Improving Accuracy of LLM Applications
by DeepLearning.AI × Lamini & Meta
A systematic workflow — evals, prompting, then fine-tuning — to push an LLM app from flaky to reliable.
Overview
Rather than treating fine-tuning as a first resort, this course teaches a systematic accuracy-improvement workflow. You build an evaluation set, apply prompt engineering and few-shot examples, and only then fine-tune (using memory-tuning techniques) to reduce hallucination on a text-to-SQL task. It shows how to combine techniques and measure each step's impact — exactly the discipline production teams need.
At a Glance
- Topic
- Fine-Tuning
- Level
- Intermediate
- Format
- Course
- Cost
- Free
- Duration
- ~1-2 hours
- Provider
- DeepLearning.AI × Lamini & Meta
- Hands-on
- Yes — code/exercises
- Certificate
- None
What You’ll Learn
- ✓Building evaluation sets for LLM apps
- ✓Prompt engineering before fine-tuning
- ✓Targeted fine-tuning to reduce hallucination
- ✓Measuring the impact of each change
Highlights
- •Evaluation-driven, systematic approach
- •Real text-to-SQL case study
Who It’s For
Best For
- ✓Teams pushing an LLM app to production reliability
Prerequisites
- •Python
- •Basic LLM app experience
FAQ
What is Improving Accuracy of LLM Applications?
A course on the disciplined process of improving LLM app accuracy: building evaluations, prompt engineering, and targeted fine-tuning.
Is Improving Accuracy of LLM Applications free?
Improving Accuracy of LLM Applications is free to access.
What level is Improving Accuracy of LLM Applications for?
Improving Accuracy of LLM Applications is aimed at a intermediate audience. Recommended background: Python, Basic LLM app experience.
How long does Improving Accuracy of LLM Applications take?
Expect roughly ~1-2 hours. Most learners work through it at their own pace.
What will I learn from Improving Accuracy of LLM Applications?
You'll learn: Building evaluation sets for LLM apps; Prompt engineering before fine-tuning; Targeted fine-tuning to reduce hallucination; Measuring the impact of each change.