chains, and Agents
  • Building a Simple Chatbot with LangChain
  • Understanding Fine-Tuning vs. Prompt Engineering
  • Optimizing Prompts for Conversational AI
  • Hands-on: Customizing Llama 2 with Domain-Specific Data
  • Combining Llama 2 with LangChain for Advanced AI Workflows
  • Using Flowise to Visually Build AI-powered applications
  • Developing a Multi-Turn Conversational AI
  • How Memory Works in Conversational AI
  • Implementing Session-Based and Long-Term Memory in LangChain
  • Building AI Assistants with Context Awareness
  • Deployment Strategies: Local, Cloud, and Edge AI
  • Using APIs to Integrate Conversational AI into Web & Mobile Apps
  • Deploying an AI Chatbot with Llama 2 & LangChain
  • AI Bias & Hallucinations: How to Minimize Risks
  • Securing AI Applications & Handling Sensitive Data
  • Future of Conversational AI: Trends & Innovations

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Course Feature

Course Duration
Learning Support
Tailor-made Training Plan
Customized Quotes

FAQs

Llama 2 is Meta's open-source large language model that offers competitive performance to proprietary alternatives while providing greater flexibility for customization and deployment. Unlike closed models, Llama 2 can be run locally or on your infrastructure, giving developers full control over data privacy, cost management, and integration capabilities. The course explores Llama 2's architecture, capabilities, and optimal use cases compared to other models in the AI ecosystem.
The course covers multiple customization strategies, including prompt engineering techniques, fine-tuning with domain-specific data, and implementing specialized knowledge bases. You'll learn to enhance Llama 2's performance for specific industries by creating tailored prompt templates, training on relevant datasets, and integrating external knowledge sources. Through hands-on exercises, you'll develop conversational AI solutions optimized for domains like customer service, healthcare, finance, and technical support.
This course is designed for a diverse audience, including software developers, AI enthusiasts, product managers, and technical professionals looking to implement conversational AI solutions.
For this GenAI course, participants need to be familiar with APIs and HTTP requests.