Key Takeaways
Course Name | Improving Accuracy of LLM Applications |
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Platform | DeepLearning.AI |
Price | Free |
Duration | 1 Hour |
Level | Intermediate |
Prerequisites | Intermediate Python, Familiarity with LLMs |
Skills | LLM Evaluation, Prompt Engineering, Fine-Tuning, Memory Tuning, Text-to-SQL Conversion |
About
This course, offered by DeepLearning.AI, guides you through improving the accuracy and reliability of Large Language Model (LLM) applications. If you’ve struggled with inconsistent model outputs, this course provides a structured approach to enhance your application’s performance, using practical techniques like evaluation metrics, prompt engineering, and fine-tuning.
Who is teaching?
The course is taught by Sharon Zhou, Co-founder & CEO of Lamini, and Amit Sangani, Senior Director of Partner Engineering at Meta.
What is covered?
- Development steps to improve LLM reliability, including evaluation, prompting, and self-reflection.
- Memory tuning to enhance model performance and reduce hallucinations.
- Building an LLM application that converts text to SQL using Llama 3-8b model.
- Instruction and memory fine-tuning techniques like LoRA and MoME.
Skills you will develop
- LLM Evaluation Metrics
- Prompt Engineering
- Fine-Tuning Techniques
- Memory Tuning for LLMs
- Building Text-to-SQL Agents
Level
This course is suitable for intermediate learners with a background in Python and LLMs. Check out AI courses for options of different levels.
Ready to enhance the accuracy of your LLM applications? Enroll in this free course today and start building more reliable and factual AI models!