Building Smarter AI Applications
Design and implement secure, production-ready AI solutions using RAG and local or networked LLMs; improve accuracy, preserve data privacy, and integrate AI into real projects.
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Audience: Developers, Application Engineers, Data Engineers
Duration: TBD
Format: Lectures and hands-on labs
Overview
A hands-on journey into secure, responsible, and practical AI with Retrieval-Augmented Generation (RAG) and local / networked LLMs—equipping teams to design and implement solutions that respect data privacy while delivering real business value.
Objective
Design and implement secure, production-ready AI solutions using RAG and local or networked LLMs; improve accuracy, preserve data privacy, and integrate AI into real projects.
What You Will Learn
- Structuring private data for semantic search
- Applying prompt engineering for optimal outcomes
- Implementing secure AI with local or networked models
- Using LangChain and vector databases for advanced search & RAG
- Building & improving conversational bots and multimodal AI systems
- Exposing AI capabilities through RESTful APIs
Course Details
Audience: Developers, Application Engineers, Data Engineers
Duration: TBD
Format: Lectures and hands-on labs
TBD
Setup: TBD
Detailed Outline
- Secure, responsible and practical AI foundations
- Privacy-preserving architectures for enterprise data
- From prototypes to production: accuracy, observability, and integration
- Structure private data for semantic search
- Prompt engineering patterns and guardrails
- Local vs. hosted LLM trade-offs and security considerations
- LangChain + vector DB workflows for RAG
- Conversational bots & multimodal AI
- RESTful API exposure and service design
- Agentic-on-Bedrock
- Chat-with-your-own-data-Langchain
- Database-agent
- Functions-Tool-Agents-Langchain
- LLM_With_Semantic_Search
- Langchain
- Prompt-engineering
- Serverless-LLM-Bedrock
- WIP
- llama4
Ready to Get Started?
Contact us to learn more about this course and schedule your training.