LLM in Python Applications Training in Marina Bay, Singapore

The LLM in Python Applications Training is a hands-on, developer-focused program that teaches how to build real-world applications powered by Large Language Models (LLMs) using Python. The course covers LLM fundamentals, prompt engineering, API integration, orchestration frameworks, RAG basics, and deployment patterns to create scalable, production-ready AI applications.

Introduction to LLMs & Python AI Applications

• What are Large Language Models

• LLM capabilities and limitations

• Common LLM-powered application patterns

• Python’s role in GenAI ecosystems

LLM APIs & Python Integration

• Accessing LLMs via APIs

• Authentication and key management

• Request/response handling in Python

• Managing tokens, latency, and costs

Prompt Engineering Fundamentals

• Prompt structure and best practices

• System, user, and assistant prompts

• Few-shot and zero-shot prompting

• Reducing hallucination

Advanced Prompting & Control

• Prompt templates and dynamic prompts

• Structured outputs (JSON, schemas)

• Function calling concepts

• Guardrails and validation

Building LLM Apps with LangChain

• LangChain architecture overview

• Chains, tools, and agents

• Memory and conversational context

• Modular and reusable pipelines

Data-Aware LLM Applications

• Connecting LLMs to files and databases

• Text preprocessing and chunking

• Metadata usage

• Introduction to document loader

Embeddings & Vector Search

• Creating embeddings in Python

• Similarity search concepts

• Vector indexing and retrieval

• Performance considerations

Retrieval-Augmented Generation (RAG)

• RAG architecture and workflow

• Context injection strategies

• Improving answer accuracy

• Enterprise document Q&A systems

Error Handling, Testing & Evaluation

• Handling API failures and retries

• Prompt and response testing

• Evaluating LLM outputs

• Human-in-the-loop review

Performance, Cost & Scaling

• Caching and batching strategies

• Asynchronous Python patterns

• Cost optimization techniques

• Scaling LLM applications

Security, Privacy & Responsible AI

• Protecting sensitive data

• Access control and secrets management

• Bias, safety, and ethical considerations

• Compliance basics

Hands-on Exercises

Summary and Conclusion