View source for A Survey of Techniques for Maximizing LLM Performance (OpenAI Dev Day 2023)

{{Presentation infobox
|Image = {{#ev:youtube|ahnGLM-RC1Y|350}}
|Name = A Survey of Techniques for Maximizing LLM Performance
|Type = Technical
|Event = OpenAI Dev Day 2023
|Organization = OpenAI
|Channel = OpenAI
|Presenter = John Allard, Colin Jarvis
|Description = Join us for a comprehensive survey of techniques designed to unlock the full potential of Language Model Models (LLMs). Explore strategies such as fine-tuning, RAG (Retrieval-Augmented Generation), and prompt engineering to maximize LLM performance.
|Date = Nov 14, 2023
|Website = https://www.youtube.com/watch?v=ahnGLM-RC1Y
}}

==TLDR==
[[Optimizing LLM performance]] is a complex, iterative process that involves a combination of [[prompt engineering]], [[RAG]], and [[fine-tuning]]. Each technique addresses specific optimization needs and challenges, and their effective combination can significantly enhance LLM capabilities. The journey from initial prompt engineering to fine-tuning represents a comprehensive approach to LLM optimization, underscored by practical insights and real-world applications.

==Introduction==
This article explores the techniques for maximizing the performance of [[Large Language Models]] (LLMs) like those developed by [[OpenAI]]. The insights are drawn from the experiences of [[John Allard]], an engineering lead at OpenAI, and [[Colin]], head of solutions practice in Europe, shared during OpenAI's first developer conference.

==Background==
LLMs have revolutionized the field of [[natural language processing]], offering unprecedented capabilities in understanding and generating [[human-like text]]. However, optimizing these models for specific tasks remains a challenge. The focus is on understanding and applying various techniques to enhance LLM performance.

==Prompt Engineering==
[[Prompt engineering]] involves crafting inputs to guide the LLM's response in a desired direction. It is an effective starting point for LLM optimization, allowing for rapid testing and learning. Key strategies include:
#Writing clear instructions
#Breaking complex tasks into simpler subtasks
#Giving LLMs time to think
#Testing changes systematically

Despite its usefulness, prompt engineering has limitations, especially in introducing new information and reliably replicating complex styles or methods.

==Retrieval-Augmented Generation (RAG)==
[[RAG]] extends the capabilities of LLMs by combining their predictive power with external knowledge sources. It involves retrieving relevant information from a [[database]] or [[knowledge base]] and presenting it to the LLM along with the query. This approach helps in:
#Introducing new information
#Reducing hallucinations by controlling content

However, RAG is not suited for embedding understanding of broad domains or teaching models new language formats.

==Fine-Tuning==
[[Fine-tuning]] is a transformative process where an existing LLM is further trained on a specific, often smaller and more domain-specific [[dataset]]. It offers two primary benefits:
#Achieving higher performance levels
#Enhancing efficiency during model interaction

Fine-tuning is particularly effective for emphasizing existing knowledge, modifying output structure or tone, and teaching complex instructions. It is less effective for adding new knowledge and quick iterations on new use cases.

==Practical Applications and Case Studies==
The techniques were applied to the [[Spider 1.0]] benchmark, which involves generating [[SQL queries]] from [[natural language descriptions]]. The journey involved starting with prompt engineering, moving to RAG, and eventually fine-tuning with the help of partners at [[Scale AI]]. The process exemplified the non-linear nature of LLM optimization and the need for multiple iterations to achieve the desired performance.