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Unclaimed ProfileIn the rapidly evolving landscape of artificial intelligence, mastering the art of prompt engineering is no longer just a soft skill—it is a critical technical competency for AI engineers. This comprehensive course bridges the gap between basic prompt testing and the robust construction of large-scale AI automation systems. Students will dive deep into the architecture of modern Generative AI, moving beyond interface experimentation to understand how to programmatically influence LLM behavior using Python and the Transformers ecosystem. By exploring advanced topics such as Retrieval-Augmented Generation (RAG) and the integration of vector databases, you will learn how to ground AI outputs in proprietary, real-world data, drastically reducing hallucinations. This program is designed to move your skillset from passive interaction to active engineering, covering the full stack from model selection and fine-tuning strategies to the operational nuances of deploying production-ready AI agents. Whether you are aiming to build intelligent search systems, automated customer support bots, or custom enterprise LLM pipelines, this training provides the technical framework necessary to build scalable, reliable, and intelligent applications that solve genuine business problems in the modern digital age.
About the creator
Abeera sajid
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Abeera Sajid is a dedicated educator and technical specialist in the rapidly evolving field of Generative AI and prompt engineering. With a practical focus on bridging the gap between complex artificial intelligence algorithms and real-world application, Abeera helps AI engineers, developers, and professionals unlock the full potential of large language models. Her instruction centers on actionable skills, guiding students through the intricacies of crafting precise prompts, automating workflows, and leveraging modern AI tools like ChatGPT for maximum productivity. Abeera’s teaching philosophy is built on hands-on experimentation. She believes that mastering generative AI requires more than just understanding theoretical concepts; it…Show more
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Program Overview
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Learning format
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Price
Price may change · updated within 1–2 weeks
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What You'll Learn
- Design sophisticated, structured prompt pipelines including few-shot, chain-of-thought, and multi-step reasoning patterns for complex model execution.
- Architect end-to-end Retrieval-Augmented Generation (RAG) workflows that seamlessly integrate custom knowledge bases with foundation models.
- Implement and optimize vector databases like FAISS, ChromaDB, and Pinecone to achieve high-performance semantic search and information retrieval.
- Utilize Python-based frameworks like LangChain or LlamaIndex to orchestrate multi-agent workflows and modular AI application logic.
- Master the technical integration of HuggingFace Transformers for localized model inference and advanced fine-tuning strategies.
- Apply MLOps best practices for monitoring, evaluating, and iterating on LLM performance in production-grade environments.
- Develop autonomous AI agents capable of tool usage, such as API interactions and real-time data fetching, to expand system capabilities beyond text generation.
Best For
- Software engineers looking to transition into AI and machine learning engineering.
- Data scientists interested in mastering LLM orchestration and RAG system architecture.
- Technical product managers responsible for building generative AI features for enterprise apps.
Not For
- Beginners who have no prior experience with Python programming or machine learning fundamentals.
- Individuals looking for a high-level conceptual overview without wanting to write production-level code.
- Marketing professionals looking for simple tips on how to write better prompts for ChatGPT.
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