LLM in Python Applications Training in San Francisco

LLM in Python Applications Training in San Francisco, USA

This developer-focused program teaches how to build real-world applications powered by Large Language Models (LLMs) using Python.

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LLM in Python Applications Training is a professional training program delivered by ProgNXT in San Francisco, USA. ProgNXT, a globally recognized corporate training provider in 55+ countries, offers this LLM in Python Applications Training course with hands-on, instructor-led sessions. The LLM in Python Applications Training is a hands-on, developer-focused program that teaches how to build real-world applications powered by Large...

Expert Panel

Designed by the ProgNXT AI & Data Science Expert Panel, specializing in Generative AI, Machine Learning, and ChatGPT applications

ProgNXT AI & Data Science Expert Panel

Course Code: OUB27
21 Hrs
Last Updated: April 2026

Course Rating5/5

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.

Welcome to the official LLM in Python Applications Training certification program in San Francisco, USA. This comprehensive training is designed to elevate your professional skills and provide you with practical, industry-relevant knowledge locally. As a globally recognized corporate training provider operating in 55+ countries, ProgNXT ensures that our curriculum meets the highest standards of excellence.

Whether you are looking to upskill your team or advance your personal career, our expert-led sessions will guide you through the core concepts of this domain. Upon successful completion of the 21 Hrs program, participants will receive a globally accepted certification, demonstrating their proficiency and readiness to tackle complex challenges in the field.

Working knowledge of Python
Familiarity with APIs and JSON
Basic understanding of machine learning or AI concepts (recommended)
Experience with data handling or backend development is helpful

By the end of this training, participants will be able to:

Build Python applications using LLM APIs
Design effective prompts and workflows
Integrate LLMs with external data sources
Implement Retrieval-Augmented Generation (RAG)
Handle errors, costs, and latency in LLM apps
Deploy and monitor LLM-powered Python applications

LLM fundamentals and architectures
Prompt engineering in Python
API-based LLM integration
Orchestration with LangChain / LlamaIndex
RAG pipelines
Application deployment and monitoring
Responsible and secure AI usage

Prompt and workflow design
LLM API integration
RAG implementation
Error handling and optimization
End-to-end application functionality

Participants who attend all sessions and successfully complete the course assessments will receive a ProgNXT Certificate of Completion.

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

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Participants will be evaluated based on: Daily quizzes, mock questions and query resolution. Group Discussion and Case Study Discussion. Assessment will be through hands-on labs, quizzes, and hands-on exercises.

ProgNXT Certificate of Completion will be awarded to participants who complete the course and pass the evaluations.

LLM in Python Applications Training Events in san-francisco

Online Events

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More Upcoming Session Dates

October 2026

🌐 Online Instructor-Led

23rd Oct 2026→25th Oct 2026

📍 San Francisco, USA

24th Oct 2026→26th Oct 2026

November 2026

🌐 Online Instructor-Led

2nd Nov 2026→4th Nov 2026

📍 San Francisco, USA

11th Nov 2026→13th Nov 2026

📍 San Francisco, USA

18th Nov 2026→20th Nov 2026

🌐 Online Instructor-Led

24th Nov 2026→26th Nov 2026

December 2026

📍 San Francisco, USA

8th Dec 2026→10th Dec 2026

🌐 Online Instructor-Led

11th Dec 2026→13th Dec 2026

🌐 Online Instructor-Led

18th Dec 2026→20th Dec 2026

📍 San Francisco, USA

19th Dec 2026→21st Dec 2026

Events for other courses in San Francisco, USA

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LLM in Python Applications Training in San Francisco