LLM Use: Application and Proprietary Data
Build LLM‑powered apps that securely integrate and reason over proprietary data.
Get Course Info
Audience: Developers, data scientists, team leads, project managers
Duration: 2 days
Format: Lectures and hands‑on labs (50% – 50%)
Overview
This course teaches how to build applications with LLMs that leverage multi‑step reasoning and the user's proprietary documents.
Objective
Build LLM‑powered apps that securely integrate and reason over proprietary data.
What You Will Learn
- Models, prompts, and parsers
- Chains, agents, Q&A systems
- Document loading & splitting
- Vector stores & embeddings
- Retrieval‑augmented Q&A and chat with documents
Course Details
Audience: Developers, data scientists, team leads, project managers
Duration: 2 days
Format: Lectures and hands‑on labs (50% – 50%)
Prerequisites:
- General familiarity with machine learning
Setup: Zero‑install cloud lab • Modern laptop • Chrome browser
Detailed Outline
- LLM applications overview
- Proprietary data challenges
- Security considerations
- Architecture patterns
- Model selection criteria
- Prompt design for proprietary data
- Output parsing strategies
- Error handling
- Building processing chains
- Agent-based architectures
- Multi-step reasoning
- Decision making systems
- Document ingestion pipelines
- Text chunking strategies
- Metadata preservation
- Quality control
- Embedding generation
- Vector database setup
- Similarity search
- Performance optimization
- RAG implementation
- Context retrieval
- Answer generation
- Accuracy evaluation
- Conversational interfaces
- Context management
- Multi-turn dialogues
- User experience design
- End-to-end implementation
- Testing and validation
- Deployment considerations
- Maintenance strategies
Ready to Get Started?
Contact us to learn more about this course and schedule your training.