Prompt Engineering Automation with Python Course
Course Outline
Welcome to the 5-day artificial intelligence and prompt engineering course! This exciting and cutting-edge Prompt Engineering Automation with Python Course shows you how to automate prompting with Python to get the most value out of artificial intelligence.
You will explore the latest and most powerful Python and LangChain tools so you can automate agents. Emphasis is placed on balancing creative ambition with controlled responsibility to unlock sustainable business value.
Prompt Engineering Automation with Python Course Benefits
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Benefits
Understanding how best practice Python can help business: This course shows how to get the most out of combining Python tools with large language models, using the LangChain Python framework and techniques such as prompt templates, chains, vector embeddings and agents.
Leveraging the full range of automated AI tools available: Use large language models to automatically combine the value in your business files with freely available public data.
Applying autonomous and best practice prompting techniques: With practical business use cases, this course provides hands-on coding for real-time competitor analysis, dynamic marketing and insightful recruitment strategies relevant to all industries.
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Training Prerequisites
- Must be able to write and run basic Python scripts.
- Familiar with principles of libraries and API calls.
- Working knowledge of writing prompts for chat models.
- Comfortable with various AI outputs.
- Experience with AI-generated conversations and hallucinations.
Prompt Engineering with Python Training Outline
Day 1: Introduction to LangChain with Python
- Why Python is required for advanced prompt engineering
- LangChain Python framework overview
- LangChain tools - LLM, search, retrievers, chains, agents
- Prompt engineering with Python
- Querying LLMs with Python
Day 2: LangChain tools deep dive
- In-depth review of the five main LangChain tools: LLM, search, retrievers, chains, agents
- Hands-on exercises using Python on each tool
- Prompt injection and sequencing with chains
Day 3: LangChain Agents and Automation
- Configuring LangChain agents
- Agent automation with Python
- Monitoring and debugging agents
- Reviewing agents’ readiness for production
Day 4: Prompt theory and templates
- Creating effective, prompt templates
- Injecting dynamic data into prompts
- Storing prompts & outputs in vector databases
- Use cases for scaling prompts
Day 5: Commercial Use Cases and Assessment
- Real-world business applications of LangChain
- Hands-on workshop solving sample business challenges
- Developing an end-to-end LangChain solution
- Presentations and review
- Capstone assessment applying skills to a new commercial use case
- Future applications
- choosing a selection results in a full page refresh