Building Customized LLMs with OpenAI
Develop advanced skills customizing and deploying large language models via hands-on projects, covering RAG, fine-tuning, applications.
Modules/Weeks
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Course Description
Limited Time Offer: Use promo code SPRINGAI at checkout to enroll in this course for free!
As generative AI transforms industries, professionals must not only understand how large language models (LLMs) work but also learn to customize and deploy them effectively. Off-the-shelf solutions rarely meet nuanced, context-specific needs. This program equips technical practitioners to build tailored AI systems that align with organizational goals, leverage proprietary data, and address industry-specific requirements.
Learners will gain hands-on experience implementing LLMs, with a focus on advanced techniques such as retrieval-augmented generation (RAG) and fine-tuning. They will practice integrating AI APIs into real workflows, adapting models for domain-specific tasks, and building production-ready AI solutions through project-based learning and case studies.
Designed for intermediate to advanced learners with prior programming and AI fundamentals, this course is ideal for those who want to:
- Deepen practical skills in LLM implementation (software engineers and data scientists).
- Move beyond prompt engineering to develop customized, efficient models (AI/ML practitioners).
- Adapt AI for specialized use cases in healthcare, finance, law, or education (technical professionals).
- Integrate LLMs into scalable, user-facing applications (product developers and AI consultants).
Whether your goal is to build intelligent chatbots, personalized learning systems, or advanced document search tools, this program provides the applied knowledge and tools to bring cutting-edge AI into practice.
Use promotional discount code SPRINGAI to enroll in this course for free. Learners who successfully complete all course requirements will have the option to purchase a verified certificate of completion for $20.
Course Prerequisites
Basic Python programming knowledge
What You Will Learn
By the end of this course, learners will be able to:
- Create Effective Prompts for LLMs: Use zero-shot, one-shot, and few-shot prompts to guide generative models toward specific outputs.
- Implement LLM-Based Solutions: Connect to the OpenAI API and integrate generative AI features into notebooks or apps.
- Generate Text, Images, and More: Leverage Python libraries (e.g., LangChain, OpenAI) to build advanced workflows.
- Module 1: Building Custom Generative AI Retrieval-Augmented Generation (RAG)
By the end of this module, learners will be able to:
- Explain the concept of Retrieval-Augmented Generation (RAG) and its applications.
- Differentiate between human agents and LLM agents.
- Implement vector search for retrieving similar vectors.
- Build and test RAG models with synthetic data.
- Apply text analytics and knowledge graphs in generative AI.
- Module 2: Fine-Tuning
By the end of this module, learners will be able to:
- Understand the process of fine-tuning large language models.
- Differentiate various fine-tuning techniques, including adapter-based tuning
- How to fine-tune GPT models
- See how customized models can be embedded inside web applications
Instructors
Johar received an M.A. in Economics from the Birla Institute of Technology and Science and is a Fellow of the Indian Institute of Management Calcutta. He received a Ph.D. in Information Systems from the Stern School of Business, New York University in 1994. Prior to joining Columbia, Johar has worked as a quantitative trader at Morgan Stanley, Credit Suisse and Deutsche Bank, at a tech startup (MSpoke), and has taught at NYU Stern School of Business and the Gabelli School of Business Fordham University.