Maximizing LLM Performance - OpenAI Developer Conference 2023

Introduction

• Speakers: John Allard (OpenAI) and Colin
• Topic: Techniques to maximize LLM (Large Language Model) performance
• Key Announcements: Launch of 3.5 Turbo fine-tuning in August, including features like fine-tuning on function calling data, continuous fine-tuning, and UI for fine-tuning on the platform.

Understanding LLM Optimization Challenges

• Optimizing LLMs is challenging due to:
  • Separating signal from noise
  • Measuring abstract performance
  • Choosing the right approach to solve identified problems
• Goal: Provide a framework for optimization and tools to use

Approaches to Optimizing LLM Performance

Common Approaches

• Prompt Engineering: Always the best place to start
• Retrieval-Augmented Generation (RAG): Adding relevant context to the input
• Fine-Tuning: Customizing the LLM for specific tasks

Framework for Optimization

• Two optimization axes:
  1. Context Optimization: What does the model need to know?
  2. LLM Optimization: How does the model need to act?
• Optimization Journey: Start with prompt engineering, move to RAG if needed, and use fine-tuning as the final step if necessary

Prompt Engineering

Strategies

• Write clear instructions
• Split complex tasks into simpler subtasks
• Give GPTs time to think
• Test changes systematically

Intuition

• Good for: Early testing, providing a baseline, quick iteration
• Not good for: Introducing new information, replicating complex styles, minimizing token usage

Retrieval-Augmented Generation (RAG)

Overview

• Use external content to help the model answer questions
• Common process: Embed documents, create a knowledge base, retrieve relevant content, combine with a prompt, and get the answer

Intuition

• Good for: Introducing new information, reducing hallucinations
• Not good for: Broad domain understanding, teaching new styles, minimizing token usage

Example

• Success Story: Customer improved from 45% to 98% accuracy using various RAG techniques (e.g., re-ranking, classification)
• Cautionary Tale: Avoiding bad search outcomes (e.g., irrelevant content due to poor retrieval)
• Evaluation Metrics: Using frameworks like Ragas to measure LLM performance

Fine-Tuning

Definition

• Continue training an existing model on a smaller, domain-specific dataset.

Benefits

1. Achieving unreachable performance levels without fine-tuning
2. More efficient interaction due to less complex prompting

Use Cases

• Good for: Emphasizing specific knowledge, customizing output structure, teaching complex instructions
• Not good for: Adding entirely new knowledge, quick iteration on new use cases

Example

• Canva's Success Story: Improved structured design generation with fine-tuned models
• Cautionary Tale: Misalignment due to insufficient thought into training data (e.g., using Slack messages for blog post writing)

Process

1. Collect and prepare data
2. Start training (use good hyperparameter tuning)
3. Evaluate using different methods
4. Deploy and integrate a feedback loop

Combining Techniques: Fine-Tuning and RAG

• Fine-tuning for efficient instruction-following
• RAG for adding relevant context

Practical Application

Example: Spider 1.0 Benchmark

• Objective: Produce syntactically correct SQL queries from natural language
• Steps: Start with simple RAG and prompt engineering, then transition to fine-tuning

Results

• Initial: 69% accuracy with prompt engineering
• Hypothetical document embeddings: Improved to 78%
• Fine-tuning combined with RAG: Achieved 83.5% accuracy

Takeaways

1. Start simple with prompt engineering
2. Analyze error types to decide between RAG and fine-tuning
3. Iterate and revisit techniques as necessary

Conclusion

• Start with prompt engineering, then explore RAG and fine-tuning based on problem types
• It's a non-linear, iterative journey for maximizing LLM performance

Feel free to reach out to John and Colin for further discussions. [Applause]