Build intelligent apps with LLMs using Python, LangChain, and prompt engineeringβhands-on and practical.
What you will learn
Build intelligent applications using large language models (LLMs) like GPT and Mistral.
Design effective prompts to guide LLM behavior using advanced prompt engineering techniques.
Compare and evaluate popular LLMs for different application scenarios.
Implement retrieval-augmented generation (RAG) with embeddings and vector databases.
Use LangChain to create dynamic, modular AI-powered workflows.
Create conversational agents and assistants capable of natural, context-aware dialogue.
Embed custom data into LLM pipelines using semantic chunking and indexing.
Apply few-shot learning strategies to improve response quality in LLM outputs.
Integrate external tools and APIs with LLM agents for enhanced functionality.
Deploy Python-based AI applications with real-world usability and scalability.
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- Unlock the potential of cutting-edge AI by transforming raw language models into sophisticated, problem-solving applications.
- Move beyond theoretical concepts to practical implementation, gaining the confidence to architect and deploy intelligent systems.
- Master the art of communicating with AI, learning to craft precise instructions that elicit desired and creative outputs.
- Develop a strategic understanding of how to select the right AI brain for your specific application, optimizing for performance and cost.
- Construct robust data pipelines that empower LLMs with your proprietary information, making them uniquely informed and relevant.
- Engineer interactive experiences that feel natural and intelligent, akin to interacting with a human expert.
- Gain the ability to build AI systems that can reason, plan, and execute tasks by connecting them to the wider digital ecosystem.
- Learn techniques to refine and elevate AI responses, ensuring accuracy, relevance, and a higher degree of sophistication.
- Forge intelligent agents that can leverage external services and data sources, extending their capabilities far beyond their core language processing.
- Construct and launch your AI creations into the real world, ensuring they are not just functional but also user-friendly and scalable.
- Explore the iterative development process of AI applications, from initial concept to polished, production-ready solutions.
- Develop a foundational skill set that is highly in-demand in the rapidly evolving field of artificial intelligence.
- Gain insights into the ethical considerations and best practices when building and deploying AI-powered applications.
- Pros:
- Highly practical and project-oriented approach to learning LLM development.
- Covers a comprehensive suite of modern AI development tools and frameworks.
- Empowers learners to build immediately deployable AI solutions.
- Cons:
- Requires a solid foundation in Python programming to fully leverage the course material.
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